Udemy Machine Learning Data Science and Generative AI with Python 2025

/01. Getting Started/

5. Activity WINDOWS Installing and Using Anaconda & Course Materials.mp4

6. Activity MAC Installing and Using Anaconda & Course Materials.mp4

7. Activity LINUX Installing and Using Anaconda & Course Materials.mp4

12. Introducing the Pandas Library Optional.mp4

8. Python Basics, Part 1 Optional.mp4

9. Activity Python Basics, Part 2 Optional.mp4

1. Introduction.mp4

2. Udemy 101 Getting the Most From This Course.mp4

11. Activity Python Basics, Part 4 Optional.mp4

10. Activity Python Basics, Part 3 Optional.mp4

12. Introducing the Pandas Library Optional.vtt

5. Activity WINDOWS Installing and Using Anaconda & Course Materials.vtt

7. Activity LINUX Installing and Using Anaconda & Course Materials.vtt

6. Activity MAC Installing and Using Anaconda & Course Materials.vtt

8. Python Basics, Part 1 Optional.vtt

9. Activity Python Basics, Part 2 Optional.vtt

11. Activity Python Basics, Part 4 Optional.vtt

1. Introduction.vtt

10. Activity Python Basics, Part 3 Optional.vtt

2. Udemy 101 Getting the Most From This Course.vtt

4. Installation Getting Started.html

3. Important note.html

/02. Statistics and Probability Refresher, and Python Practice/

4. Activity Variation and Standard Deviation.mp4

9. Activity Advanced Visualization with Seaborn.mp4

11. Exercise Conditional Probability.mp4

8. Activity A Crash Course in matplotlib.mp4

1. Types of Data (Numerical, Categorical, Ordinal).mp4

10. Activity Covariance and Correlation.mp4

13. Bayes' Theorem.mp4

3. Activity Using mean, median, and mode in Python.mp4

7. Activity Percentiles and Moments.mp4

6. Common Data Distributions (Normal, Binomial, Poisson, etc).mp4

2. Mean, Median, Mode.mp4

12. Exercise Solution Conditional Probability of Purchase by Age.mp4

5. Probability Density Function Probability Mass Function.mp4

9. Activity Advanced Visualization with Seaborn.vtt

11. Exercise Conditional Probability.vtt

7. Activity Percentiles and Moments.vtt

10. Activity Covariance and Correlation.vtt

4. Activity Variation and Standard Deviation.vtt

8. Activity A Crash Course in matplotlib.vtt

3. Activity Using mean, median, and mode in Python.vtt

6. Common Data Distributions (Normal, Binomial, Poisson, etc).vtt

1. Types of Data (Numerical, Categorical, Ordinal).vtt

2. Mean, Median, Mode.vtt

13. Bayes' Theorem.vtt

5. Probability Density Function Probability Mass Function.vtt

12. Exercise Solution Conditional Probability of Purchase by Age.vtt

/03. Predictive Models/

3. Activity Multiple Regression, and Predicting Car Prices.mp4

1. Activity Linear Regression.mp4

2. Activity Polynomial Regression.mp4

4. Multi-Level Models.mp4

3. Activity Multiple Regression, and Predicting Car Prices.vtt

1. Activity Linear Regression.vtt

2. Activity Polynomial Regression.vtt

4. Multi-Level Models.vtt

/04. Machine Learning with Python/

11. Decision Trees Concepts.mp4

4. Activity Implementing a Spam Classifier with Naive Bayes.mp4

14. Activity XGBoost.mp4

12. Activity Decision Trees Predicting Hiring Decisions.mp4

1. Supervised vs. Unsupervised Learning, and TrainTest.mp4

16. Activity Using SVM to cluster people using scikit-learn.mp4

13. Ensemble Learning.mp4

5. K-Means Clustering.mp4

6. Activity Clustering people based on income and age.mp4

2. Activity Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4

15. Support Vector Machines (SVM) Overview.mp4

7. Measuring Entropy.mp4

3. Bayesian Methods Concepts.mp4

9. Activity MAC Installing Graphviz.mp4

10. Activity LINUX Installing Graphviz.mp4

8. Activity WINDOWS Installing Graphviz.mp4

14. Activity XGBoost.vtt

12. Activity Decision Trees Predicting Hiring Decisions.vtt

16. Activity Using SVM to cluster people using scikit-learn.vtt

1. Supervised vs. Unsupervised Learning, and TrainTest.vtt

11. Decision Trees Concepts.vtt

4. Activity Implementing a Spam Classifier with Naive Bayes.vtt

5. K-Means Clustering.vtt

13. Ensemble Learning.vtt

