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With strong roots in statistics, Machine Learning is becoming one of the most exciting and fast-paced computer science fields. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent.

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Chatbots, spam filtering, ad serving, search engines, and fraud detection are among just a few examples of how machine learning models underpin everyday life. Machine learning lets us find patterns and create mathematical models for things that would sometimes be impossible for humans to do.

**Important Machine Learning courses **

Machine Learning — Coursera

This is the course for which all other machine learning courses are judged.

The course uses the open-source programming language Octave instead of Python or R for the assignments. This might be a deal-breaker for some, but Octave is a simple way to learn the fundamentals of ML if you’re a complete beginner.

Overall, the course material is extremely well-rounded and intuitively articulated by Ng. The math required to understand each algorithm is completely explained, with some calculus explanations and a refresher for Linear Algebra. The course is fairly self-contained, but some knowledge of Linear Algebra beforehand would help.

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Course structure:

- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Application Example: Photo OCR

This is undoubtedly the best course to start with a newcomer.

**Deep Learning Specialization — Coursera**

This specialization is a more advanced course series for anyone interested in learning about neural networks and Deep Learning, and how they solve many problems.

The assignments and lectures in each course utilize the Python programming language and use the TensorFlow library for neural networks. This is naturally an excellent follow-up to Ng’s Machine Learning course since you’ll receive a similar lecture style but now will be exposed to using Python for machine learning.

Courses:

- Neural Networks and Deep Learning
- Introduction to Deep Learning
- Neural Network Basics
- Shallow Neural Networks
- Deep Neural Networks

- Improving Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models

To understand the algorithms presented in this course, you should already be familiar with Linear Algebra and machine learning in general.

**Machine Learning Crash Course — Google AI**

This course comes from Google AI Education, a completely free platform that’s a mix of articles, videos, and interactive content.

The Machine Learning Crash Course covers the topics needed to solve ML problems as soon as possible. Like the previous course, Python is the programming language of choice, and TensorFlow is introduced. Each main section of the curriculum contains an interactive Jupyter notebook hosted on Google Colab.

Courses:

- Linear and Logistic Regression
- Classification
- Training and loss
- Reducing Loss – gradient descent, learning rates
- TensorFlow
- Overfitting
- Training sets, splitting, and validation
- Feature Engineering and cleaning data
- Feature Crosses
- Regularization – L1 and L2, Lambda
- Model performance metrics
- Neural Networks – single and multi-class
- Embeddings
- ML Engineering

This is the best option in this list if you have tinkered with ML but are looking to cover all your bases. The course discusses many nuances of machine learning that may otherwise take hundreds of hours to learn serendipitously.

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**Machine Learning with Python — Coursera**

Another beginner course, but this one focuses solely on the most fundamental machine learning algorithms. The instructor, slide animations, and explanation of the algorithms combine very nicely to give you an intuitive feel for the basics.

This course uses Python and is somewhat lighter on the mathematics behind the algorithms. With each module, you’ll get a chance to spool up an interactive Jupyter notebook in your browser to work through the new concepts you just learned. Each notebook reinforces your knowledge and gives you concrete instructions for using an algorithm on real data.

Course structure:

- Intro to Machine Learning
- Regression
- Classification
- Clustering
- Recommender Systems
- Final Project

One of the best things about this course is the practical advice given for each algorithm. When introduced to a new algorithm, the instructor provides you with how it works, its pros and cons, and what sort of situations you should use it in.

**Advanced Machine Learning Specialization — Coursera**

This is another advanced series of courses that casts a very wide net. If you are interested in covering as many machine learning techniques as possible, this Specialization is the key to a balanced and extensive online curriculum.

The instruction in this course is fantastic: extremely well-presented and concise. Due to its advanced nature, you will need more math than any other courses listed so far. If you have already taken a beginner course and brushed up on linear algebra and calculus, this is a good choice to fill out the rest of your machine learning expertise.

Much of what’s covered in this Specialization is pivotal to many machine learning projects.

Courses:

- Introduction to Deep Learning
- Intro to Optimization
- Intro to Neural Networks
- Deep Learning for Images
- Unsupervised Representation Learning
- Deep Learning for Sequences
- Final Project

- How to Win Data Science Competitions: Learn from Top Kagglers

- Bayesian Methods for Machine Learning
- Practical Reinforcement Learning
- Deep Learning in Computer Vision
- Natural Language Processing
- Addressing the Large Hadron Collider Challenges by Machine Learning

It takes about 8-10 months to complete this series of courses, so if you start today, in a little under a year, you’ll have learned a massive amount of machine learning and be able to start tackling more cutting-edge applications.

