Machine Learning, Data Science and Generative AI with Python

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Sundog Education by Frank Kane,Frank Kane,Sundog Education Team

18:45:32

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01 - Getting Started

001 Introduction.mp4

02:41

002 Udemy 101 Getting the Most From This Course.mp4

02:10

003 Important note.html

004 Installation Getting Started.html

005 [Activity] WINDOWS Installing and Using Anaconda & Course Materials.mp4

10:50

006 [Activity] MAC Installing and Using Anaconda & Course Materials.mp4

08:07

007 [Activity] LINUX Installing and Using Anaconda & Course Materials.mp4

09:11

008 Python Basics, Part 1 [Optional].mp4

04:59

009 [Activity] Python Basics, Part 2 [Optional].mp4

05:17

010 [Activity] Python Basics, Part 3 [Optional].mp4

02:46

011 [Activity] Python Basics, Part 4 [Optional].mp4

04:02

012 Introducing the Pandas Library [Optional].mp4

10:08

02 - Statistics and Probability Refresher, and Python Practice

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

06:58

002 Mean, Median, Mode.mp4

05:26

003 [Activity] Using mean, median, and mode in Python.mp4

08:21

004 [Activity] Variation and Standard Deviation.mp4

11:12

005 Probability Density Function; Probability Mass Function.mp4

03:27

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

07:45

007 [Activity] Percentiles and Moments.mp4

12:33

008 [Activity] A Crash Course in matplotlib.mp4

13:46

009 [Activity] Advanced Visualization with Seaborn.mp4

17:30

010 [Activity] Covariance and Correlation.mp4

11:31

011 [Exercise] Conditional Probability.mp4

16:04

012 Exercise Solution Conditional Probability of Purchase by Age.mp4

02:20

013 Bayes Theorem.mp4

05:23

03 - Predictive Models

001 [Activity] Linear Regression.mp4

11:01

002 [Activity] Polynomial Regression.mp4

08:04

003 [Activity] Multiple Regression, and Predicting Car Prices.mp4

16:26

004 Multi-Level Models.mp4

04:36

04 - Machine Learning with Python

001 Supervised vs. Unsupervised Learning, and TrainTest.mp4

08:57

002 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4

05:47

003 Bayesian Methods Concepts.mp4

04:00

004 [Activity] Implementing a Spam Classifier with Naive Bayes.mp4

08:05

005 K-Means Clustering.mp4

07:23

006 [Activity] Clustering people based on income and age.mp4

05:14

007 Measuring Entropy.mp4

03:10

008 [Activity] WINDOWS Installing Graphviz.mp4

00:22

009 [Activity] MAC Installing Graphviz.mp4

01:16

010 [Activity] LINUX Installing Graphviz.mp4

00:54

011 Decision Trees Concepts.mp4

08:43

012 [Activity] Decision Trees Predicting Hiring Decisions.mp4

09:47

013 Ensemble Learning.mp4

05:59

014 [Activity] XGBoost.mp4

15:30

015 Support Vector Machines (SVM) Overview.mp4

04:27

016 [Activity] Using SVM to cluster people using scikit-learn.mp4

09:30

05 - Recommender Systems

001 User-Based Collaborative Filtering.mp4

07:57

002 Item-Based Collaborative Filtering.mp4

08:15

003 [Activity] Finding Movie Similarities using Cosine Similarity.mp4

09:08

004 [Activity] Improving the Results of Movie Similarities.mp4

07:59

005 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering.mp4

10:22

006 [Exercise] Improve the recommenders results.mp4

05:29

06 - More Data Mining and Machine Learning Techniques

001 K-Nearest-Neighbors Concepts.mp4

03:44

002 [Activity] Using KNN to predict a rating for a movie.mp4

12:29

003 Dimensionality Reduction; Principal Component Analysis (PCA).mp4

05:44

004 [Activity] PCA Example with the Iris data set.mp4

09:05

005 Data Warehousing Overview ETL and ELT.mp4

09:05

006 Reinforcement Learning.mp4

12:44

007 [Activity] Reinforcement Learning & Q-Learning with Gym.mp4

12:57

008 Understanding a Confusion Matrix.mp4

