Learn how to use TensorFlow 2.0 in this full tutorial course for beginners.

This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

Notebooks

Course Contents

  • ⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
  • ⌨️ Module 2: Introduction to TensorFlow (00:30:08)
  • ⌨️ Module 3: Core Learning Algorithms (01:00:00)
  • ⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
  • ⌨️ Module 5: Deep Computer Vision – Convolutional Neural Networks (03:43:10)
  • ⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
  • ⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
  • ⌨️ Module 8: Conclusion and Next Steps (06:48:24)

Learn the essentials of statistics in this complete (and free!) course from freeCodeCamp.org.

This course introduces the various methods used to collect, organize, summarize, interpret and reach conclusions about data. An emphasis is placed on demonstrating that statistics is more than mathematical calculations. By using examples gathered from real life, students learn to use statistical methods as analytical tools to develop generalizations and meaningful conclusions in their field of study.