IIRF Online > Development > Data Science > Machine Learning > Machine Learning, Data Science and Generative AI with Python
Machine Learning, Data Science and Generative AI with Python by Udemy
Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks
Course Highlights
- Build artificial neural networks with Tensorflow and Keras
- Implement machine learning at massive scale with Apache Spark's MLLib
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values
Skills you will learn!
Curriculum
12 Topics
Introduction
Udemy 101: Getting the Most From This Course
Important note
Installation: Getting Started
[Activity] WINDOWS: Installing and Using Anaconda & Course Materials
[Activity] MAC: Installing and Using Anaconda & Course Materials
[Activity] LINUX: Installing and Using Anaconda & 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]
13 Topics
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 etc)
[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
4 Topics
[Activity] Linear Regression
[Activity] Polynomial Regression
[Activity] Multiple Regression and Predicting Car Prices
Multi-Level Models
16 Topics
Supervised vs. 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
6 Topics
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
9 Topics
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 data set
Data Warehousing Overview: ETL and ELT
Reinforcement Learning
[Activity] Reinforcement Learning & Q-Learning with Gym
Understanding a Confusion Matrix
Measuring Classifiers (Precision Recall F1 ROC AUC)
10 Topics
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
12 Topics
Warning about Java 11 and Spark 3!
Spark installation notes for MacOS and Linux users
[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
6 Topics
Deploying Models to Real-Time Systems
A/B Testing Concepts
T-Tests and P-Values
[Activity] Hands-on With T-Tests
Determining How Long to Run an Experiment
A/B Test Gotchas
17 Topics
Deep Learning Pre-Requisites
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 (CNN's)
[Activity] Using CNN's for handwriting recognition
Recurrent Neural Networks (RNN's)
[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
6 Topics
Variational Auto-Encoders (VAE's) - how they work
Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST
Generative Adversarial Networks (GAN's) - How they work
Generative Adversarial Networks (GAN's) - Playing with some demos
Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST
Learning More about Deep Learning
13 Topics
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
10 Topics
[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)
2 Topics
Your final project assignment: Mammogram Classification
Final project review
3 Topics
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Bonus Lecture
Machine Learning, Data Science and Generative AI with Python
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