Machine Learning: From Theory to Practice

This course has been updated on 15-08-2024 with the latest ML algorithms and best practices

Master supervised and unsupervised learning algorithms with hands-on projects. Learn to build, train, and evaluate machine learning models using industry-standard libraries. This intermediate course bridges the gap between theoretical concepts and practical implementation, guided by an ML expert with 12+ years of experience at top tech companies.

4.8 (298 Verified ratings)
8,230 Enrolled Learners
Last Updated: Nov 10, 2024 2:15 PM
English
Instructor
Created by:
Sri Sreedhar
Course Preview
₹2,500
This course includes:
  • 22h:30m:45s on-demand videos
  • 156 Lectures
  • 15 Exercises
  • 18 Quizzes
  • Access on any Device
  • Certificate of completion

What you'll learn

Understand supervised and unsupervised learning
Master classification and regression algorithms
Work with Scikit-learn for model building
Evaluate and optimize ML models
Handle feature engineering and selection
Complete 4 real-world ML projects

Requirements

  • Basic Python programming knowledge
  • Familiarity with NumPy and Pandas
  • Understanding of basic statistics

Description

Machine Learning is transforming industries and creating unprecedented demand for skilled professionals. This comprehensive course takes you from understanding core ML concepts to building production-ready models.

Throughout this course, you'll work on four real-world projects that demonstrate the practical application of machine learning. From predicting customer churn to classifying medical images, each project builds on your knowledge and provides hands-on experience with industry tools and techniques.

You'll learn not just how to implement algorithms, but also how to think about machine learning problems, approach data preprocessing, and deploy models to production. The course emphasizes best practices and common pitfalls to avoid.

Key Features:
  • Complete coverage of supervised and unsupervised learning
  • Four end-to-end real-world projects
  • Model evaluation and hyperparameter tuning
  • Advanced feature engineering techniques
  • Lifetime access to course materials
  • Certificate of completion

Course Content

12 sections • 156 lectures • 22h 30m total length

  • What is Machine Learning?
    18:20
  • Types of Learning
    28:15
  • Train-Test Split and Validation
    32:10
  • Practice Exercise 1
    Exercise

Master linear regression, polynomial regression, and other regression techniques.

Learn classification algorithms including logistic regression, decision trees, and ensemble methods.

Explore clustering and dimensionality reduction techniques.

Apply your knowledge to solve real business problems with actual datasets.

Instructor

Instructor

Sri Sreedhar

ML Engineer | Ex-Google, Meta

Sri has over 12 years of experience building machine learning systems at scale. She has led ML initiatives at top tech companies including Google and Meta, working on recommendation systems, NLP, and computer vision. She is passionate about democratizing machine learning education and has mentored 500+ students.

Experience 12+ Years
Students Taught 8,000+
Course Rating
4.8
Courses 4 Courses

Student Reviews

4.8

298 reviews

5 star
82%
4 star
15%
3 star
2%
2 star
1%
1 star
0%
Reviewer
Alex Kumar
1 week ago

Sri's approach to teaching ML is fantastic! The projects helped me land an ML internship. Highly recommended for anyone serious about ML.

Reviewer
Priya Sharma
2 weeks ago

Clear explanations, good balance of theory and practice. The real projects make it worth every penny!

Reviewer
David Martinez
3 weeks ago

Best ML course I've taken! The practical projects and industry insights are invaluable.

Course Features
  • Duration 22h 30m
  • Level Intermediate
  • Language English
  • Certificate Yes
  • Enrolled 8,230