williamkoller / machine-learning-path

Repository to help new students in Machine Learning :robot:

Machine Learning - 3 months practice

In this repository are some courses and path to learn Machine Learning in 3 - 4 months. I hope I can help people who are just starting out

---

Project Status

Month 1

This month we gonna divider in to four weeks for study.

Week 1

Probability

• Probability - The Science of Uncertainty and Data

Introduction to probabilistic models, including random processes and the basic elements of statistical inference.

• Probability

Questions for study probability.

Week 2

Linear Algebra

• Linear Algebra

This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.

• Linear Algebra

This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering.

Week 3

Calculous

• Calculous - Essences of calculous

This youtube channel have so many videos with so much theory of calculous.

Week 4

Algorithms

• Algorithm Design and Analysis

Learn about the core principles of computer science: algorithmic thinking and computational problem solving.

• Intro Algorithms

This class will give you an introduction to the design and analysis of algorithms, enabling you to analyze networks and discover how individuals are connected.

Month 2

This month we gonna study Python.

Python:

• Intro to Python for Data Science

This course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses

• Learn Python

This site is one interactive Python tutorial.

Month 3

This month we gonna see so many courses of machine learning, and now you can decide your path.

• Google AI

Whether you’re just learning to code or you’re a seasoned machine learning practitioner, you’ll find information and exercises to help you develop your skills and advance your projects.

• Machine Learning - Stanford University

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

• Machine Learning - Columbia University

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection. Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

• Fundamentals of Deep Learning for Computer Vision - Nvidia and Nvidia Deep Learning Institute

In this hands-on course, you will learn the basics of deep learning by training and deploying neural networks.

Conclusion

After this path you can start to implement you machine learning algorithms and enjoy the IA world. Please if you have some question or like to add other courses please open one PR.

Contributing DSWG Members

Team Leads (Contacts) : Lucas Fonseca