Unlock the Power of Machine Learning and Generative AI

What you'll learn

Implement and evaluate machine learning models in Python.

Apply dimensionality reduction and clustering techniques.

Understand and explain core generative AI models.

Build and train Artificial Neural Networks (ANNs) and Multi-Layer Perceptrons (MLPs) using Keras.

Requirements

Basic Programming in Python

Description

Unlock the Power of Machine Learning and Generative AIThis comprehensive course provides a deep dive into the core concepts and practical applications of machine learning and generative AI. Starting with foundational principles like supervised, unsupervised, and reinforcement learning, you'll progress through data preprocessing, evaluation metrics, and essential algorithms like linear and logistic regression, decision trees, and random forests.Dive into unsupervised learning with K-means clustering and Principal Component Analysis (PCA), mastering dimensionality reduction. Transition to deep learning with Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs) using Keras.Finally, explore the cutting edge of generative AI, including Transformer attention mechanisms, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Recurrent Neural Networks (RNNs), and Gated Recurrent Units (GRUs).Course Highlights:Practical Labs: Hands-on experience coding in Python, solidifying your understanding of key algorithms.Comprehensive Coverage: From fundamental machine learning to advanced generative AI techniques.Detailed Evaluation: Learn to assess model performance with various metrics and confusion matrices.Deep Learning Mastery: Implement and train neural networks using Keras.Generative AI Exploration: Demystify Transformers, GANs, VAEs, and RNNs.Regular Quizzes: Reinforce learning with quizzes after each module.This course is designed for anyone seeking a robust understanding of machine learning and generative AI, from beginners to those looking to expand their knowledge.

Overview

Section 1: Introduction

Lecture 1 Introduction Lecture

Lecture 2 Supervised Learning LAB

Lecture 3 Unsupervised Learning LAB

Lecture 4 Data Preprocessing

Lecture 5 Evaluation Metrics - Accuracy, Precision, Recall, F1-Score

Lecture 6 Evaluation Metrics - Confusion Matrix

Section 2: Module 2 Supervised Learning

Lecture 7 Linear Regression

Lecture 8 Logistic Regression

Lecture 9 Decision Trees

Lecture 10 Random Forest

Section 3: Module 3 Unsupervised Learning

Lecture 11 K Means Clustering

Lecture 12 K Means Clustering Python Code

Lecture 13 Principal Component Analysis (PCA)

Section 4: Module 4 Deep Learning

Lecture 14 Introduction to Deep Learning and Artificial Neural Network (ANN)

Lecture 15 Coding ANN in Python

Lecture 16 The Perceptron

Lecture 17 Convolutional Neural Networks

Lecture 18 Coding a CNN

Lecture 19 Implementing MLP with Keras Part 1

Lecture 20 Implementing MLP with Keras Part 2

Lecture 21 Implementing MLP with Keras Part 3

Section 5: Module 5 Generative AI

Lecture 22 Transformer's Attention Mechanism

Lecture 23 Understanding Transformers

Lecture 24 Understanding the Generative Adversarial Networks (GANs)

Lecture 25 Understanding Variational Autoencoders (VAEs)

Lecture 26 Recurrent Nerual Networks

Lecture 27 Gated Recurrent Units (GRUs)

Anyone interested in AI and Machine Learning