The Best Machine Learning Courses

Best machine learning courses 2021. Learn Machine Learning this year from these top courses. Average time to learn is between 4-10 months. Curriculum and learning guide included.

The Best Machine Learning Courses

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

Chat bots, 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 is what lets us find patterns and create mathematical models for things that would sometimes be impossible for humans to do.

What is the criteria that makes a really good machine learning course?

After several years of following the e-learning landscape and enrolling in countless machine learning courses from various platforms, like Coursera, Edx, Udemy, Udacity, and DataCamp, I’ve collected the best machine learning courses currently available.

  • Strictly focus on machine learning
  • Use free, open-source libraries for those languages. Some instructors and providers use commercial packages, so these courses are removed from consideration.
  • Use free, open-source programming languages, namely Python, R, or Octave
  • Contain programming assignments for practice and hands-on experience
  • Explain how the algorithms work mathematically
  • Have engaging instructors and interesting lectures
  • Have above-average ratings and reviews from various aggregators and forums
  • Be self-paced, on-demand or available every month or so

There are numerous certification courses in machine learning. I have put together a list of a few of the best courses, generally free or at slightest reasonable, that will assist you ended up an ML master.

#1 Machine Learning by Andrew Ng— Coursera

This is probably the popular Machine learning certification taught by AI and ML pioneer Andrew Ng and Stanford University, which also includes certification.

You’ll be tested on each and every topic that you learn in this course, and based on the completion and the final score that you get, you’ll be awarded the certificate.

This course does add value to you as a developer and gives you a good understanding of the mathematics behind all the machine learning algorithms that you come up with.

I personally really like this one. Andrew Ng takes you through the course using Octave, which is an excellent tool to test your algorithm before making it go live on your project.

Overall, the course material is extremely well-rounded and intuitively articulated by Ng. All of 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 definitely help.

Here is the link to join this course  Machine Learning by Andrew Ng (Coursera)

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 course will teach you how to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. This also one of the top-rated and hands-on course which will teach you deep learning by giving some real-world examples.

Here are the key things you will learn in this course:

  • Linear and Logistic Regression
  • Supervised and Unsupervised Machine Learning
  • How to build a neural net with Python and NumPy
  • How to build a neural net with Google's TensorFlow
  • The Backpropagation training method
  • Bayesian Machine Learning
  • Convolutional Neural Networks
  • Hidden Markov Models
  • Natural Language Processing with Deep Learning

In short, a good training course to learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

Here is the link to join this course  Deep Learning A-Z: Hands-On Artificial Neural Networks (Udemy)

This is probably the new Machine learning certification taught by Dr.Mushtaq Hussain an experience trainer and assistant professor with demonstrated history of working in the corporate and education industry

This course does add value to you as a developer and gives you a good understanding of the mathematics behind all the machine learning algorithms that you come up with.

The best thing about this course is that its free of cost and well organized. Its covers all aspects of machine learning understanding.

Course structure:

  • Introduction
  • Linear Regression with One Variable
  • Linear Algebra
  • Linear Regression with Multiple Variables
  • Octave/Matlab Tutorial
  • Logistic Regression
  • Regularization
  • Neural Networks: Representation
  • Performance Parameters
  • DataSet
  • Data Sampling and Model evaluation
  • Decision Tree
  • Graph Neural Networks
  • Data Splitting

Here is the link to join this course — Machine Learning by Dr. Mushtaq Hussain (Coursesteach)

#4 Machine Learning — EdX

This is an advanced course that has the highest math prerequisite out of any other course in 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.

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

Here is the link to join this course — Machine Learning -Edx

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