Machine Learning

Description

The Machine Learning course is designed to introduce students to the core concepts and techniques used to build intelligent systems that can learn from data. It covers key topics such as supervised and unsupervised learning, model evaluation, regression, classification, clustering, and neural networks. Learners also gain hands-on experience with popular tools and frameworks like Python and NLP applying their knowledge through real-world projects and datasets.

This course is ideal for individuals with a basic understanding of programming and mathematics who want to dive into the world of artificial intelligence. It bridges theory and practice, helping students understand how machine learning models work and how to deploy them in real-life applications. By the end of the course, learners will be capable of designing, training, and evaluating machine learning models, preparing them for roles such as Machine Learning Engineer, Data Scientist, or AI Specialist.

Level

All Levels
Last Updated

06/03/2025
Certificate

Certificate of completion

Demo Lecture

Course Curriculum

COURSE INSTRUCTION

Read Before You Start

LETS GET STARTED

Module 1 – Introduction to Machine Learning
1.1 – Definition and basic concepts
1.2 – Types supervised, unsupervised, and reinforcement
Module 2 – Mathematics for Machine Learning
2.1 – Linear algebra
2.2 – Calculus
2.3 – Probability and statistics
Module 3 – Python Programming:
Python Introduction
Python Basics
Module 4 – Libraries and Data Processing
NumPy
Pandas
Matpotlib
Seaborn
Module 5 – Supervised Learning and Module 6 – Model Evaluation and Validation
Module 7 – Unsupervised Learning
Module 8 – Feature Engineering
Module 9 – Advanced Regression Techniques
Module 10 – Natural Language Processing (NLP)
NLP Part – 1
NLP Part – 2
Module 11 – Deep Learning Fundamentals
Module 12 – Convolutional Neural Networks (CNNs)
Module 13 – Recurrent Neural Networks (RNNs)
Module 14 – Generative Adversarial Networks (GANs):
Module 15 – Deployment and Model Optimization
Module 16 – Special Topics in Machine Learning
Module 17 – How to use Kaggle Platform

PROJECT

MOCK INTERVIEW QNA

PROJECT SUBMISSION (OPTIONAL)

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Level

All Levels
Last Updated

06/03/2025
Certificate

Certificate of completion

Demo Lecture

Course Instructor

Harshith Sai

Course Central

Machine Learning

Description

Demo Lecture

Course Curriculum

COURSE INSTRUCTION

Read Before You Start

LETS GET STARTED

Module 1 – Introduction to Machine Learning

1.1 – Definition and basic concepts

1.2 – Types supervised, unsupervised, and reinforcement

Module 2 – Mathematics for Machine Learning

2.1 – Linear algebra

2.2 – Calculus

2.3 – Probability and statistics

Module 3 – Python Programming:

Python Introduction

Python Basics

Module 4 – Libraries and Data Processing

NumPy

Pandas

Matpotlib

Seaborn

Module 5 – Supervised Learning and Module 6 – Model Evaluation and Validation

Module 7 – Unsupervised Learning

Module 8 – Feature Engineering

Module 9 – Advanced Regression Techniques

Module 10 – Natural Language Processing (NLP)

NLP Part – 1

NLP Part – 2

Module 11 – Deep Learning Fundamentals

Module 12 – Convolutional Neural Networks (CNNs)

Module 13 – Recurrent Neural Networks (RNNs)

Module 14 – Generative Adversarial Networks (GANs):

Module 15 – Deployment and Model Optimization

Module 16 – Special Topics in Machine Learning

Module 17 – How to use Kaggle Platform

PROJECT

Project – Spam Classifier

Bonus Project – Sentiment Analysis

MOCK INTERVIEW QNA

Machine Learning Mock Interview Questions and Answers

PROJECT SUBMISSION (OPTIONAL)

Project Submission

Certificate you will get

Demo Lecture

Tags

Course Instructor

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