So, what is the difference between linear regression and decision trees?

**Linear Regression is used to predict continuous outputs where there is a linear relationship between the features of the dataset and the output variable. It is used for regression problems where you are trying to predict something with infinite possible answers such as the price of a house. **

**Decision trees can be used for either classification or regression problems and are useful for complex datasets. They work by splitting the dataset, in a tree-like structure, into smaller and smaller subsets and then make predictions based on what subset a new example would fall into. **

There are many nuances to consider with both linear regression and decision trees and there are a number of things you can do to get them to perform better.

Linear regression gives a continuous output and is used for regression tasks. It can be used when the independent variables (the factors that you want to use to predict with) have a linear relationship with the output variable (what you want to predict) ie it is of the form Y= C+aX1+bX2 (linear) and it is not of the form Y = C+aX1X2 (non-linear).

Examples could be predicting the price of a house, the number of expected sales on a particular day based on the temperature or the number of tickets that will be sold based on the price.

Using linear regression for classification is problematic since it doesn’t give you an output in the range of 0 to 1 and it is also susceptible to outliers. Instead, logistic regression is used for classification.

Also, if there is more than one feature vector then multiple linear regression can be used and if there is not a linear relationship between the features and the output then polynomial regression can be used.

Linear regression is appropriate for datasets where there is a linear relationship between the features and the output variable. Polynomial regression can also be used when there is a non-linear relationship between the features and the output.

You can read more about when linear regression is appropriate in this post.

Linear regression can be prone to underfitting the data. If you build a model using linear regression and you find that both the test accuracy and the training accuracy are low then this would likely be due to underfitting. In this case, it would likely help to switch to polynomial regression which involves multiplying feature vectors to an nth degree polynomial. To do this, you can use the PolynomialFeatures class from sklearn.

If you find that the test accuracy is much lower than the test accuracy then you can use regularization to reduce the variance (overfitting) of the model. To do this you can use Ridge Regression, Lasso Regression, Elastic-net or early stopping.

Feature scaling is not generally required in linear, multiple or polynomial regression. However, there are some reasons why you might want to scale and normalize the data which get explained in this StackExchange question.

- Polynomial regression
- Decision tree regression
- Random forest regression
- Support vector regression

Decision trees are a powerful machine learning algorithm that can be used for classification and regression tasks. They work by splitting the data up multiple times based on the category that they fall into or their continuous output in the case of regression.

In the case of regression, decision trees learn by splitting the training examples in a way such that the sum of squared residuals is minimized. It then predicts the output value by taking the average of all of the examples that fall into a certain leaf on the decision tree and using that as the output prediction.

You can watch the video below to see how decision trees work for regression.

For classification, decision trees learn by splitting the training examples so that it can divide the data into subsets that separate the categories the data points fall into as much as possible. They then make output predictions based on the most common category in the subset that the new example would fall into. You can watch the two videos below to see more intuition about how decision trees work.

This is also a good video about decision trees for classification:

Decision trees are useful when there are complex relationships between the features and the output variables. They also work well compared to other algorithms when there are missing features, when there is a mix of categorical and numerical features and when there is a big difference in the scale of features.

Decision trees can be prone to overfitting. To prevent overfitting, it would help to restrict the maximum depth of the tree, to set a minimum number of samples a node must have before splitting it, to set a minimum number of samples that a leaf must contain and to set a maximum number of leaves that the tree can contain. All of these things can be done in sklearn as shown here and here.

It would also likely help to use a random forest instead which reduces overfitting by aggregating the results of multiple decision trees on the same dataset. The video below shows how random forests work.

Decision trees do not require feature scaling.

In the case of classification:

- Random forests
- Support vector machines
- K-nearest neighbors
- Logistic regression
- Naive bayes
- Neural networks

For regression:

- Linear regression
- Polynomial regression
- Support vector regression
- Random forest regression

So, what is the difference between classification and clustering? **Classification is a supervised learning approach that learns to figure out what class a new example should fit in by learning from training data that contains the class labels for the data points. Clustering is an unsupervised learning approach which tries to cluster similar examples together without knowing what their labels are. **

For example, classification might be used to determine if an email is spam or not. The algorithm would learn by detecting patterns in training examples that contain spam or not spam labels.

Although, clustering can also be used for this as well by clustering emails together based on the heading, body and who the sender is but without the spam or not spam labels.

Clustering could also be used to segment a company’s customers based on their purchasing history.

Classification algorithms are used to assign a class to a new example. They work by learning from training data that contains the labels to go along with the features and then use the patterns they find in the data to predict what class a new example should fall into.

An example would be predicting if a house will sell for **more than** a certain price (note that predicting the exact price the house will sell for would be a regression problem and grouping similar houses together would be classification).

Binary classification is where there are only two output classes (ie spam or not spam). Whereas, multiclass classification is where there are more than two output classes (ie predicting what dog breed a picture of a dog is).

Below are some algorithms that are commonly used for classification.

Logistic Regression outputs the probability that an example falls into a certain class. It is often a good algorithm for binary classification but it can also be used for multiclass classification.

Pros: Outputs the probabilities of a classification, easy to interpret and works well for binary classification.

Cons: Cannot handle non-linear data.

A decision tree can be used for either regression or classification. It works by splitting the data up in a tree-like pattern into smaller and smaller subsets. Then, when predicting the output value of a set of features, it will predict the output based on the subset that the set of features falls into. You can look here for a more detailed explanation.

You can watch the video below to see how decision trees work.

Pros: It can find complex patterns in the data, doesn’t require a lot of preprocessing, easy to understand.

Cons: It can be prone to overfitting.

Random forests are very similar to decision trees and can be used for classification or regression. The difference is that random forests build multiple decision trees on random subsets of the data and then average the results. This often allows for much more accurate predictions on unseen data than with decision trees. You can read more about how random forests are used for classification here.

You can watch the video below to see an explanation of how random forests work.

Pros: More accurate on unseen data than decision trees, outputs the importance of variables and can find complex patterns in the data.

Cons: It does not give a precise prediction for regression and it does not extend beyond the range seen in training data.

Support vector machines learn what class examples belong to by fitting a line between the data points and maximizing the margin on either side of that line based on their y-labels. SVMs can either use a “hard margin” or a “soft margin”. Hard margin SVMs do not allow any data points to fall within the margin but soft margin SVMs do. Allowing for some data points to fit within the margin helps to avoid overfitting.

You can watch the video below to see how SVMs work.

Pros: They work well for datasets with lots of features, they work well when the classes are separable,

Cons: They do not provide probability estimates, they don’t work well with large datasets.

The Naive Bayes classifier uses Bayes theorem to classify data. You can look here for more info about how it works.

Pros: Makes predictions quickly, works well on categorical data, fast training time and easy to interpret.