Throughout the months, you will also be creating several real projects that result in a computer learning how to read, see, and play. These projects will be great candidates for your portfolio and will result in your GitHub looking very active to any interested employers.

**Machine Learning — EdX**

This is an advanced course with the highest math prerequisite out of any other course on this list. You’ll need a very firm grasp of Linear Algebra, Calculus, Probability, and programming. The course has interesting programming assignments in either Python or Octave, but the course doesn’t teach either language.

One of the biggest differences with this course is the coverage of the probabilistic approach to machine learning. If you’ve been interested in reading a textbook, like Machine Learning: A Probabilistic Perspective — which is one of the most recommended data science books in Master’s programs — then this course would be a fantastic complement.

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Course structure:

- Maximum Likelihood Estimation, Linear Regression, Least Squares
- Ridge Regression, Bias-Variance, Bayes Rule, Maximum a Posteriori Inference
- Nearest Neighbor Classification, Bayes Classifiers, Linear Classifiers, Perceptron
- Logistic Regression, Laplace Approximation, Kernel Methods, Gaussian Processes
- Maximum Margin, Support Vector Machines (SVM), Trees, Random Forests, Boosting
- Clustering, K-Means, EM Algorithm, Missing Data
- Mixtures of Gaussians, Matrix Factorization
- Non-Negative Matrix Factorization, Latent Factor Models, PCA and Variations
- Markov Models, Hidden Markov Models
- Continuous State-space Models, Association Analysis
- Model Selection, Next Steps

**Introduction to Machine Learning for Coders — Fast.ai**

Fast.ai produced this excellent, free machine learning course for those that already have roughly a year of **Python** programming experience.

It’s astounding how much time and effort the founders of Fast.ai have put into this course — and other courses on their site. The content is based on the University of San Diego’s Data Science program, so you’ll find that the lectures are done in a classroom with students, similar to the MIT OpenCourseware style.

The course has many videos, some homework assignments, extensive notes, and a discussion board. Unfortunately, you won’t find graded assignments and quizzes or certification upon completion, so Coursera/Edx would be a better route for you if you’d rather have those features.

Course Structure:

- Introduction to Random Forests
- Random Forest Deep Dive
- Performance, Validation, and Model Interpretation
- Feature Importance. Tree Interpreter
- Extrapolation and RF from Scratch
- Data Products and Live Coding
- RF From Scratch and Gradient Descent
- Gradient Descent and Logistic Regression
- Regularization, Learning Rates, and NLP
- More NLP and Columnar Data
- Embeddings
- Complete Rossmann. Ethical Issues

This course is excellent if you’re a programmer who wants to learn and apply ML techniques, but I find there is one drawback: they teach machine learning through the use of their open-source library (called fastai), which is a layer over other machine learning libraries, like PyTorch.

**Fundamental Algorithms**

There’s a base set of algorithms in machine learning that everyone should be familiar with and have experience using. These are:

- Linear Regression
- Logistic Regression
- k-Means Clustering
- k-Nearest Neighbors
- Support Vector Machines (SVM)
- Decision Trees
- Random Forests
- Naive Bayes

These are the essentials, but there are many, many more. The courses listed above contain essentially all of these with some variation. Understanding how these techniques work and when to use them will be critical when taking on new projects.

After the basics, some more advanced techniques to learn would be:

- Ensembles
- Boosting
- Dimensionality Reduction
- Reinforcement Learning
- Neural Networks and Deep Learning

This is just a start, but these algorithms are what you see in some of the most interesting machine learning solutions, and they’re practical additions to your toolbox.

And just like the basic techniques, with each new tool, you learn you should make it a habit to apply it to a project immediately to solidify your understanding and have something to go back to when in need of a refresher.

**Tackle a Project**

Learning machine learning online is challenging and extremely rewarding. It’s important to remember that just watching videos and taking quizzes doesn’t mean you’re really learning the material.

As soon as you start learning the basics, you should look for interesting data that you can use while experimenting with your new skills. The courses above will give you some intuition on when to apply certain algorithms, and so it’s a good practice to use them in a project of your own immediately.

Through trial and error, exploration, and feedback, you’ll discover how to experiment with different techniques, how to measure results, and how to classify or make predictions. For some inspiration on what kind of ML project to take on, see this list of examples.

Tackling projects gives you a better high-level understanding of the machine learning landscape. As you get into more advanced concepts, like Deep Learning, there’s virtually an unlimited number of techniques and methods to understand.

Machine learning is incredibly enjoyable and exciting to learn and experiment with, and I hope you found a course above that fits your own journey into this exciting field.

Machine learning makes up one component of Data Science. If you’re also interested in learning about statistics, visualization, data analysis, and more be sure to check out the top data science courses, which is a guide that follows a similar format to this one.

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