Certification Course on Machine Learning

About the Course

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, data mining, 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). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Course Objective:

  • To introduce students to the basic concepts and techniques of Machine Learning.
  • To develop skills of using recent machine learning software for solving practical problems.
  • To gain experience of doing independent study and research.

Course Outcome:

Upon successful completion of this course, students will be able to

  1. Gain knowledge about basic concepts of Machine Learning
  2. Identify machine learning techniques suitable for a given problem
  3. Solve the problems using various machine learning techniques
  4. Design application using machine learning techniques.

Course Duration: 3 Months

Course Content

S.NoCourse Content
1Introduction
2Linear Regression with One Variable
3Linear algebra Review
4Linear Regression with Multiple Variables
5Running the program in Octave / MATLAB
6Logistic Regression
7Regularization
8Neural Networks
9Advice for Applying Machine Learning
10Machine Learning System Design
11Support Vector machines
12Unsupervised Learning
13Dimensionality Reduction
14Anomaly Detection
15Recommender systems
16Large Scale Machine Learning
17Application Example: Photo OCR
18Practice Exercises
19Online Certification