Machine Learning, Data Science and Generative AI with Python

سرفصل های دوره

Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks

01 - Getting Started

001 Introduction

002 Udemy 101 Getting the Most From This Course

003 Important note.html

004 Installation Getting Started.html

005 [Activity] WINDOWS Installing and Using Anaconda & Course Materials

006 [Activity] MAC Installing and Using Anaconda & Course Materials

007 [Activity] LINUX Installing and Using Anaconda & Course Materials

008 Python Basics, Part 1 [Optional]

009 [Activity] Python Basics, Part 2 [Optional]

010 [Activity] Python Basics, Part 3 [Optional]

011 [Activity] Python Basics, Part 4 [Optional]

012 Introducing the Pandas Library [Optional]

02 - Statistics and Probability Refresher, and Python Practice

001 Types of Data (Numerical, Categorical, Ordinal)

002 Mean, Median, Mode

003 [Activity] Using mean, median, and mode in Python

004 [Activity] Variation and Standard Deviation

005 Probability Density Function; Probability Mass Function

006 Common Data Distributions (Normal, Binomial, Poisson, etc)

007 [Activity] Percentiles and Moments

008 [Activity] A Crash Course in matplotlib

009 [Activity] Advanced Visualization with Seaborn

010 [Activity] Covariance and Correlation

011 [Exercise] Conditional Probability

012 Exercise Solution Conditional Probability of Purchase by Age

013 Bayes Theorem

03 - Predictive Models

001 [Activity] Linear Regression

002 [Activity] Polynomial Regression

003 [Activity] Multiple Regression, and Predicting Car Prices

004 Multi-Level Models

04 - Machine Learning with Python

001 Supervised vs. Unsupervised Learning, and TrainTest

002 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression

003 Bayesian Methods Concepts

004 [Activity] Implementing a Spam Classifier with Naive Bayes

005 K-Means Clustering

006 [Activity] Clustering people based on income and age

007 Measuring Entropy

008 [Activity] WINDOWS Installing Graphviz

009 [Activity] MAC Installing Graphviz

010 [Activity] LINUX Installing Graphviz

011 Decision Trees Concepts

012 [Activity] Decision Trees Predicting Hiring Decisions

013 Ensemble Learning

014 [Activity] XGBoost

015 Support Vector Machines (SVM) Overview

016 [Activity] Using SVM to cluster people using scikit-learn

05 - Recommender Systems

001 User-Based Collaborative Filtering

002 Item-Based Collaborative Filtering

003 [Activity] Finding Movie Similarities using Cosine Similarity

004 [Activity] Improving the Results of Movie Similarities

005 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering

006 [Exercise] Improve the recommenders results

06 - More Data Mining and Machine Learning Techniques

001 K-Nearest-Neighbors Concepts

002 [Activity] Using KNN to predict a rating for a movie

003 Dimensionality Reduction; Principal Component Analysis (PCA)

004 [Activity] PCA Example with the Iris data set

005 Data Warehousing Overview ETL and ELT

006 Reinforcement Learning

007 [Activity] Reinforcement Learning & Q-Learning with Gym

008 Understanding a Confusion Matrix

009 Measuring Classifiers (Precision, Recall, F1, ROC, AUC)

external-links.txt

07 - Dealing with Real-World Data

001 BiasVariance Tradeoff

002 [Activity] K-Fold Cross-Validation to avoid overfitting

003 Data Cleaning and Normalization

004 [Activity] Cleaning web log data

005 Normalizing numerical data

006 [Activity] Detecting outliers

007 Feature Engineering and the Curse of Dimensionality

008 Imputation Techniques for Missing Data

009 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE

010 Binning, Transforming, Encoding, Scaling, and Shuffling

08 - Apache Spark Machine Learning on Big Data

001 Warning about Java 21+ and Spark 3!.html

002 Spark installation notes for MacOS and Linux users.html

003 [Activity] Installing Spark

004 Spark Introduction

005 Spark and the Resilient Distributed Dataset (RDD)

