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Artificial Intelligence & Machine Learning Introductory Program

person icon Phani Avagaddi

4.6

Artificial Intelligence & Machine Learning Introductory Program

Foundation Course - Designed for beginners and enthusiasts

updated on icon Updated on Jun, 2025

language icon Language - English

person icon Phani Avagaddi

category icon IT and Software ,Other IT and Software,Python

Lectures -8

Duration -18 hours

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4.6

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Course Description

Implement Machine Learning algorithms

How to improve your Machine Learning Models

Build a portfolio of work to have on your resume

Supervised and Unsupervised Learning

Explore large datasets using data visualization tools like Matplotlib 

Learn NumPy and how it is used in Machine Learning

Learn to use the popular library Scikit-learn in your projects

Learn to perform Classification and Regression modelling

Master Machine Learning and use it on the job

Learn which Machine Learning model to choose for each type of problem

Learn best practices when it comes to Data Science Workflow

Learn how to program in Python using the latest Python 3

Learn to pre process data, clean data, and analyze large data.

Developer Environment setup for Data Science and Machine Learning

A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided

Real life case studies and projects to understand how things are done in the real world

Guidance to choose your career path based on your background and build the path in next 6 months

Comprehensive understanding on foundational concepts like Neurons, Perceptron, Multilayer Perceptron, Transformers. 

Good overview on Convolution Neural Networks and Recurrent Neural Networks

Goals

  • An Insight and comprehensive overview of the concepts of AI and ML
  • Implement Machine Learning algorithms
  • How to improve your Machine Learning Models
  • Build a portfolio of work to have on your resume
  • Supervised and Unsupervised Learning
  • Explore large datasets using data visualization tools like Matplotlib 
  • Learn NumPy and how it is used in Machine Learning
  • Learn to use the popular library Scikit-learn in your projects
  • Learn to perform Classification and Regression modelling
  • Master Machine Learning and use it on the job
  • Learn which Machine Learning model to choose for each type of problem
  • Learn best practices when it comes to Data Science Workflow
  • Learn how to program in Python using the latest Python 3
  • Learn to pre process data, clean data, and analyze large data.
  • Developer Environment setup for Data Science and Machine Learning
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Real life case studies and projects to understand how things are done in the real world
  • Guidance to choose your career path based on your background and build the path in next 6 months
  • Comprehensive understanding on foundational concepts like Neurons, Perceptron, Multilayer Perceptron, Transformers. 
  • Good overview on Convolution Neural Networks and Recurrent Neural Networks

Prerequisites

Basic Python Programming, Basic Mathematical Concepts like Vector Algebra, Vector Calculus, Probability and Statistics. 

Non Programmers are also can learn these skills with some initial ground preparation.

Artificial Intelligence & Machine Learning Introductory Program

Curriculum

Check out the detailed breakdown of what’s inside the course

AIML - Foundation - Introduction

1 Lectures
  • play icon AI and ML and DataScience Introduction with tools and Models 02:11:15 02:11:15

Supervised Learning & Unsupervised Learning

1 Lectures
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Python Basics and Programming

1 Lectures
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Data Pre-Processing and Machine Learning

1 Lectures
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Deep Learning Fundamentals

1 Lectures
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Neural Networks and Generative AI Introduction

1 Lectures
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Large Language Models and NLP, Github Account setup

1 Lectures
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CNN, Transformers and Gen AI

1 Lectures
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Instructor Details

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Phani Avagaddi

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