2. Activity Using TrainTest to Prevent Overfitting a Polynomial Regression.vtt

6. Activity Clustering people based on income and age.vtt

15. Support Vector Machines (SVM) Overview.vtt

3. Bayesian Methods Concepts.vtt

7. Measuring Entropy.vtt

9. Activity MAC Installing Graphviz.vtt

10. Activity LINUX Installing Graphviz.vtt

8. Activity WINDOWS Installing Graphviz.vtt

/05. Recommender Systems/

5. Activity Making Movie Recommendations with Item-Based Collaborative Filtering.mp4

3. Activity Finding Movie Similarities using Cosine Similarity.mp4

1. User-Based Collaborative Filtering.mp4

4. Activity Improving the Results of Movie Similarities.mp4

6. Exercise Improve the recommender's results.mp4

2. Item-Based Collaborative Filtering.mp4

5. Activity Making Movie Recommendations with Item-Based Collaborative Filtering.vtt

3. Activity Finding Movie Similarities using Cosine Similarity.vtt

2. Item-Based Collaborative Filtering.vtt

1. User-Based Collaborative Filtering.vtt

4. Activity Improving the Results of Movie Similarities.vtt

6. Exercise Improve the recommender's results.vtt

/06. More Data Mining and Machine Learning Techniques/

6. Reinforcement Learning.mp4

2. Activity Using KNN to predict a rating for a movie.mp4

4. Activity PCA Example with the Iris data set.mp4

7. Activity Reinforcement Learning & Q-Learning with Gym.mp4

5. Data Warehousing Overview ETL and ELT.mp4

3. Dimensionality Reduction Principal Component Analysis (PCA).mp4

1. K-Nearest-Neighbors Concepts.mp4

9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4

8. Understanding a Confusion Matrix.mp4

7. Activity Reinforcement Learning & Q-Learning with Gym.vtt

6. Reinforcement Learning.vtt

2. Activity Using KNN to predict a rating for a movie.vtt

5. Data Warehousing Overview ETL and ELT.vtt

4. Activity PCA Example with the Iris data set.vtt

9. Measuring Classifiers (Precision, Recall, F1, ROC, AUC).vtt

8. Understanding a Confusion Matrix.vtt

3. Dimensionality Reduction Principal Component Analysis (PCA).vtt

1. K-Nearest-Neighbors Concepts.vtt

/07. Dealing with Real-World Data/

3. Data Cleaning and Normalization.mp4

2. Activity K-Fold Cross-Validation to avoid overfitting.mp4

10. Binning, Transforming, Encoding, Scaling, and Shuffling.mp4

4. Activity Cleaning web log data.mp4

6. Activity Detecting outliers.mp4

1. BiasVariance Tradeoff.mp4

8. Imputation Techniques for Missing Data.mp4

9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4

7. Feature Engineering and the Curse of Dimensionality.mp4

5. Normalizing numerical data.mp4

4. Activity Cleaning web log data.vtt

2. Activity K-Fold Cross-Validation to avoid overfitting.vtt

8. Imputation Techniques for Missing Data.vtt

10. Binning, Transforming, Encoding, Scaling, and Shuffling.vtt

3. Data Cleaning and Normalization.vtt

7. Feature Engineering and the Curse of Dimensionality.vtt

6. Activity Detecting outliers.vtt

1. BiasVariance Tradeoff.vtt

9. Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.vtt

5. Normalizing numerical data.vtt

/08. Apache Spark Machine Learning on Big Data/

3. Activity Installing Spark.mp4

7. Introduction to Decision Trees in Spark.mp4

8. Activity K-Means Clustering in Spark.mp4

10. Activity Searching Wikipedia with Spark.mp4

9. TF IDF.mp4

11. Activity Using the Spark DataFrame API for MLLib.mp4

4. Spark Introduction.mp4

5. Spark and the Resilient Distributed Dataset (RDD).mp4

6. Introducing MLLib.mp4

7. Introduction to Decision Trees in Spark.vtt

5. Spark and the Resilient Distributed Dataset (RDD).vtt

3. Activity Installing Spark.vtt

8. Activity K-Means Clustering in Spark.vtt

4. Spark Introduction.vtt

10. Activity Searching Wikipedia with Spark.vtt

11. Activity Using the Spark DataFrame API for MLLib.vtt

9. TF IDF.vtt

6. Introducing MLLib.vtt

2. Spark installation notes for MacOS and Linux users.html

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

/09. Experimental Design ML in the Real World/

6. AB Test Gotchas.mp4

