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:
- Show an understanding of machine learning landscape and its utilization in AI
- Suggest most suitable ML techniques in a suitable scenario
- 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:
- Linear Algebra
- Probability
- Statistics
- Python programming
Who should take this course? Who should not?
This course is designed for:
- Students in the areas related to IT/Business Analytics
- Early stage data scientists
- Professionals related to AI/Machine Learning
- Researchers who want to apply machine learning in their analysis
Curriculum
Module 1: Basics of Machine Learning
Lecture 2
Introduction to the Course
Lecture 3
What is Machine Learning
Lecture 4
Machine Learning Types
Module 2: Machine Learning Process
Lecture 5
Machine Learning Process - 1
Lecture 6
Machine Learning Process - 2
Module 3: Supervised Machine Learning ( Classification Algorithms)
Lecture 9
Naive Bayes Classifier
Lecture 10
Classification Algorithms
Lecture 11
Artificial Neural Networks
Lecture 12
Ensemble Classifiers
Module 4: Supervised Learning (Regression Algorithms)
Lecture 13
Classification vs. Regression
Lecture 14
Challenges for Linear Regression
Module 5: Clustering Algorithms
Lecture 15
Clustering Algorithms
Lecture 16
Clustering - Evaluation Techniques
Module 6: Dimensionality Reduction and Feature Selection
Lecture 18
Dimensionality reduction - Practice
Lecture 19
Feature Selection
Lecture 20
Feature Selection - Practice
Module 7: Reinforcement Learning
Module 8: Natural Language Processing
Lecture 24
NLP - Introduction - Practice
Lecture 25
Feature Engineering in NLP
Lecture 26
NLP - Sentiment Analysis - Practice
Module 9: Visual Recognition and Deep Learning
Lecture 28
Deep Learning for Visual Recognition