Learning Path to Machine Learning Learning Path for understanding the implementation and mathematical background of Machine Learning algorithms Theoretical Phase 1. Linear Algebra https://www.coursera.org/learn/linear-algebra-machine-learning 2. Calculus https://www.coursera.org/learn/multivariate-calculus-machine-learning 3. Statistics https://www.edx.org/course/probability-the-science-of-uncertainty-and-data 4. Algorithms https://www.coursera.org/learn/algorithms-part1 https://www.coursera.org/learn/algorithms-part2 Optional Phase If you are not familiar with programming, reading this book is recommended: 5. Python Crash Course: A Hands-on, Project-Based Introduction to Programming (Can be found in the following post) Practical Phase 6. Data Science & Python https://www.coursera.org/learn/python-data-analysis?specialization=data-science-python 7. Data Visualization https://www.coursera.org/learn/python-plotting?specialization=data-science-python 8. Machine Learning https://www.coursera.org/learn/machine-learning (After this you can start participating in Kaggle competitions) 9. Deep Learning https://www.coursera.org/specializations/deep-learning