Machine Learning, Data Science and Generative AI with Python [Video]
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Free ChapterGetting Started
- Introduction
- [Activity] Windows: Installing and Using Anaconda and Course Materials
- [Activity] MAC: Installing and Using Anaconda and Course Materials
- [Activity] Linux: Installing and Using Anaconda and Course Materials
- Python Basics, Part 1 [Optional]
- [Activity] Python Basics, Part 2 [Optional]
- [Activity] Python Basics, Part 3 [Optional]
- [Activity] Python Basics, Part 4 [Optional]
- Introducing the Pandas Library [Optional]
-
Statistics and Probability Refresher, and Python Practice
- Types of Data (Numerical, Categorical, Ordinal)
- Mean, Median, Mode
- [Activity] Using Mean, Median, and Mode in Python
- [Activity] Variation and Standard Deviation
- Probability Density Function; Probability Mass Function
- Common Data Distributions (Normal, Binomial, Poisson, and So On)
- [Activity] Percentiles and Moments
- [Activity] A Crash Course in matplotlib
- [Activity] Advanced Visualization with Seaborn
- [Activity] Covariance and Correlation
- [Exercise] Conditional Probability
- Exercise Solution: Conditional Probability of Purchase by Age
- Bayes' Theorem
-
Predictive Models
-
Machine Learning with Python
- Supervised Versus Unsupervised Learning, and Train/Test
- [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
- Bayesian Methods: Concepts
- [Activity] Implementing a Spam Classifier with Naive Bayes
- K-Means Clustering
- [Activity] Clustering People Based on Income and Age
- Measuring Entropy
- [Activity] Windows: Installing GraphViz
- [Activity] MAC: Installing GraphViz
- [Activity] Linux: Installing GraphViz
- Decision Trees: Concepts
- [Activity] Decision Trees: Predicting Hiring Decisions
- Ensemble Learning
- [Activity] XGBoost
- Support Vector Machines (SVM) Overview
- [Activity] Using SVM to Cluster People Using Scikit-Learn
-
Recommender Systems
- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
- [Activity] Finding Movie Similarities Using Cosine Similarity
- [Activity] Improving the Results of Movie Similarities
- [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering
- [Exercise] Improve the Recommender's Results
-
More Data Mining and Machine Learning Techniques
- K-Nearest-Neighbors: Concepts
- [Activity] Using KNN to Predict a Rating for a Movie
- Dimensionality Reduction; Principal Component Analysis (PCA)
- [Activity] PCA Example with the Iris Dataset
- Data Warehousing Overview: ETL and ELT
- Reinforcement Learning
- [Activity] Reinforcement Learning and Q-Learning with Gym
- Understanding a Confusion Matrix
- Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
-
Dealing with Real-World Data
- Bias/Variance Tradeoff
- [Activity] K-Fold Cross-Validation to Avoid Overfitting
- Data Cleaning and Normalization
- [Activity] Cleaning Web Log Data
- Normalizing Numerical Data
- [Activity] Detecting Outliers
- Feature Engineering and the Curse of Dimensionality
- Imputation Techniques for Missing Data
- Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
- Binning, Transforming, Encoding, Scaling, and Shuffling
-
Apache Spark: Machine Learning on Big Data
- [Activity] Installing Spark - Part 1
- [Activity] Installing Spark - Part 2
- Spark Introduction
- Spark and the Resilient Distributed Dataset (RDD)
- Introducing MLLib
- Introduction to Decision Trees in Spark
- [Activity] K-Means Clustering in Spark
- TF / IDF
- [Activity] Searching Wikipedia with Spark
- [Activity] Using the Spark DataFrame API for MLLib
-
Experimental Design / ML in the Real World
-
Deep Learning and Neural Networks
- Deep Learning Prerequisites
- The History of Artificial Neural Networks
- [Activity] Deep Learning in the TensorFlow Playground
- Deep Learning Details
- Introducing TensorFlow
- [Activity] Using TensorFlow, Part 1
- [Activity] Using TensorFlow, Part 2
- [Activity] Introducing Keras
- [Activity] Using Keras to Predict Political Affiliations
- Convolutional Neural Networks (CNNs)
- [Activity] Using CNNs for Handwriting Recognition
- Recurrent Neural Networks (RNNs)
- [Activity] Using a RNN for Sentiment Analysis
- [Activity] Transfer Learning
- Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
- Deep Learning Regularization with Dropout and Early Stopping
- The Ethics of Deep Learning
-
Generative Models
- Variational Auto-Encoders (VAEs) - How They Work
- Variational Auto-Encoders (VAE) - Hands-On with Fashion MNIST
- Generative Adversarial Networks (GANs) - How They Work
- Generative Adversarial Networks (GANs) - Playing with Some Demos
- Generative Adversarial Networks (GANs) - Hands-On with Fashion MNIST
- Learning More about Deep Learning
-
Generative AI: GPT, ChatGPT, Transformers, Self-Attention Based Neural Networks
- The Transformer Architecture (encoders, decoders, and self-attention.)