Cons: It considers each feature as being independent which is not always true.

K-nearest Neighbours classifies examples by looking at the classes of the k-nearest data points to that example.

You can watch the video below to see how it works:

Pros: Easy to understand, quickly adapts to new training data and useful for multiclass classification.

Cons: Becomes less effective as the number of features increases, requires feature scaling, sensitive to outliers.

Neural networks are loosely based on the neurons in the brain. They can be used for many different tasks including regression and classification. They tend to be the best algorithms for very large datasets. You can read more about neural networks here and you can read more about using Neural Networks to classify images of numbers here.

Pros: Can be used for classification or regression, work well with large amounts of data, work well with non-linear data and can make predictions quickly after being trained.

Cons: they are described as being “black boxes” meaning that it is difficult to understand why they are weighted the way they are and they require a lot of data to train on.

The goal of clustering algorithms is to group similar data points together. They are unsupervised algorithms, meaning that the data they are used with does not contain output labels.

So, if the goal is to create a spam classifier, the dataset wouldn’t say if the email is spam or not, it would just contain data about the emails such as the body text and the subject line.

Below are the clustering algorithms that you can currently implement using Scikit-Learn, their advantages and their disadvantages.

K-means clustering is used to segment data into k clusters. It works by initially placing k centroids to the data, the data points are then linked to the closest centroid, then the best clusters are found by iteratively minimizing the sum of the squared distances between the data points and the nearest centroid. You can watch the video below to see how it works. You can also read more about it here.

Pros: Scales well to large datasets, easily adapts to new examples, generalizes well to different cluster shapes.

Cons: You must choose k manually, heavily impacted by outliers, doesn’t do well when the clusters have different sizes and densities.

Source: https://developers.google.com/machine-learning/clustering/algorithm/advantages-disadvantages

Affinity propagation is a centroid based algorithm similar to k-means clustering. However, it does not require you to set the number of clusters beforehand. The problem with this is that it results in a higher time complexity. You can read more about the algorithm here and you can see how it works in the video below.

Pros: Determines the number of clusters for you, works well when there are lots of clusters, works if there are uneven cluster sizes or non-flat geometry.

Cons: It has a worse time complexity than k-means clustering.

Mean-shift is a hierarchical clustering algorithm. It works by creating circles around each of the examples and then combining examples into the same cluster if they all fall into a circle together. You can watch the video below for a very good demonstration of how it works.

Pros: It figures out how many clusters there should be for you, works well when there are lots of clusters, works if there are uneven cluster sizes or non-flat geometry.

Cons: Does not scale well

Spectral clustering is a very useful algorithm for situations where the desired clusters have very abnormal shapes. This Quora post explains very well why you would want to use the algorithm and this post explains how it works.

Pros: Good for data with dense data points and abnormal shapes

Cons: Doesn’t work well with large datasets or datasets with lots of clusters.

Hierarchical clustering is useful for grouping correlated features on heatmaps. You can read more about how it works and its applications here.

Pros: Hierarchical clustering gives you a dendrogram which is useful for visualizing the relationships of in the data.

Cons: Doesn’t scale well and it is not as good as other clustering algorithms at accurately clustering the data (source).

DBSCAN is useful when there are dense clusters that are separated by less dense regions. It also scales well with the amount of data there is so it is preferred over many of the other algorithms that try to separate dense regions and non-dense regions.

Pros: Ignores outliers, scales well.

Cons: You need to set the neighborhood parameter

OPTICS is similar to DBSCAN in that it is useful for separating dense clusters from less dense regions, it also works in a similar way to DBSCAN. However, it also adds a minimum radius term for core points and it calculates reachability distances. You can look here and here for more details.

Pros: Scales well, good for detecting clusters of differing densities.

Cons: Slower than DBSCAN.

Gaussian mixture models assume that the data points have been accumulated with a finite number of Gaussian distributions. They are useful when you want to estimate densities or if you know that the data has come from Gaussian distributions. You can look here for more information.

Pros: More flexible than K-Means, good for estimating densities.

Cons: Doesn’t scale well

Birch is a hierarchical algorithm that is useful when the dataset is large and there are a lot of clusters within the dataset. It can be used as an alternative to mini-batch k-means. You can read more about it here and here.

Pros: Works well on large datasets with lots of clusters and it is useful for data reduction.

Cons: Not as accurate as alternative models.

]]>So, what is the difference between regression and classification? **Regression is used when you are trying to predict an output variable that is continuous. Whereas, classification is used when you are trying to predict the class that a set of features should fall into.**

For example, regression might be used to predict the price of a house. Whereas, classification would be used to predict whether the price of the house is above or below a certain price or if it will rise or fall in price.

Both of them fall under the umbrella of supervised learning algorithms. This means that the training data that they learn from includes the output values they are trying to learn to predict. If the data is not labeled then it is called an unsupervised machine learning problem and a clustering algorithm will be used instead.

Regression, in machine learning, is where you train an algorithm to predict a continuous (y) output based on a set of features (X).

Examples of regression problems could include:

- Predicting the price of houses based on data such as the quality of schools in the area, the number of bedrooms in the house and the location of the house.
- Predicting the sales revenue of a company based on data such as the previous sales of the company.
- How much a customer will cost a company based on data from previous customers.

Below are some popular machine learning algorithms that are used for regression problems.

Linear regression is used to find a linear relationship between the target and one or more predictor variables. An example of when linear regression would be used could be to predict someone’s height based on their age, gender and weight.

You can look here for a more detailed explanation of how linear regression works in machine learning.

Polynomial Regression allows for a non-linear relationship to be found. It is used when the relationship between the y values and the X values is not linear. You can look here for a more detailed explanation of how it works and how to use it in machine learning.

**Pros**: Simple to implement, works well without a lot of data and easy to interpret.

**Cons**: It is not complex enough to find more complex relationships and it is susceptible to outliers.

A decision tree can be used for either regression or classification. It works by splitting the data up in a tree-like pattern into smaller and smaller subsets. Then, when predicting the output value of a set of features, it will predict the output based on the subset that the set of features falls into. You can look here for a more detailed explanation.

You can watch the video below to see how decision trees work.

You can watch the video below for an explanation of how decision trees are used for regression.

Pros: It can find complex patterns in the data, doesn’t require a lot of preprocessing, easy to understand.

Cons: It can be prone to overfitting.

Random forests are very similar to decision trees and can be used for classification or regression. The difference is that random forests build multiple decision trees on random subsets of the data and then average the results. This often allows for much more accurate predictions on unseen data than with decision trees.

You can watch the video below to see an explanation of how random forests work.

Pros: More accurate on unseen data than decision trees, outputs the importance of variables and can find complex patterns in the data.

Cons: It does not give a precise prediction for regression and it does not extend beyond the range seen in training data.

