Free online materials to learn Machine Learning and Data Science

We know that many of you are interested in Machine Learning and Data Science and you are constantly looking for freely available free learning materials. That’s why we have prepared for you a list of useful online courses and materials 📖🥕:

Required tools to begin with:

Python ( Python 3 is the best option)
IPython and Jupyter Notebook (Jupyter Notebook)
Libraries for calculations: NumPy, Pandas, Scikit-Learn, Matplotlib.

You can install Python 3 and all the necessary packages in a few clicks using the Anaconda Python. Anaconda is a quite popular tool among machine learning engineers.

It’s okay if you have Python 2.7 installed. Upgrading to Python 3 is not necessary. Instead of Anaconda, you can use pip.

Online courses:

Machine Learning

• Andrew Ng’s Machine Learning Class on Coursera: https://www.coursera.org/learn/machine-learning
• Hugo Larochelle’s Neural Net lectures:
  https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: https://www.coursera.org/learn/introduction-tensorflow
• Daphne Koller’s Probabilistic Graphical Models: http://work.caltech.edu/lectures.html
• Cal Tech’s Learning from Data course: http://work.caltech.edu/lectures.html
• Andrew Ng’s Structuring Machine Learning Projects: https://www.coursera.org/learn/machine-learning-projects
• Machine Learning for Business Professionals: https://www.coursera.org/learn/machine-learning-business-professionals
• Applied Machine Learning in Python: https://www.coursera.org/learn/python-machine-learning

Sztuczna Inteligencja

• MIT’s Artificial Intelligence course: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/
• IBM’s Introduction to Artificial Intelligence (AI): https://www.coursera.org/learn/introduction-to-ai
• Andrew Ng’s AI For Everyone: https://www.coursera.org/learn/ai-for-everyone

Data Analysis

• Duke’s University Statistics with R Specialization: https://www.coursera.org/specializations/statistics
• Cloudera’s Modern Big Data Analysis with SQL Specialization: https://www.coursera.org/specializations/cloudera-big-data-analysis-sql
• EPFL’s Big Data Analysis with Scala and Spark: https://www.coursera.org/learn/scala-spark-big-data
• IBM’s Data Analysis with Python: https://www.coursera.org/learn/data-analysis-with-python
• Advanced statistical computing course by Vanderbilt University:
  http://stronginference.com/Bios8366/lectures.html

Programming

• Rice’s Introduction to Interactive Programming in Python: https://www.coursera.org/learn/interactive-python-1
• MIT’s Introduction to Computer Science & Programming in Python: https://www.edx.org/course/6-00-1x-introduction-to-computer-science-and-programming-using-python-3#.UzMf-K2wJr4
• Python For Everybody by University of Michigan: https://www.coursera.org/learn/python
• John Hoppkins’s R Programming: https://www.coursera.org/learn/r-programming

Other useful materials:

1. Data Science self-study material: http://learnds.com/
2. Collection of notes “Practical data science in Python”: https://radimrehurek.com/data_science_python/
3. Video “Introduction to machine learning with scikit-learn” by Kevin Markham: http://blog.kaggle.com/2015/04/08/new-video-series-introduction-to-machine-learning-with-scikit-learn/
4. Book “The Elements of Statistical Learning” by Professor Pedro Domingos from the University of Washington: https://web.stanford.edu/~hastie/ElemStatLearn//

Enjoy learning 😉