machine learningWell, if you’re reading this, you already know what Machine Learning is but just as a formality:

Machine Learning (ML), a subset of artificial intelligence (AI), is the area of computational science that focuses on interpreting, analysing patterns and structures in data to enable learning and decision making without any human intervention. In simple words, machine learning allows the user to feed the computer an algorithm, an insane amount of data, have the computer analyse it, and make decisions based only on the input data.

Machine Learning, or AI, has gained an immense popularity over the last few years and still continues to do so. Right now, it complements the Big Data trend in the tech industry, where it proves to be incredibly helpful in sorting through the data, making predictions, and calculated suggestions. Even we have been experimenting with AI/ML modules for software development which includes pharmaceutical billing software and various others.

Of course, the amazement it carries with it is well known and understood.

But this is usually the point where everyone interested stops and asks:
“How do I get in on this?”
Fret not, we got your back.

1. Stanford University’s Machine Learning Course
Andrew Ng is the man. The founder of Google Brain and former chief scientist at Baidu, Andrew Ng’s course is the clear winner in terms of ratings, reviews, and syllabus fit. Seeing how this course was what practically founded Coursera, that doesn’t seem unbelievable. Although it has a smaller scope than the original Stanford class, it covers a large number of algorithms and techniques. The estimated timeline is eleven weeks, which includes two weeks of neural networks and deep learnings.

2. Columbia University’s Machine Learning Course
A relatively new entrant in the edX course catalog. Professor John Paisley is noted as brilliant, clear, and clever. The course covers more algorithms than the Stanford course and almost all of the aspects of the machine learning workflow. It has a rather advanced introduction that demands that the students be clear on all their prerequisites: calculus, linear algebra, statistics, probability, and coding. The modes of evaluation include quizzes, programming assignments, and a final exam. Python, Octave, or MATLAB are encouraged. The course timeline is estimated to be around twelve weeks.

3. Machine Learning A-Z: Hands-On Python and R In Data Science
This course has been taken by over 200,000 students and it is rated at an average of 4.5 out of 5. Developed by Kirill Eremenko, Data Scientist & Forex Systems Expert and Hadelin de Ponteves, Data Scientist, this is one of the best machine learning courses available online. Preferring a more hands-on approach with Python and R, this course will enable you to make accurate predictions, develop a knack of many models, and handle tools like NLP and Deep Learning.
What are the prerequisites? High school mathematics.
No, there is no catch here.

4. Python for Data Science and Machine Learning Bootcamp
As the name suggests, this course will help you understand on how to use Python to analyse data, and use powerful machine learning algorithms. The course deals with using NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more. The course has been designed by Jose Portilla, a BS and MS in Engineering from Santa Clara University. Although the Machine Learning workflow has been dealt with appropriately, the course shines in its in-depth coverage of the entire data science process.

5. Google – Machine Learning
Google’s attempt at teaching Machine Learning to the masses takes a slightly in-depth approach. It is not aimed at complete novices, however, but it requires only the most basic understanding of some concepts. It focuses on deep learning and the design of self-teaching systems. The course is aimed at people ranging from expert data analysts, to enterprising individuals wanting to make use of the limitless open source libraries and resources available.

6. TensorFlow 101: Introduction to Deep Learning

Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning.

7.  The Complete Machine Learning Course with Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

8. Deployment of Machine Learning Models

Learn how to integrate robust and reliable Machine Learning Pipelines in Production

9. Unsupervised Machine Learning Hidden Markov Models in Python

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

10. Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

So, these were the courses that you must look in order to master machine learning.