Master Program In Machine Learning & Artificial Intelligence

Advanced study in ML/AI: algorithms, deep learning, NLP, ethics, big data, hands-on projects, and research opportunities

What you'll learn

Foundations of AI & ML

Machine Learning Algorithms

Data Analytics

Business Analysis

Data Science with R

SQL Database

Data Science with Python

Deep Learning

Natural Language Processing

Data Visualization Using Tableau

Big Data

Requirements

Fresh Graduates/ Diploma in any discipline or equivalent with minimum 1 Year of working Professionals

Description

The Master’s program in Machine Learning and Artificial Intelligence offers an extensive curriculum designed to provide students with both theoretical knowledge and practical skills in these rapidly evolving fields. The program begins with foundational coursework in mathematics and programming, covering essential topics such as linear algebra, calculus, probability, and advanced programming techniques. Core courses delve into machine learning algorithms, including supervised and unsupervised learning methods, neural networks, and deep learning techniques.Students explore natural language processing (NLP) to understand and develop systems for text and speech analysis, and computer vision for interpreting visual data. The program also addresses the ethical implications of AI, focusing on issues like fairness, bias, and societal impact.Hands-on experience is a key component, with opportunities for practical projects, internships, and a research thesis or capstone project that allows students to apply their knowledge to real-world challenges. With a focus on both theoretical understanding and practical application, graduates are prepared for careers in data science, AI research, and machine learning engineering. The program typically spans 1 to 2 years and offers  online study options, with some programs available online from IIBM Institute.Advanced study in ML/AI: algorithms, deep learning, NLP, ethics, big data, hands-on projects, and research opportunities.

Overview

Section 1: Introduction

Lecture 1 Application of Machine Learning

Lecture 2 Session-2 Supervised Learning In ML

Lecture 3 Session-3 Logistics Regression Algorithm

Lecture 4 Session-4 Feature Engineering

Lecture 5 Session-5 Feature Engineering

Lecture 6 Session-6 Feature of Transformation

Lecture 7 Session-7 Outlier Detection

Lecture 8 Session-8 Outlier Detection

Lecture 9 Session-9 Statistics For Machine Learning

Lecture 10 Session-10 Statistics For Machine Learning

Lecture 11 Session-11 Statistics For Machine Learning

Lecture 12 Session-12 Machine Learning Project

Lecture 13 Session-13 Natural Language Processing

Lecture 14 Session-14 Stemming and lemmatization in NLP

Lecture 15 Session-15 Lemmatization in NLP

Lecture 16 Session-16 MySQL

Lecture 17 What is AI

Lecture 18 Deep Learning

Lecture 19 Doubt Session

Lecture 20 Convolutional Neural Network

Aspiring Data Scientists and AI Specialists,Individuals with a Passion for AI and machine learning,Financial Analysts,Researchers,Entrepreneurs and Innovators,Recent Graduates