006 Introducing MLLib

007 Introduction to Decision Trees in Spark

008 [Activity] K-Means Clustering in Spark

009 TF IDF

010 [Activity] Searching Wikipedia with Spark

011 [Activity] Using the Spark DataFrame API for MLLib

09 - Experimental Design ML in the Real World

001 Deploying Models to Real-Time Systems

002 AB Testing Concepts

003 T-Tests and P-Values

004 [Activity] Hands-on With T-Tests

005 Determining How Long to Run an Experiment

006 AB Test Gotchas

10 - Deep Learning and Neural Networks

001 Deep Learning Pre-Requisites

002 The History of Artificial Neural Networks

003 [Activity] Deep Learning in the Tensorflow Playground

004 Deep Learning Details

005 Introducing Tensorflow

006 [Activity] Using Tensorflow, Part 1

007 [Activity] Using Tensorflow, Part 2

008 [Activity] Introducing Keras

009 [Activity] Using Keras to Predict Political Affiliations

010 Convolutional Neural Networks (CNNs)

011 [Activity] Using CNNs for handwriting recognition

012 Recurrent Neural Networks (RNNs)

013 [Activity] Using a RNN for sentiment analysis

014 [Activity] Transfer Learning

015 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters

016 Deep Learning Regularization with Dropout and Early Stopping

017 The Ethics of Deep Learning

11 - Generative Models

001 Variational Auto-Encoders (VAEs) - how they work

002 Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST

002 variationalautoencoders.zip

003 Generative Adversarial Networks (GANs) - How they work

004 Generative Adversarial Networks (GANs) - Playing with some demos

005 Generative Adversarial Networks (GANs) - Hands-on with Fashion MNIST

005 gan-on-fashion-mnist.zip

006 Learning More about Deep Learning

12 - Generative AI GPT, ChatGPT, Transformers, Self Attention Based Neural Networks

001 The Transformer Architecture (encoders, decoders, and self-attention.)

002 Self-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth

003 Applications of Transformers (GPT)

004 How GPT Works, Part 1 The GPT Transformer Architecture

005 How GPT Works, Part 2 Tokenization, Positional Encoding, Embedding

006 Fine Tuning Transfer Learning with Transformers

007 transformers-mlcourse.zip

007 [Activity] Tokenization with Google CoLab and HuggingFace

008 [Activity] Positional Encoding

009 [Activity] Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT

010 [Activity] Using small and large GPT models within Google CoLab and HuggingFace

011 [Activity] Fine Tuning GPT with the IMDb dataset

012 From GPT to ChatGPT Deep Reinforcement Learning, Proximal Policy Gradients

013 From GPT to ChatGPT Reinforcement Learning from Human Feedback and Moderation

13 - The OpenAI API (Developing with GPT and ChatGPT)

001 chat-completions.zip

001 [Activity] The OpenAI Chat Completions API

002 functions.zip

002 [Activity] Using Tools and Functions in the OpenAI Chat Completion API

003 image.zip

003 [Activity] The Images (DALL-E) API in OpenAI

004 embedding.zip

004 [Activity] The Embeddings API in OpenAI Finding similarities between words

005 The Legacy Fine-Tuning API for GPT Models in OpenAI

006 extract-script.zip

006 [Demo] Fine-Tuning OpenAIs Davinci Model to simulate Data from Star Trek

007 The New OpenAI Fine-Tuning API; Fine-Tuning GPT-3.5 to simulate Commander Data!

007 makingdata.zip

008 moderation.zip

008 [Activity] The OpenAI Moderation API

009 audio.zip

009 [Activity] The OpenAI Audio API (speech to text)

14 - Retrieval Augmented Generation (RAG)

001 Retrieval Augmented Generation (RAG) How it works, with some examples

002 Demo Using Retrieval Augmented Generation (RAG) to simulate Data from Star Trek

002 data-rag.zip

15 - Final Project

001 Your final project assignment Mammogram Classification

002 Final project review

16 - You made it!

001 More to Explore

002 Dont Forget to Leave a Rating!.html

003 Bonus Lecture.html

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تولید کننده: Udemy