Neural networks are loosely based on the neurons in the brain. They can be used for many different tasks including regression and classification. They tend to be the best algorithms for very large datasets. You can read more about neural networks here and you can read about how to use them for regression here.

Pros: Can be used for classification or regression, work well with large amounts of data, work well with non-linear data and can make predictions quickly after being trained.

Cons: they are described as being “black boxes” meaning that it is difficult to understand why they are weighted the way they are and they require a lot of data to train on.

Classification algorithms are used to assign labels to unlabeled examples. They work by learning from training data that contains the labels to go along with the features and then use the patterns they find in the data to predict what class a new example should fall into. An example would be predicting if a house will sell for more than a certain price or if an email is spam or not.

Binary classification is where there are only two output classes (ie spam or not spam). Whereas, multiclass classification is where there are more than two output classes (ie predicting what dog breed a picture of a dog is).

Below are some algorithms that are commonly used for classification.

Logistic Regression outputs the probability that an example falls into a certain class. It is often a good algorithm for binary classification but it can also be used for multiclass classification.

Pros: Outputs the probabilities of a classification, easy to interpret and works well for binary classification.

Cons: Cannot handle non-linear data.

As mentioned above, Random Forests can be used for regression and classification. They are often one of the best algorithms for classification problems. You can read more about how Random Forests are used for classification here.

Support Vector Machines learn what class examples belong to by fitting a line between the data points and maximizing the margin on either side of that line based on their y-labels. SVMs can either use a “hard margin” or a “soft margin”. Hard margin SVMs do not allow any data points to fall within the margin but soft margin SVMs do. Allowing for some data points to fit within the margin helps to avoid overfitting.

You can watch the video below to see how SVMs work.

Pros: They work well for datasets with lots of features, they work well when the classes are separable,

Cons: They do not provide probability estimates, they don’t work well with large datasets.

The Naive Bayes classifier uses Bayes theorem to classify data. You can look here for more info about how it works.

Pros: Makes predictions quickly, works well on categorical data, fast training time and easy to interpret.

Cons: It considers each feature as being independent which is not always true.

K-nearest Neighbours classifies examples by looking at the classes of the k-nearest data points to that example.

You can watch the video below to see how it works:

Pros: Easy to understand, quickly adapts to new training data and useful for multiclass classification.

Cons: Becomes less effective as the number of features increases, requires feature scaling, sensitive to outliers.

Neural networks can also be used for classification problems. You can read more about using Neural Networks to classify images of numbers here.

]]>This post will show you how linear and logistic regression are different and when to use each of them.

So, what is the difference between linear regression and logistic regression? **Linear regression gives you a continuous output that is used to predict something with infinite possible answers such as the price of a house. Logistic regression gives you an output between 0 and 1 and is used to classify things such as whether or not a tumor is malignant.**

There are actually a number of similarities between linear regression and logistic regression and they can both be used to predict similar things with some nuances between them.

Here are the key differences between the two:

Linear regression gives a continuous output that can be greater than 1 or less than 0. It is used to predict things that have infinite possible answers. It does not tell you the probability of a particular outcome.

It can be used when the independent variables (the factors that you want to use to predict with) have a linear relationship with the output variable (what you want to predict) ie it is of the form Y= C+aX1+bX2 (linear) and it is not of the form Y = C+aX1X2 (non-linear).

Examples could be predicting the price of a house, the number of expected sales on a particular day based on the temperature or the number of tickets that will be sold based on the price.

Using linear regression for classification is problematic since it doesn’t give you an output in the range of 0 to 1 and it is also susceptible to outliers.

Logistic regression gives an output between 0 and 1 which tells you the probability of something happening. If the output is below 0.5 it means that it is not likely to occur, if it is above 0.5 then it is likely to occur.

Logistic regression is also to be used when there is a linear relationship between the output and the factors. The difference is that logistic regression will give you a yes or no type of answer.

Examples could be predicting if a tumor is malignant, if the price of a house will be greater or less than $300,000 or whether or not someone will win an election.

The equations used for linear regression are:

Theta represents the optimal gradients that you are trying to find that will allow you to predict the output variable.

By doing stochastic gradient descent where you select only one training example for each iteration instead of doing it over the whole training set you are able to get to a solution much more quickly.

By setting alpha = 1/(1+k), where k is the number of iterations that you have done, you can get to a solution more quickly since you’ll take larger steps at first and then take smaller steps when theta gets close to the optimal values.

Closed-form exists because the cost function is convex.

It is possible to overfit when using linear regression which is why you might want to do regularization. You can use ridge regression to avoid overfitting which gets explained in this video https://www.youtube.com/watch?v=qbvRdrd0yJ8.

The formulas used for logistic regression are:

This looks very similar to linear regression and the only difference is that with logistic regression you are using a function to make the predicted value of y be between 0 and 1. There are many different functions that are used to do this. A common function that gets used is the sigmoid function.

]]>If you are interested in machine learning then you might be wondering whether or not it would be possible for you to self-teach yourself machine learning.

In this post, I will show you that it is possible for you to do it and how I would recommend that you go about self-teaching yourself machine learning.

So, can machine learning be self-taught? **Even though there are many different skills to learn in machine learning it is possible for you to self-teach yourself machine learning. There are many courses available now that will take you from having no knowledge of machine learning to being able to understand and implement the ml algorithms yourself.**

There are actually so many machine learning courses available now that choosing the right path for you can be quite daunting. The rest of this post will lay out a roadmap for you to take if you want to self-tech yourself machine learning.

Here is the path that I would recommend:

- Take the machine learning course taught by Andrew Ng that has no prerequisites
- Learn Python programming with this course on edX
- Learn Data analysis in Python with this Youtube series and these lecture notes
- Take the deep learning course taught by Andrew Ng
- Read this book or watch this YouTube series on how to implement machine learning in Python
- Start applying your knowledge of machine learning on Kaggle competitions
- Learn calculus, linear algebra and probability on edX
- Take this more mathematical course on machine learning offered by MIT

The reason that I recommend that you start out with Andrew Ng’s course is that it will give you the chance to see if machine learning is something that you are actually interested in. This way you won’t have to invest a lot of time in learning other skills that machine learning is based on.

**If you already know calculus, linear algebra, probability and how to program in Python then I would recommend that you skip to step 8 and take the course offered by MIT.** It will go over all that you need to know in the field right now and you will be able to do machine learning in Python after only having taken that course. With that being said, I am a big fan of how Andrew Ng teaches so you could supplement the MIT course with Andrew Ng’s videos as you go.

Machine learning is based on getting statistical algorithms to learn from data and to make predictions with the data or to cluster the data in ways that a human wouldn’t be able to.

The machine learning algorithms make use of calculus, probability, linear algebra and it is necessary for you to know how to program in order to be able to implement the machine learning algorithms.

