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Dr. Muhammad Shahzad Cheema, Instructor - Machine Learning in AI

Dr. Muhammad Shahzad Cheema

Is Lead Data Scientist at IBM Watson IoT HQ Munich. He has 17-years of hands-on experience in implementing AI systems. He holds a Ph.D. in Large Scale Machine Learning from the University of Bonn, Germany and holds 3 M.Sc.’s in Mathematics, Computer Science and Robotics. He has led a number of AI and Big Data projects across industries. Some of his projects include: a first of a kind AI-based smart home with capabilities such as face recognition, behavior learning, edge analytics and life-long learning; a Reinforcement Learning based autonomous driving solution that enables a vehicle learn driving faster and smoother in real-world scenarios; optimization of world’s largest logistics network; a real-time bidding engine handling nearly a billion request per day, a cognitive cooking system that is able to learn recipes and physics of the devices. Over his career, Dr. Cheema has worked in startup, corporate, technology, research, and academia. As an AI Evangelist, he is an advocate of business- and solution-oriented data science. He has developed and mentored several Data Science teams at large tech and corporate organizations. He is also leading the Data Science Initiative that aims at AI resource development by enabling Data Science enthusiasts through focused training.

6 hours of content geared towards explaining Machine Learning in AI

  • 6 hours of video content explaining the use of machine learning in AI.
  • Learn how to suggest most suitable ML techniques in a suitable scenario.
  • Instructor has a Ph.D. in Machine Learning and three Masters Degrees, with 15 years of experience in the emerging technology space.

Duration: 6h

Course Description

This course will explain the machine learning landscape and its utilization in AI. At the end of the course, students will be able to suggest most suitable ML techniques in a suitable scenario; design, implement, and validate common ML algorithms.

What am I going to get from this course?

At the end of the course the participants shall be able to:
  1. Show an understanding of machine learning landscape and its utilization in AI
  2. Suggest most suitable ML techniques in a suitable scenario
  3. Design, implement, and validate common ML algorithms

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Basic understanding of:
  1. Linear Algebra
  2. Probability
  3. Statistics 
  4. Python programming

Who should take this course? Who should not?

This course is designed for:
  1. Students in the areas related to IT/Business Analytics
  2. Early stage data scientists
  3. Professionals related to AI/Machine Learning
  4. Researchers who want to apply machine learning in their analysis


Module 1: Basics of Machine Learning

Lecture 1 What is Machine Learning
Lecture 2 Types of Machine Learning

Module 2: Machine Learning Process

Lecture 3 Machine Learning Process - 1
Lecture 4 Machine Learning Process - 2

Module 3: Supervised Machine Learning ( Classification Algorithms)

Lecture 5 Classification
Lecture 6 Decision Tree
Lecture 7 Naive Bayes Classifier
Lecture 8 Classification Algorithms
Lecture 9 Artificial Neural Networks
Lecture 10 Ensemble Classifiers

Module 4: Supervised Learning (Regression Algorithms)

Lecture 11 Classification vs. Regression
Lecture 12 Challenges for Linear Regression

Module 5: Clustering Algorithms

Lecture 13 Clustering Algorithms
Lecture 14 Clustering - Evaluation Techniques

Module 6: Dimensionality Reduction and Feature Selection

Lecture 15 Motivation
Lecture 16 Dimensionality reduction - Practice
Lecture 17 Feature Selection
Lecture 18 Feature Selection - Practice

Module 7: Reinforcement Learning

Lecture 19 Introduction
Lecture 20 Q-Learning

Module 8: Natural Language Processing

Lecture 21 Introduction
Lecture 22 NLP - Introduction - Practice
Lecture 23 Feature Engineering in NLP
Lecture 24 NLP - Sentiment Analysis - Practice

Module 9: Visual Recognition and Deep Learning

Lecture 25 Introduction
Lecture 26 Deep Learning for Visual Recognition