Udemy - Generative Ai And Machine Learning With Python

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 Highlightsractical 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
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