Hands-on Machine Learning and Artificial Intelligence Course

Jump into the fascinating world of artificial intelligence

Hands-on Machine Learning and Artificial Intelligence

Jump into the fascinating world of artificial intelligence

What do cancer detection, sentiment analysis, image recognition, machine translation and playing atari games have in common? These are all complex real-world tasks, and the goal of artificial intelligence (AI) is to tackle these with powerful mathematical and programmatic tools. In this course, you will learn the foundational principles that enables machines to make autonomous decisions and practice implementing some of these systems. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in your field of interest.


Key elements you will encounter:

  • The concept of loss functions and gradient descent optimizer
  • Build machine learning models based on domain knowledge
  • Visualize and explore datasets using python
  • Extract informative features and transform them to fit into specific algorithm
  • Implement NLP and text analysis tools to analyze sentiment of tweets
  • Get familiar with the theory and implementations of several learning algorithms: ANN, XGB, Random Forest, Logistic Regression

Intended Audience

This course is intended for individuals interested in machine learning and AI who would like to bootstrap their skills and knowledge to a level in which they will be able to start acquiring significant experience in applying machine learning algorithms to real life projects


We recommend that attendees of this course have the following prerequisites:

  • Academic skill level of calculus, linear algebra, statistics
  • Basic programming experience
  • Python, Analytical approach - Advantage


  Module 1 - Get to know the math

  • Basic concepts in machine learning
  • Linear regression
  • Classification task
  • Dependent and explanatory variables
  • Feature extraction

  Module 2 - Decision Trees

  • Classification with decision trees
  • Feature importance and automatic feature selection by information gain
  • Overfitting and regularization in trees
  • Tree ensemble

  Module 3 - Neural Netwoks

  • Backpropagation - implement a single neuron
  • Neural nets. playground
  • Activation functions
  • Image recognition using DNN, CNN

  Module 4 - Reinforcement learning

  • Imitation learning
  • Deep Q learning
  • OpenAI Gym - learning to play games
  • Policy iterations, value iterations

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