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Master Program In Machine Learning & Artificial Intelligence
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 18.92 GB | Duration: 28h 14m
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
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