The list of free and non-free resources and courses for quick start in Machine Learning and Data Science. I built this list by searching in Google and taking references from articles about ML and data science. It is updated from time to time and you also can suggest one in comments.

The discipline can be divided to the following areas:

- Mathematics, applied statistics and the probability theory
- Programming (R or Python in particular) and working with tools for data analysis
- Machine learning methods including deep learning

Courses and resource for mathematics and statistics are skipped because this subject is covered with courses for R and Python.

# Programming

According to the poll results of KDnuggets, R, Python Duel As Top Analytics, Data Science software two languages are the most popular in the discipline: R and Python. This article describes the difference between two.

Below is the list of resources and courses for both of them.

## R language

### Courses

- On Edx.org (free courses)
- On DataCamp (free courses)
- Pluralsight (not free)

### Resources

## Python

### Courses

- On Edx.org (free courses)
- On DataCamp (free courses)
- Pluralsight (not free)
- Learn to program in Python (codecademy)

### Resources

# Machine Learning

### Courses

- Machine Learning at Coursera.org
- Neural Networks for Machine Learning at Coursera.org
- Data Analyst Nanodegree at Udacity.com
- Machine Learning Engineer Nanodegree at Udacity.com
- Process Mining: Data science in Action at Coursera.org
- On Edx.org (Machine Learning, Data Science)
- On DataCamp (Machine Learning)
- Pluralsight (not free)

### Tools

- Getting Started with Azure Machine Learning at Pluralsight
- Developing Big Data Solutions with Azure Machine Learning at Edx.org

### Problems

General problems which are the foundation of all business related problems:

- Classification
- Regression
- Clustering
- Rule Extraction

More about Machine Learning problems here.

The problems which Machine Learning helped to solve at some level:

- Manual data entry
- Detecting Spam
- Product recommendation
- Medical Diagnosis
- Customer segmentation and Lifetime value prediction
- Financial analysis
- Predictive maintenance
- Image recognition

More about solved problems here.

### Algorithms

There are a set of well-known algorithms which are used in most of the tasks. More about it here.

- Naïve Bayes Classifier Algorithm
- K Means Clustering Algorithm
- Support Vector Machine Algorithm
- Apriori Algorithm
- Linear Regression
- Logistic Regression
- Artificial Neural Networks
- Random Forests
- Decision Trees
- Nearest Neighbours

All algorithms are classified as follows:

- Supervised
- Unsupervised
- Reinforcement

# Advanced Areas

- Large Scale Machine Learning, building of algorithms when the model is studying with data which cannot be loaded in RAM of a single PC
- IoT (Internet of Things)

# See Also

Web sites related to the discipline:

- KDnuggets: Leading site on Business Analytics, Big Data, Data Mining, Data Science, and Machine Learning
- Kaggle: a platform for data-related competitions
- Hackerrank: Statistics and Machine Learning Challenges
- Tianchi: data sets & challenges

Articles with additional information:

Other courses

- A developer’s guide to the Internet of Things (IoT) at Coursera.org

E-Learning Platforms:

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