4. Activity Hands-on With T-Tests.mp4

2. AB Testing Concepts.mp4

1. Deploying Models to Real-Time Systems.mp4

3. T-Tests and P-Values.mp4

5. Determining How Long to Run an Experiment.mp4

6. AB Test Gotchas.vtt

1. Deploying Models to Real-Time Systems.vtt

2. AB Testing Concepts.vtt

4. Activity Hands-on With T-Tests.vtt

3. T-Tests and P-Values.vtt

5. Determining How Long to Run an Experiment.vtt

/10. Deep Learning and Neural Networks/

17. The Ethics of Deep Learning.mp4

14. Activity Transfer Learning.mp4

6. Activity Using Tensorflow, Part 1.mp4

7. Activity Using Tensorflow, Part 2.mp4

13. Activity Using a RNN for sentiment analysis.mp4

8. Activity Introducing Keras.mp4

1. Deep Learning Pre-Requisites.mp4

2. The History of Artificial Neural Networks.mp4

9. Activity Using Keras to Predict Political Affiliations.mp4

10. Convolutional Neural Networks (CNN's).mp4

3. Activity Deep Learning in the Tensorflow Playground.mp4

11. Activity Using CNN's for handwriting recognition.mp4

5. Introducing Tensorflow.mp4

12. Recurrent Neural Networks (RNN's).mp4

4. Deep Learning Details.mp4

16. Deep Learning Regularization with Dropout and Early Stopping.mp4

15. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4

8. Activity Introducing Keras.vtt

6. Activity Using Tensorflow, Part 1.vtt

1. Deep Learning Pre-Requisites.vtt

5. Introducing Tensorflow.vtt

9. Activity Using Keras to Predict Political Affiliations.vtt

14. Activity Transfer Learning.vtt

10. Convolutional Neural Networks (CNN's).vtt

7. Activity Using Tensorflow, Part 2.vtt

17. The Ethics of Deep Learning.vtt

2. The History of Artificial Neural Networks.vtt

3. Activity Deep Learning in the Tensorflow Playground.vtt

12. Recurrent Neural Networks (RNN's).vtt

4. Deep Learning Details.vtt

13. Activity Using a RNN for sentiment analysis.vtt

11. Activity Using CNN's for handwriting recognition.vtt

16. Deep Learning Regularization with Dropout and Early Stopping.vtt

15. Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.vtt

/11. Generative Models/

2. Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST.mp4

5. Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST.mp4

4. Generative Adversarial Networks (GAN's) - Playing with some demos.mp4

1. Variational Auto-Encoders (VAE's) - how they work.mp4

6. Learning More about Deep Learning.mp4

3. Generative Adversarial Networks (GAN's) - How they work.mp4

5. GAN_on_Fashion_MNIST.ipynb

2. VariationalAutoEncoders.ipynb

2. Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST.vtt

5. Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST.vtt

4. Generative Adversarial Networks (GAN's) - Playing with some demos.vtt

1. Variational Auto-Encoders (VAE's) - how they work.vtt

3. Generative Adversarial Networks (GAN's) - How they work.vtt

6. Learning More about Deep Learning.vtt

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

11. Activity Fine Tuning GPT with the IMDb dataset.mp4

10. Activity Using small and large GPT models within Google CoLab and HuggingFace.mp4

7. Activity Tokenization with Google CoLab and HuggingFace.mp4

12. From GPT to ChatGPT Deep Reinforcement Learning, Proximal Policy Gradients.mp4

2. Self-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth.mp4

9. Activity Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT.mp4

4. How GPT Works, Part 1 The GPT Transformer Architecture.mp4

13. From GPT to ChatGPT Reinforcement Learning from Human Feedback and Moderation.mp4

1. The Transformer Architecture (encoders, decoders, and self-attention.).mp4

5. How GPT Works, Part 2 Tokenization, Positional Encoding, Embedding.mp4

3. Applications of Transformers (GPT).mp4

7. Transformers_MLCourse.ipynb

8. Activity Positional Encoding.mp4

6. Fine Tuning Transfer Learning with Transformers.mp4

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

2. Self-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth.vtt

7. Activity Tokenization with Google CoLab and HuggingFace.vtt

4. How GPT Works, Part 1 The GPT Transformer Architecture.vtt