With that being said, there are some courses available that will teach you machine learning that do not assume that you have any prior knowledge. These courses will often be a good place to start since they will get you up to speed quickly and will give you a chance to see if machine learning is something that you are actually interested in. Many of them are paid but there are some that are free such as the course taught by Andrew Ng.

If you are looking to self-teach yourself machine learning then my advice would be to start out with an introductory machine learning course.

It is true that it is necessary to know calculus, linear algebra and probability to be able to fully understand the mathematics of the machine learning algorithms. However, it is only really linear algebra that you need to have familiarity with in order to understand how the algorithms work.

In the course taught by Andrew Ng, he teaches you the linear algebra that you need to know and he explains the algorithms in a way such that you do not need to know calculus to understand how they work.

If you don’t already know how to program then it will be necessary for you to self-teach yourself how.

This is because it is necessary to use programming to apply the machine learning algorithms to data. It is also necessary to use programming to modify the datasets so that they work well with the machine learning algorithms.

The programming language that I would recommend that you teach yourself would be Python. This is because it is the most popular machine learning language, it is one of the easier languages to learn and it has many built-in libraries that allow you to apply the ml algorithms more easily.

I would recommend that you use this edX course to learn Python that is taught by MIT.

Once you have learned how to program in Python I would recommend that you learn how to do data analysis in Python since you will make use of it a lot in machine learning. I would recommend that you learn data analysis with this YouTube series and this pdf.

After having learned how to code in Python it would be worthwhile for you to learn deep learning specifically.

Deep learning is a subset of machine learning where the algorithms work well when they are given large amounts of data and computational power. They have become very popular in recent years due to the sharp increase in data and computational power that has become available to us.

The course that I would recommend that you take to learn deep learning would be this one taught by Andrew Ng from Stanford. It does not have any prerequisite knowledge except for Python and taking his machine learning course beforehand would be recommended.

Once you know how the machine learning algorithms work it would be worthwhile for you to take the time to learn how to implement them in Python.

I would recommend that you read this book to do that. It is the second edition of a practical machine learning book that was extremely popular and is one that I have personally made use of many times. If you do not want to pay for a book then this YouTube series is also good.

Once you know how to implement the machine learning algorithms you’ll be ready to apply machine learning on your own datasets.

You can find datasets that you can use on the website Kaggle and you can compete in machine learning competitions on there as well.

You’ll likely find that you will identify a number of gaps in your knowledge when you start implementing the algorithms yourself but this is ok since it will help you to learn much more effectively.

If you are looking to get a job in machine learning then creating your own machine learning projects can be very beneficial since it shows to employers that you know how to implement machine learning. I have written more about how you can create your own machine learning projects and get a machine learning job here.

To learn how the machine learning algorithms work properly and how to implement them in a more statistically sound way it would be helpful to take the time to learn the necessary mathematics for machine learning.

The courses that I would recommend would be:

Calculus (MIT)

Linear algebra (The University of Texas at Austin) I would also recommend this Youtube series

Probability (MIT)

After having learned the required mathematics you could either read a more mathematical machine learning book or you could take a more mathematical machine learning course. The book that I would recommend would be this one and the course that I would recommend would be this one that is taught by MIT on edX.

There are paid machine learning courses that you can take that will teach you everything that you need to know in order to learn machine learning in one place.

The advantage of taking a course like this is that you will be able to learn machine learning in a more structured way which should help to make it easier and faster. However, these courses can be expensive, they are usually membership based and the free material available is often taught by some of the worlds top universities.

The paid course that I would recommend the most would be this one on Dataquest. You can watch a review of Dataquest in the video below.

Before you set out learning machine learning it would help to consider what you intend to do in machine learning.

If you want to be a machine learning researcher then you’ll need an undergraduate degree in a field such as stats, math or computer science. It would also be better for you to learn the programming and mathematics first then to take the course offered by MIT since it will be necessary for you to understand the mathematics that goes into the algorithms. You can supplement your studies by watching Andrew Ng’s videos as well.

If you intend to build ai-based applications then it would be worthwhile for you to focus on getting to a point where you are able to build machine learning programs as quickly as possible. To do that I would recommend that you follow steps one through six as shown above.

If you intend to get a job in machine learning then I would recommend that you do all of the steps, 1-8, since you will need to be able to explain the algorithms mathematically and you will need to be proficient in implementing them. It will also be necessary for you to learn SQL, data structures and computational complexity. I have talked about getting a job in machine learning in the past here.

]]>If you’re not a technical person and you are interested in machine learning then you might be wondering whether or not it will be possible for you to do it.

This post will show you whether or not a non-technical person can learn machine learning and how I would recommend that you go about doing it.

So, can a non-technical person learn machine learning?** Machine learning makes use of a lot of programming and mathematics. However, even if you are not a technical person, it will be possible for you to learn machine learning if you follow the right path.**

Machine learning has become very popular in recent years and this has resulted in many educational resources on machine learning being made. There are even courses available which will teach you how machine learning works even if you have no prior knowledge of programming and college level mathematics. This means that, even as a non-technical person, it will be possible for you to learn machine learning.

Below, I will show you the path that I would recommend that you take to learn machine learning:

- Take the machine learning course taught by Andrew Ng that has no prerequisites
- Learn Python programming with this course
- Learn Data analysis in Python with this Youtube series and these lecture notes
- Take the deep learning course taught by Andrew Ng
- Read this book or watch this YouTube series on how to implement machine learning in Python
- Start applying your knowledge of machine learning on Kaggle competitions
- Learn calculus, linear algebra and probability on edX
- Take this more mathematical course on machine learning offered by MIT

The reason that I recommend that you start with Andrew Ng’s course before you even start learning to program is that it will give you the chance to see if machine learning is something that you would actually be interested in before investing a lot of time into it.

Once you have completed step 6 then you will be able to implement the machine learning models yourself just fine.

However, you will not have a good understanding of the mathematics that goes into the machine learning models to do that it will be necessary for you to learn the mathematics that goes into them. This is why I recommend that you do steps 7 and 8 to get a better understanding of how the machine learning models work mathematically.

Overall, machine learning is quite a technical field since it makes use of programming, statistics and mathematics to apply statistical algorithms to data.

In order to be able to apply the machine learning models, it will be necessary for you to learn how to program in a programming language such as Python.

The machine learning models make use of a lot of mathematics to work. The mathematics that the machine learning models use include linear algebra, calculus and probability theory. However, there are courses that will teach you how the machine learning models work assuming that you do not already know any of the mathematics. I would recommend that you start there and then learn the mathematics of the machine learning more thoroughly after.

If you intend to work with very large datasets or you intend to get a job in machine learning then it will also be necessary for you to learn SQL.

