This course is the perfect place for beginners to understand the core idea of building systems that have the ability to automatically learn from data and improve the experience without being explicitly programmed. In this course, you will learn about concepts of Machine Learning, effective machine learning techniques, and gain practice implementing them and getting them to work for yourself all in a classroom program.
The course will be mentored & guided by Industry experts having hands-on experience in ML-based industry projects. The course includes 1 major & 2 minor projects based on real-world applications with guided lab sessions.
It will be an online live (Live Stream) class, so you can attend this class from any geographical location. It will be an interactive live session, where you can ask your doubts to the instructor (similar to offline classroom program).
Classes will be scheduled for Weekends - Saturday & Sunday
Class timing would be 4 PM - 7 PM (IST)
Pre-requisites:
Should know the basics of any one programming language: C / C++, Java or Python
Recommended for:
Anyone who wants to learn and build ML-based solutions specifically
What is Machine Learning
Type of problems solved by ML
Real-World Scenario where ML is applicable
Software Installation
Brief Discussion about Libraries and application
Hands-on Python
Basic Syntax and data structures
Knowledge about NumPy
Learning Data Handling in Pandas
Understanding different Graphs in Matplotlib
Understanding of -
Categorical Data
Data Scaling
Data Splitting
Handling Missing Data
A-Z Mathematical Explanation to LR
Model
Forward Propagation
Cost Function
Gradient Descent
Training
Linear Regression Scratch Code
Linear Regression using library
Multiple Linear Regression
Polynomial Regression
Evaluation Metrics
Decision Tree Regression
Random Forest Regression
MS in Data Science @ Monash University, Australia | ML Engineer @ GeeksforGeeks
Grad Student at Monash University, Australia with subjects focused on Machine Learning, Natural Language Processing, Data Science, Big Data. He is actively working in research and has published research papers in the field of neural networks, forecasting, and image processing. He has experience of being a technical coordinator in Google Developers Group. He has also worked as a research intern at the Indian Institute of Technology (IIT Jammu). He has been working on ML ( as an intern + Full-time) for the last 3.5 years.
Batch | Date | Type | Register |
---|---|---|---|
MLPL 6 | 03 April '21 to 09 May '21 | Live Classes | Registration Closed |