12. From GPT to ChatGPT Deep Reinforcement Learning, Proximal Policy Gradients.vtt

11. Activity Fine Tuning GPT with the IMDb dataset.vtt

13. From GPT to ChatGPT Reinforcement Learning from Human Feedback and Moderation.vtt

9. Activity Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT.vtt

10. Activity Using small and large GPT models within Google CoLab and HuggingFace.vtt

5. How GPT Works, Part 2 Tokenization, Positional Encoding, Embedding.vtt

3. Applications of Transformers (GPT).vtt

6. Fine Tuning Transfer Learning with Transformers.vtt

8. Activity Positional Encoding.vtt

/13. The OpenAI API (Developing with GPT and ChatGPT)/

7. The New OpenAI Fine-Tuning API Fine-Tuning GPT-3.5 to simulate Commander Data!.mp4

6. Demo Fine-Tuning OpenAI's Davinci Model to simulate Data from Star Trek.mp4

2. Activity Using Tools and Functions in the OpenAI Chat Completion API.mp4

1. Activity The OpenAI Chat Completions API.mp4

4. Activity The Embeddings API in OpenAI Finding similarities between words.mp4

3. Activity The Images (DALL-E) API in OpenAI.mp4

8. Activity The OpenAI Moderation API.mp4

9. Activity The OpenAI Audio API (speech to text).mp4

5. The Legacy Fine-Tuning API for GPT Models in OpenAI.mp4

7. The New OpenAI Fine-Tuning API Fine-Tuning GPT-3.5 to simulate Commander Data!.vtt

6. Demo Fine-Tuning OpenAI's Davinci Model to simulate Data from Star Trek.vtt

1. Activity The OpenAI Chat Completions API.vtt

2. Activity Using Tools and Functions in the OpenAI Chat Completion API.vtt

7. MakingData.ipynb

4. Activity The Embeddings API in OpenAI Finding similarities between words.vtt

5. The Legacy Fine-Tuning API for GPT Models in OpenAI.vtt

3. Activity The Images (DALL-E) API in OpenAI.vtt

9. Activity The OpenAI Audio API (speech to text).vtt

8. Activity The OpenAI Moderation API.vtt

2. Functions.py

6. extract-script.py

1. Chat-Completions.py

4. Embedding.py

3. Image.py

9. Audio.py

8. Moderation.py

/14. Retrieval Augmented Generation (RAG,) Advanced RAG, and LLM Agents/

4. Activity Evaluating our RAG-based Cdr. Data using RAGAS and langchain.mp4

8. Activity Simulating Cdr. Data with Advanced RAG and langchain.mp4

10. Activity Building a Cdr. Data chatbot with LLM Agents, web search & math tools.mp4

1. Retrieval Augmented Generation (RAG) How it works, with some examples.mp4

2. Demo Using Retrieval Augmented Generation (RAG) to simulate Data from Star Trek.mp4

5. Advanced RAG Pre-Retrieval chunking semantic chunking data extraction.mp4

3. RAG Metrics The RAG Triad, relevancy, recall, precision, accuracy, and more.mp4

9. LLM Agents and Swarms of Agents.mp4

7. Advanced RAG Prompt Compression, and More Tuning Opportunities.mp4

6. Advanced RAG Query Rewriting.mp4

8. Data_Advanced_RAG.ipynb

2. Data_RAG.ipynb

10. Data_Agent.ipynb

4. Data_RAG_Metrics.ipynb

2. Demo Using Retrieval Augmented Generation (RAG) to simulate Data from Star Trek.vtt

4. Activity Evaluating our RAG-based Cdr. Data using RAGAS and langchain.vtt

1. Retrieval Augmented Generation (RAG) How it works, with some examples.vtt

10. Activity Building a Cdr. Data chatbot with LLM Agents, web search & math tools.vtt

8. Activity Simulating Cdr. Data with Advanced RAG and langchain.vtt

3. RAG Metrics The RAG Triad, relevancy, recall, precision, accuracy, and more.vtt

5. Advanced RAG Pre-Retrieval chunking semantic chunking data extraction.vtt

7. Advanced RAG Prompt Compression, and More Tuning Opportunities.vtt

9. LLM Agents and Swarms of Agents.vtt

6. Advanced RAG Query Rewriting.vtt

/15. Final Project/

2. Final project review.mp4

1. Your final project assignment Mammogram Classification.mp4

2. Final project review.vtt

1. Your final project assignment Mammogram Classification.vtt

/16. You made it!/

1. More to Explore.mp4

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3. Bonus Lecture.html

1. More to Explore.vtt

2. Don't Forget to Leave a Rating!.html

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