If you intend to get a job in machine learning it would also be necessary for you to learn algorithmic complexity which is focused on analyzing the time that computer programs take to run.

If you are interested in learning machine learning then I would recommend that you start by taking this course taught by Andrew Ng from Stanford University. The course does not assume that you have any prior knowledge but it does teach you the linear algebra that is necessary to understand the machine learning algorithms. This is likely the most popular machine learning course since Andrew Ng does a very good job of explaining machine learning and the models in a very intuitive way.

I would recommend that you start there since you will be able to see whether or not machine learning is something that you are actually interested in before spending a lot of time learning the programming and mathematics.

In order to be able to apply the machine learning models, it will be necessary for you to learn how to program.

The programming language that I would recommend the most would be Python. This is because it is the most popular machine learning language, it is relatively easy to learn and it has a number of built-in libraries that allow you to implement the machine learning algorithms easily. To learn Python I would recommend that you take this course taught by MIT on edX.

Once you have learned how to program in Python I would recommend that you learn how to do some basic data analysis in Python. This is because data analysis gets used a lot in machine learning especially when you are preparing the data so that it works well with the machine learning models. To learn data analysis in Python I would recommend that you learn Numpy with these lecture notes and Pandas using this YouTube playlist.

There are many more programming languages that get used in machine learning and one of them is R. This is a programming language that is specifically designed to be used in statistics which is why it tends to be very popular in statistical fields. It has a number of built-in libraries and a reasonably large support community. However, I would recommend that you learn Python over R since most machine learning tutorials are in Python.

Javascript is another programming language that you might want to consider. It is designed for the web so, if you intend to make a web-based AI solution then Javascript is something that you might want to consider.

You can watch the video below to see more about programming languages in machine learning.

After you have learned to program in Python I would recommend that you take this course on deep learning taught by Andrew Ng.

Deep learning is a subset of machine learning which features the algorithms that do very well when they are given a lot of data. They have become very popular in recent years due to the sharp increase in data that businesses have had become available to them.

Next, it would be worthwhile to take a more practical machine learning course that will show you how to implement the different machine learning algorithms in Python. I would recommend this book. However, a free alternative would be to watch this YouTube series.

Now, you’ll be ready to implement the machine learning algorithms yourself on your own datasets. You can do this by coming up with your own datasets to use machine learning on or you can compete in machine learning competitions on Kaggle.

If you want to learn how the machine learning models work mathematically, how to fully optimize the machine learning models or you want to get a job or research position in machine learning then it will be necessary for you to learn the mathematics.

To learn the mathematics subjects I would recommend the following:

Calculus (MIT)

Linear algebra (The University of Texas at Austin) I would also recommend this Youtube series

Probability (MIT)

After learning the mathematics for machine learning you can either take a more mathematical machine learning course or you could read a machine learning book. There are many different books that you can get but a popular one is this one which also has a slightly less mathematical version which is this one. The course that I would recommend would be this one which is taught by MIT.

There are also paid machine learning courses, that you can take, that will take you from having no knowledge of machine learning to being able to apply machine learning models yourself.

The paid course that I would recommend would be the one offered by Dataquest which will teach you the mathematics that goes into machine learning, how to learn programming for machine learning and how to implement the machine learning models.

You can watch a review of Dataquest in the video below.

]]>If you’re looking to start programming in machine learning then you will probably be wondering what the best way to start would be.

This post will show you how to start programming in machine learning.

Here is the recommended sequence that you can take to learn machine learning from being an absolute beginner to having a solid understanding of how to implement the different machine learning algorithms effectively.

- Take the machine learning course taught by Andrew Ng that has no prerequisites
- Learn Python programming with this course
- Learn Data analysis in Python with this Youtube series
- Take the deep learning course taught by Andrew Ng
- Read this book or watch this Youtube series on how to implement machine learning in Python
- Start applying your knowledge of machine learning on Kaggle competitions
- Learn calculus, linear algebra and probability on edX
- Take this more advanced course on machine learning offered by MIT

It is at step 6 that you will be able to program in machine learning effectively. Steps 7 and 8 are recommended if you want to get a solid understanding of the mathematics that goes into the machine learning algorithms and of how to apply them in a more statistically vetted way.

I recommend learning Python since it is the most popular machine learning language and most machine learning tutorials are done in Python. If you already know how to program in Python then you can skip step 4.

If you already know linear algebra, calculus, and probability then my advice to you would be to learn how to program in Python then to skip to step 8 and then to apply your knowledge on your own projects.

Machine learning is where statistical algorithms are applied to data that allows for predictions to be made about new pieces of data.

To learn how to apply the machine learning algorithms it is necessary for you to know how to program, how to do data preprocessing in that programming language, what the machine learning models are and how to apply them in that programming language.

To be able to understand how the machine learning algorithms work mathematically it is necessary to know calculus, linear algebra and probability theory.

To start programming in machine learning the first thing that I would recommend that you do would be to take an introductory course on machine learning.

The course that I would recommend would be this one which is taught by Andrew Ng from Stanford University. You do not need to have any prior knowledge to take the course.

By taking the course straight away you will be able to see whether or not machine learning is something that you are interested in before having to invest a lot of time in learning programming and mathematics.

After taking the introductory course and deciding that machine learning is a field that you would like to enter then it would be time for you to learn a programming language if you haven’t already.

There are a number of different programming languages that get used in machine learning and the best one will depend on what you intend to do.

With that being said, the most popular machine learning programming language is Python and I would recommend that you start out by learning Python. The reason for this is that most machine learning resources use Python, it is an easier machine language to learn and it has a wide range of uses. The course that I would recommend that you use to learn Python would be this one.

Once you have learned how to program in Python it would be worthwhile for you to learn how to do data analysis in Python. This is because it will be necessary for you to know how to massage the data so that it is ready to be used with the machine learning algorithms. You can watch this Youtube series to see how.

Another popular language used in machine learning is R which is a programming language that is mainly used in statistics. It has a number of built-in libraries that allow you to quickly implement the machine learning algorithms. However, so does Python and Python has a wider range of uses.

Javascript is also a programming language that you might want to consider. It is a programming language designed for the web and it would be a useful language to know if you want to make a web-based AI solution.

You can watch the video below to see more about programming languages used in machine learning.

Once you have learned the basics of machine learning and how to program in Python it would be worthwhile for you to learn about deep learning. This is a subset of machine learning algorithms that perform very well when they are given a lot of data.

The huge spike in data that companies have been receiving, in recent years, along with improvements in computational power has resulted in deep learning algorithms becoming very popular.

The course that I would recommend to learn deep learning would be this one.

Once you have learned how to program in Python and what the machine learning algorithms are it would be worthwhile to take a more practical machine learning course that will show you how to implement the machine learning algorithms.

