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
This month we gonna divider in to four weeks for study.
Introduction to probabilistic models, including random processes and the basic elements of statistical inference.
Questions for study probability.
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.
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.
This youtube channel have so many videos with so much theory of calculous.
Learn about the core principles of computer science: algorithmic thinking and computational problem solving.
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.
This month we gonna study Python.
Python:
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
This site is one interactive Python tutorial.
This month we gonna see so many courses of machine learning, and now you can decide your path.
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.
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).
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.
In this hands-on course, you will learn the basics of deep learning by training and deploying neural networks.
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.
Team Leads (Contacts) : Lucas Fonseca