- Self-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth
- Applications of Transformers (GPT)
- How GPT Works, Part 1: The GPT Transformer Architecture
- How GPT Works, Part 2: Tokenization, Positional Encoding, Embedding
- Fine Tuning / Transfer Learning with Transformers
- [Activity] Tokenization with Google CoLab and HuggingFace
- [Activity] Positional Encoding
- [Activity] Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT
- [Activity] Using small and large GPT models within Google CoLab and HuggingFace
- [Activity] Fine Tuning GPT with the IMDb dataset
- From GPT to ChatGPT: Deep Reinforcement Learning, Proximal Policy Gradients
- From GPT to ChatGPT: Reinforcement Learning from Human Feedback and Moderation
-
The OpenAI API (Developing with GPT and ChatGPT)
- [Activity] The OpenAI Chat Completions API
- [Activity] Using Functions in the OpenAI Chat Completion API
- [Activity] The Images (DALL-E) API in OpenAI
- [Activity] The Embeddings API in OpenAI: Finding similarities between words
- [Activity] The Completions API in OpenAI
- The Legacy Fine-Tuning API for GPT Models in OpenAI
- [Demo] Fine-Tuning OpenAI's Davinci Model to simulate Data from Star Trek
- The New OpenAI Fine-Tuning API; Fine-Tuning GPT-3.5 to simulate Commander Data!
- [Activity] The OpenAI Moderation API
- [Activity] The OpenAI Audio API (speech to text)
-
Final Project
-
You Made It!
About this
video
This course begins with a Python crash course and then guides you on setting up Microsoft Windows-based PCs, Linux desktops, and Macs. After the setup, we delve into machine learning, AI, and data mining techniques, which include deep learning and neural networks with TensorFlow and Keras; generative models with variational autoencoders and generative adversarial networks; data visualization in Python with Matplotlib and Seaborn; transfer learning, sentiment analysis, image recognition, and classification; regression analysis, K-Means Clustering, Principal Component Analysis, training/testing and cross-validation, Bayesian methods, decision trees, and random forests.
Additionally, we will cover multiple regression, multilevel models, support vector machines, reinforcement learning, collaborative filtering, K-Nearest Neighbors, the bias/variance tradeoff, ensemble learning, term frequency/inverse document frequency, experimental design, and A/B testing, feature engineering, hyperparameter tuning, and much more! There's a dedicated section on machine learning with Apache Spark to scale up these techniques to "big data" analyzed on a computing cluster.
The course will cover the Transformer architecture, delve into the role of self-attention in AI, explore GPT applications, and practice fine-tuning Transformers for tasks such as movie review analysis. Furthermore, we will look at integrating the OpenAI API for ChatGPT, creating with DALL-E, understanding embeddings, and leveraging audio-to-text to enhance AI with real-world data and moderation.
- Publication date:
- September 2016
- Publisher
- Packt
- Duration
- 18 hours 11 minutes
- ISBN
- 9781787127081
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(5 reviews total)