I would recommend this book the most since it will show you how to get the most out of the machine learning algorithms in Python. It is also now in the second edition and the first edition was very popular. If you do not want to buy the book then this Youtube series is also good.

At this point, you will have enough knowledge of machine learning to implement the machine learning algorithms yourself on your own datasets.

You can do this by acquiring your own data to work on or you can compete in competitions on websites such as Kaggle.com.

You’ll likely find that, when you start applying machine learning yourself, you will find gaps in your knowledge. This will help you to learn more effectively since you will be able to hone in on the areas that you are weak on.

Now, you will be able to program in machine learning and you can stop here if you just want to be able to implement the machine learning algorithms yourself.

If you want to learn the mathematics that goes into machine learning then it will be necessary for you to learn calculus, linear algebra and probability.

The courses that I would recommend would be these ones:

Calculus (MIT)

Linear algebra (The University of Texas at Austin) I would also recommend this Youtube series

Probability (MIT)

Once you have learned the mathematics then you could either take a more advanced course on machine learning or you could read a more mathematical machine learning book.

One option would be to read the book Elements of Statistical Learning (second edition) or you could take the machine learning course taught by MIT on edX that you can find here.

If you want to learn how to program in machine learning in a more structured way that takes you through the whole process then it will be necessary for you to take a paid course instead.

The paid course that I would recommend would be this sequence on Dataquest which will teach you everything that you needing to know to program in machine learning including the mathematics and programming in Python.

You can watch a review of Dataquest in the video below.

The amount of time that it will take you will depend largely on what you already know. If you already know how to program in a programming language such as python then it will only be necessary for you to take an introductory machine learning course and then to start applying the machine learning algorithms to your own datasets. You could start doing this after only having spent about 60 hours learning what the machine learning algorithms are and how to apply them.

However, you will not have a strong understanding of the mathematics that goes into the algorithms and you would not likely be fully aware of how to optimize the algorithms. To fully learn those things will likely take you a lot longer.

If you are starting from a point of not knowing any programming at all or any of the mathematics then it will take you longer to learn how to implement the machine learning algorithms and even longer to learn the mathematics that goes into them.

]]>If you are looking to learn machine learning and you are a beginner then you are probably wondering where to start.

This post will help you learn machine learning by giving you a roadmap that you can follow.

So, how does a beginner learn machine learning?

- Take the machine learning course taught by Andrew Ng that has no prerequisites
- Learn Python programming with this course
- Learn Data analysis in Python with this Youtube series
- Take the deep learning course taught by Andrew Ng
- Read this book or watch this Youtube series on how to implement machine learning in Python
- Start applying your knowledge of machine learning on Kaggle competitions
- Learn calculus, linear algebra and probability on edX
- Take this more advanced course on machine learning offered by MIT

There are actually a number of different paths that you can take depending on your goals, whether or not you are willing to spend money and what you already know. Below, I will explain what you need to know, how to go about learning it and some other things that you should consider.

Machine learning is where statistical algorithms are applied to datasets giving the algorithms the ability to make predictions based on the data.

To learn machine learning and to be able to make use of the algorithms it is necessary to have knowledge of a programming language such as Python and to be able to do data analysis in that programming language. To be able to understand the machine learning algorithms it is also necessary to have knowledge of linear algebra, calculus and probability.

With that being said, there are some courses that will teach you how the machine learning algorithms work without assuming any prior knowledge. These courses are a great way to start learning machine learning and to see if machine learning is something that you would be interested in pursuing. I will discuss the introductory courses in a moment but this is the one I recommend you start with.

However, to be able to understand how the machine learning algorithms work mathematically it is necessary to have an understanding of probability, linear algebra and calculus.

There are a number of different programming languages that get used in machine learning and the best one for you to learn will depend on what you intend to do with it.

If you are a beginner then it is most likely that the best programming language for you to learn for machine learning would be Python. This is because it is a very popular programming language in machine learning so there are a lot of machine learning tutorials based on Python, it is easy to implement the algorithms in Python and Python is one of the easier programming languages to learn.

Another popular machine learning programming language is R. It is not as popular as Python but it is popular in statistics and data analysis. It features a number of built-in libraries that allow the machine learning algorithms to get run quickly. If you are a beginner then I would recommend Python over R since it is more popular, there are a wider number of use cases of Python and most machine learning tutorials use Python.

Another programming language that you might want to learn is Javascript. This is a programming language that was designed to be used on the web and it is a useful language to learn if you want to create a web-based ai solution.

You can watch the video below to see more about machine learning programming languages:

Below, I will show you a sequence that you can take to learn machine learning assuming that you do not already know programming, linear algebra, calculus or probability.

There are a number of courses that will teach you machine learning without assuming you have any prior knowledge.

One of these courses are the most popular machine learning course available that is taught by Andrew Ng from Stanford University and you do not have to pay to take the course. The course will show you how the machine learning works, it will teach you the necessary linear algebra and it will teach you how to optimize and choose the machine learning algorithms. You can find the course here.

I would recommend that you start by taking that course because it will allow you to see whether or not machine learning is something that you would be interested in without having to invest too much time.

After taking the course taught by Andrew Ng and deciding that machine learning is a field that you would like to pursue then it would be worthwhile for you to learn Python.

Python is the most popular machine learning language and it is one of the easier languages to learn.

I would recommend that you learn Python by taking this course from MIT on edX. The course will teach you how to code in Python effectively and it will also teach you data structures and elementary computational complexity which will be useful knowledge if you are looking to get a job in machine learning.

After learning how to program in Python it would be worthwhile to take the time now to learn how to do data analysis in Python. This is because you will need to know how to manipulate the datasets so that the machine learning algorithms work well with them.

A good course that will teach you the basics of data analysis would be this series on Youtube.

After you have learned to code in Python it would be worthwhile for you to learn deep learning. This is a subset of the machine learning algorithms that perform very well when they are trained on very large datasets.

Deep learning has become popular, in recent years, because the amount of data that companies have been receiving has increased a lot and computers have become more powerful. This has made deep learning algorithms much more useful.

To learn deep learning I would recommend that you take this course which is also taught by Andrew Ng from Stanford University.

After learning how the machine learning algorithms work it would help to learn how to implement the algorithms effectively in Python.

I would recommend that you do so by reading this book. It is the second edition to a very popular machine learning book that teaches you how to implement and get the most out of machine learning algorithms in Python.

If you do not want to pay for the book then this Youtube series is also good.

Once you know how the machine learning algorithms work and how to apply them it would be time for you to start implementing your knowledge. You can do this by participating in competitions on Kaggle or by applying machine learning to your own datasets.

When you implement the algorithms by yourself you will be able to identify gaps in your knowledge and you will be able to learn quickly.

At this point, you will be able to apply machine learning algorithms by yourself and it would be up to you if you want to learn the mathematics that goes into the algorithms.

