Artificial Intelligence & Machine Learning Introductory Program
Foundation Course - Designed for beginners and enthusiasts
IT and Software ,Other IT and Software,Python
Lectures -8
Duration -18 hours
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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.

Curriculum
Check out the detailed breakdown of what’s inside the course
AIML - Foundation - Introduction
1 Lectures
-
AI and ML and DataScience Introduction with tools and Models 02:11:15 02:11:15
Supervised Learning & Unsupervised Learning
1 Lectures

Python Basics and Programming
1 Lectures

Data Pre-Processing and Machine Learning
1 Lectures

Deep Learning Fundamentals
1 Lectures

Neural Networks and Generative AI Introduction
1 Lectures

Large Language Models and NLP, Github Account setup
1 Lectures

CNN, Transformers and Gen AI
1 Lectures

Instructor Details

Phani Avagaddi
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