ZERO to HERO: AI & ML Starter Course with Hands-On Projects

COURSE AUTHOR –
Prathamesh Khedekar

Last Updated on May 20, 2023 by GeeksGod

Hi there!

This course aims to provide you with an overview of Artificial Intelligence (AI) & Machine Learning (ML) using simplified explanations and hands-on projects.

We will be covering the following topics in this course:

Introduction to Artificial Intelligence (AI) & Neural NetworksDifference between Artificial Intelligence, Machine Learning & Deep LearningStandardized Architecture for AI & ML SystemsMachine Learning (ML) Algorithms – Supervised vs Unsupervised vs Reinforcement LearningDay in a life of an AI & ML EngineerSkills you will need for an AI & ML Engineer roleMethods to evaluate the performance of Machine Learning ModelsConfusion Matrix, Accuracy, Precision, RecallEpoch, Learning Rate, Batch SizeHands-on AI & ML Projects with Open-Source ToolsSummary

This is the first version of this course and it will be updated as we continue to witness the evolution of Artificial Intelligence and Machine Learning. My goal is to ensure people from all over the world are able to access this course and are able to learn the fundamentals of AI & Machine Learning and apply the same in their journey.

You will benefit from this course if:

You want to learn the basics of AI & Machine Learning and you are looking for beginner-friendly hands-on exposureYou are contemplating switching your career to Artificial Intelligence & Machine LearningYou have a genuine interest in improving your understanding of AI & Machine LearningYou want to learn the standardized framework used to build and evaluate AI/ML ModelsYou are building a new startup and need to solidify your understanding of AI & ML concepts

Hope you will enjoy this course!

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What you will learn :

1. Basics of Human Brain & Artificial Neural Network – Biological Neurons & Artificial Neurons
2. Difference between Artificial Intelligence, Machine Learning & Deep Learning
3. 3 Machine Learning Techniques – Supervised Learning, Unsupervised Learning, Reinforcement Learning with examples
4. Learn how to train, evaluate and optimize a Machine Learning model
5. Day in a life of an AI/ML Engineer
6. ML Model Evaluation Method – Confusion Matrix, Error, Recall, Precision, Accuracy
7. ML Model Optimization Method – Learning Rate, Epoch, Batch Size
8. ML Model Training – Using Open-Source Tools
9. Hands-On Project 1 – Build a Machine Learning model using Healthcare Dataset
10. Hands-On Project 2 – Build a Machine Learning model using Agriculture Dataset