If you do want to learn the mathematics then I would recommend the following courses:

Calculus (MIT)

Linear algebra (The University of Texas at Austin) I would also recommend this Youtube series

Probability (MIT)

Once you have learned the necessary mathematics then you can take a more advanced course or read a mathematical machine learning book.

The course that I would recommend that you take would be this one that is taught by MIT on edX.

If you want to learn machine learning as a beginner with no prior knowledge then you could consider taking a paid course.

By taking a paid course you will be able to learn machine learning in a more structured way where you will be able to learn machine learning and the required skills on one platform.

I would recommend Dataquest which will teach you how to program in python, the required mathematics and statistics and machine learning in a sequential order.

You can watch a review of Dataquest in the video below.

There are some additional skills that you may want to learn depending on what you intend to do in machine learning.

**Knowledge of SQL**

If you intend to get a job in machine learning or you intend to create a web-based ai solution then you will likely need to have some knowledge of SQL.

**Knowledge of computational complexity**

In most machine learning interviews, there will be a section where you are tested on your knowledge of computational complexity which is focused on assessing the efficiency of computer algorithms. If you intend to get a job in machine learning then you will need to learn computational complexity.

**Domain knowledge**

To get the most out of the machine learning algorithms it will be helpful to have knowledge of the industry that you are applying machine learning to. This is because it will be helpful to know what variables would likely be good predictors of what you are trying to predict. This is why, if you are looking to get a job in data science, it will often be very helpful to have knowledge of the industry that you will be working in.

]]>If you are a non-programmer that is looking to learn machine learning then you might be wondering whether or not you will be able to learn machine learning and how to go about learning it.

This post will show you how you can learn machine learning as a non-programmer.

So, can a non-programmer learn machine learning? **A non-programmer can learn machine learning. It does not take long to learn the necessary programming for machine learning and there are some machine learning courses that do not assume that you know any machine learning. **

If you are a non-programmer that is looking to learn machine learning then this would be the route that I would suggest that you take:

- Take the machine learning course taught by Andrew Ng that has no prerequisites
- Learn Python programming with this course
- Learn Data analysis in Python with this Youtube series
- Take the deep learning course taught by Andrew Ng
- Read this book or watch this Youtube series on how to implement machine learning in Python
- Start applying your knowledge of machine learning on Kaggle competitions
- Learn calculus, linear algebra and probability on edX
- Take this more advanced course on machine learning offered by MIT

There are many different ways that you can go about learning machine learning and there is a lot of material that is available to learn from. The goal with the above outline is to get you to a stage where you can make use of machine learning as quickly as possible.

Python is the most popular programming language that gets used in machine learning. Python is one of the easier programming languages to learn, it has a large support community and it is very adaptable with a wide range of uses. Additionally, Python comes with a number of built-in libraries that allow machine learning algorithms to be run in only a few lines of code. **Most of the machine learning tutorials are also given using Python so knowing Python will make it easier to learn machine learning. **

R is another popular language that gets used for machine learning. R is mainly used by those in statistical fields and it comes with a number of inbuilt libraries that let the machine learning algorithms get used quickly. However, R is not as widely applicable as Python and it is primarily used for statistics and data analytics.

Another programming language that gets used in machine learning is Javascript. It is a programming language that was specifically designed for the web and it would be a useful language to learn if you intend to make a web-based ai solution.

You can watch the video below to see more about the different programming languages that get used in machine learning.

Since Python is such a popular machine learning language that is also popular in a number of other ways as well I would recommend that you learn Python. Python is a fast language to learn and most machine learning tutorials are done in Python.

To learn Python I would recommend that you take this course. The course is offered by MIT on Edx and you do not have to pay to take the course. Not only will it teach you how to code in Python but it will also teach you data structures, testing and debugging and it also introduces you to algorithmic complexity. If you intend to get a job in machine learning then knowledge of those three things will be very helpful.

Machine learning makes use of a lot of data. In order to implement the machine learning algorithms effectively and to be able to make sense of the data it is necessary for you to have some knowledge of how to do data analysis in a programming language such as Python. This is why I would recommend that you learn how to do data analysis using this Youtube series after learning how to code in Python.

Having a good understanding of data analysis will make it much easier for you to understand machine learning tutorials and to implement the machine learning algorithms on your own.

In addition to having knowledge of programming, there are a number of other things that you will need to know in order to be able to implement the machine learning algorithms effectively.

Skills that are required to fully understand the machine learning algorithms and how to effectively make use of them include calculus, linear algebra, probability and statistics.

With that being said, there are some courses that will teach you machine learning without assuming that you have any knowledge of any of the above.

One of those courses is the machine learning course taught by Andrew Ng who is a professor at Stanford University. This course will teach you about all of the machine learning algorithms and will also teach you the linear algebra that you need to know to be able to understand the material. He does go into the calculus behind the algorithms, at times, briefly. However, you can still understand what is going on without knowledge of calculus.

**I would recommend that you start with Andrew Ng’s course before you start anything else including learning Python. This is because it will give you a chance to see whether or not machine learning is for you without investing too much time into learning it. **

Once you have taken the above machine learning course taught be Andrew Ng and you have decided that machine learning is for you then I would recommend that you now take the time to learn Python and data analysis in Python using the courses linked to above.

After you have learned how to program with Python I would then recommend that you take Andrew Ng’s deep learning course. This course will teach you all that you need to know about deep learning using the Python programming language and it does not assume that you know calculus or linear algebra.

Deep learning is a subset of machine learning where the algorithms are able to become very accurate when they are given a lot of data. Deep learning algorithms were not very popular until recent years because it has only been in recent years that we have had access to enough data and computational power to make use of the algorithms.

Once you have learned what the machine learning and deep learning algorithms are it would be very helpful to make use of some material that teaches you the specifics of how to implement the algorithms in Python.

I would strongly recommend that you use this book to learn how to implement the machine learning algorithms. It is the second edition of a very popular first edition that went into the specifics of how to get the best out of the machine learning algorithms and how to make use of them in Python.

If you do not want to pay for the book then this Youtube series would be a good alternative way of learning how to implement the machine learning algorithms.

Once you have learned how to implement the machine learning algorithms then I would recommend that you start working on your own machine learning projects. You can do this by finding your own datasets to make use of or you can compete in competitions on Kaggle.

Active learning is one of the best ways to learn and you will learn a lot by implementing the machine learning algorithms and trying to optimize them on your own.

At this point, it would be worthwhile for you to learn the necessary mathematics to learn exactly how the machine learning algorithms work.

The courses that I would recommend that you use to learn the required mathematics are as follows:

Calculus (MIT)

Linear algebra (The University of Texas at Austin) I would also recommend this Youtube series

Probability (MIT)

Once you have learned the necessary mathematics then it would be worthwhile for you to learn from a more advanced book or course. I would recommend that you do this by taking the machine learning course offered by MIT. In this course they will teach you the mathematics that goes into the machine learning algorithms and the probabilistic elements involved.

While there is a lot of free material that you can use to learn machine learning and programming with, none of the material will take you directly from zero-programming knowledge to knowing how to effectively make use of machine learning.

That is why you might want to consider taking a paid course since there are a number that will take you through the entire process of learning everything involved in learning and implementing machine learning.

The course that I would recommend that you use would be Dataquest. This course will teach you machine learning even if you have no programming knowledge and it will also teach you the necessary mathematics that you will need to know.

You can watch the video below to see a review of Dataquest.

The amount of time that it will take you will depend on how much mathematics you know already.

To learn Python using the course linked to above it will take 9 weeks while spending 15 hours per week on the course. Andrew Ng’s machine learning course takes 55 hours to complete and his deep learning course takes 3 months with 11 hours per week being spent on the course.

It will also take you some time to learn how to implement the machine learning algorithms in Python. If you use the book that I suggest above then you should expect it to take at least 50 hours to work through.

If you don’t already know calculus, linear algebra and probability then you’ll also need to learn those if you want to learn the mathematics that goes into the algorithms. The courses that I linked to above are semester-long courses offered by MIT so it would take about three months to learn each of them.

The machine learning course offered by MIT also takes 15 weeks to complete and you should expect to spend 10-14 hours per week on the course.

Computational complexity is an area of computer science that focuses on how long it takes for computer programs to run.

Whether or not you should learn algorithmic complexity in detail depends on how far you want to go with machine learning and if you intend to get a job in machine learning. Many machine learning job interviews will test you on algorithmic complexity so you will need to learn it if you want to get a job in machine learning.

]]>If you are interested in learning machine learning but you are not sure where to start then you might not be sure where to start.

The aim of this post will be to give you a roadmap of the things that you can do to learn the ins and outs of machine learning in a time effective manner.

**So, how to learn machine learning from zero? **

Here are the steps that I would recommend that you take

- Take the machine learning course taught by Andrew Ng that has no prerequisites
- Learn Python programming with this course
- Learn Data analysis in Python with this Youtube series
- Take the deep learning course taught by Andrew Ng
- Read this book or watch this Youtube series on how to implement machine learning in Python
- Start applying your knowledge of machine learning on Kaggle competitions
- Learn calculus, linear algebra and probability on edX
- Take this more advanced course on machine learning offered by MIT

There are many different possible paths that you can take and the above is just one of them. I recommended the above because of the high reviews that the courses have received and the cheapness of the courses. The only thing that you would have to pay for above would be the book that I recommend at step 5.

If you want to take a course that will take you from zero to being able to make use of machine learning and you are willing to pay then I would recommend that you do this course offered by Dataquest.

This course will assume that you have no prior knowledge and will interactively teach you all of the required material to learn machine learning. The course will even teach you the calculus, statistics and linear algebra that you need to know for machine learning so it will help to save time by just teaching you what you need to know.

You can see a review of Dataquest below if you are thinking of going down that route.

There are courses, such as the one offered by Andrew Ng, that will teach you machine learning without assuming any prior knowledge.

However, to implement the machine learning algorithms it will be necessary for you to learn a programming language such as Python and how to prepare data using that programming language.

It will also be necessary for you to learn calculus, probability and linear algebra if you want to be able to get a mathematical understanding of how the machine learning algorithms work and of how to make use of them effectively.

If you are looking to learn machine learning and you do not have any prior knowledge then I would recommend that you start out by taking Andrew Ng’s course on machine learning.

The course does not assume any prior knowledge and features a section where he teaches you the necessary linear algebra. However, he doesn’t teach you calculus so it will be difficult to understand certain parts of the course. This is not an issue, however, since he teaches it in a way such that you can fully understand the course without having a knowledge of calculus.

The main reason that I would recommend that you start with this course is that it will give you the opportunity to see whether or not machine learning is for you before you have invested a lot of time into learning the mathematics and programming.

After taking the course above and deciding that you are interested in machine learning then it will be necessary to learn how to implement the machine learning models in a programming language such as Python.

I would recommend that you learn Python specifically since it is very popular in the machine learning community and it is an intuitive and easy programming language to learn.

The course that I would recommend that you use to learn Python would be this one since it also teaches you computer science fundamentals. This will help you, later on, if you decide to progress to more advanced material in machine learning and if you start looking for a job in machine learning.

After having learned Python it would be worthwhile for you to learn some data analysis in Python. This is because it will be necessary for you to know how to manipulate data so that it is in an optimal form for the machine learning models. I would recommend that you watch this Youtube series to learn data analysis in Python.

Once you have learned how to program in Python then I would recommend that you work through this course offered by Andrew Ng. The course teaches deep learning algorithms which are a special group of machine learning algorithms that are able to get high levels of accuracy when they are given a lot of data to learn from.

The next recommendation that I would make would be to read this book that will teach you everything that you need to know about implementing the machine learning algorithms in Python.

If you do not want to spend money on the book then a good alternative would be to watch this Youtube series.

At this point, you will know what the different machine learning algorithms are and how to implement them. This is when you should start actively working on implementing your own machine learning projects.

A good way to do this is by competing in Kaggle competitions. Kaggle is a website that hosts paid machine learning competitions where people compete with each other to get the highest possible score by applying machine learning models to different types of datasets.

**Active learning is the most effective form of learning. By implementing the algorithms on Kaggle you will be able to identify gaps in your knowledge and learn the things that you need to learn the most. **

If you want to learn the mathematical underpinnings of machine learning then it will be necessary for you to learn calculus, linear algebra and probability. Having knowledge of probability and statistics will also be especially helpful since it will help you to learn how to implement the algorithms in a more statistically stringent way.

The courses that I would recommend you use to learn these subjects are as follows:

Calculus (MIT)

Linear algebra (The University of Texas at Austin) I would also recommend this Youtube series

Probability (MIT)

Once you have learned the mathematics that is required to learn machine learning then it would be worthwhile for you to go through a course that teaches you the mathematics that goes into the machine learning algorithms.

The course that I would recommend would be this one.

To learn machine learning with Andrew Ng’s course will take 50 hours. However, to also learn Python and data analysis using the courses linked above will take another 140 hours. You will then need to learn how to implement the algorithms using the book linked to above which you should expect to take at least another 50 hours. So, you would be looking at 240 hours to get to a point where you can comfortably implement machine learning algorithms **without any prior knowledge.**

If you then want to effectively learn the mathematics that goes into the machine learning then it would also be necessary for you to learn calculus, linear algebra and probability and then to take a more advanced machine learning course such as the one offered by MIT. This will likely double the amount of time that it would take.

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