05:18

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

06:35

external-links.txt

07 - Dealing with Real-World Data

001 BiasVariance Tradeoff.mp4

06:15

002 [Activity] K-Fold Cross-Validation to avoid overfitting.mp4

10:26

003 Data Cleaning and Normalization.mp4

07:10

004 [Activity] Cleaning web log data.mp4

10:56

005 Normalizing numerical data.mp4

03:22

006 [Activity] Detecting outliers.mp4

06:21

007 Feature Engineering and the Curse of Dimensionality.mp4

06:04

008 Imputation Techniques for Missing Data.mp4

07:48

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

05:35

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

07:51

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.mp4

11:06

004 Spark Introduction.mp4

09:10

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

11:42

006 Introducing MLLib.mp4

05:09

007 Introduction to Decision Trees in Spark.mp4

16:15

008 [Activity] K-Means Clustering in Spark.mp4

11:23

009 TF IDF.mp4

06:43

010 [Activity] Searching Wikipedia with Spark.mp4

08:21

011 [Activity] Using the Spark DataFrame API for MLLib.mp4

08:07

09 - Experimental Design ML in the Real World

001 Deploying Models to Real-Time Systems.mp4

08:42

002 AB Testing Concepts.mp4

08:23

003 T-Tests and P-Values.mp4

05:59

004 [Activity] Hands-on With T-Tests.mp4

06:04

005 Determining How Long to Run an Experiment.mp4

03:24

006 AB Test Gotchas.mp4

09:26

10 - Deep Learning and Neural Networks

001 Deep Learning Pre-Requisites.mp4

11:43

002 The History of Artificial Neural Networks.mp4

11:14

003 [Activity] Deep Learning in the Tensorflow Playground.mp4

12:00

004 Deep Learning Details.mp4

09:29

005 Introducing Tensorflow.mp4

11:29

006 [Activity] Using Tensorflow, Part 1.mp4

13:11

007 [Activity] Using Tensorflow, Part 2.mp4

12:03

008 [Activity] Introducing Keras.mp4

13:33

009 [Activity] Using Keras to Predict Political Affiliations.mp4

11:49

010 Convolutional Neural Networks (CNNs).mp4

11:28

011 [Activity] Using CNNs for handwriting recognition.mp4

08:02

012 Recurrent Neural Networks (RNNs).mp4

11:02

013 [Activity] Using a RNN for sentiment analysis.mp4

09:37

014 [Activity] Transfer Learning.mp4

12:14

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

04:39

016 Deep Learning Regularization with Dropout and Early Stopping.mp4

06:21

017 The Ethics of Deep Learning.mp4

11:02

11 - Generative Models

001 Variational Auto-Encoders (VAEs) - how they work.mp4

10:23

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

26:31

002 variationalautoencoders.zip

003 Generative Adversarial Networks (GANs) - How they work.mp4

07:40

004 Generative Adversarial Networks (GANs) - Playing with some demos.mp4

11:23

005 Generative Adversarial Networks (GANs) - Hands-on with Fashion MNIST.mp4

15:20

005 gan-on-fashion-mnist.zip

006 Learning More about Deep Learning.mp4

01:45

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

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

10:08

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

09:51

003 Applications of Transformers (GPT).mp4

04:35

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

07:20

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

04:49

006 Fine Tuning Transfer Learning with Transformers.mp4

02:29

007 transformers-mlcourse.zip

007 [Activity] Tokenization with Google CoLab and HuggingFace.mp4

09:12

008 [Activity] Positional Encoding.mp4

02:14

009 [Activity] Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT.mp4

06:13

010 [Activity] Using small and large GPT models within Google CoLab and HuggingFace.mp4

05:31

011 [Activity] Fine Tuning GPT with the IMDb dataset.mp4

06:44

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

07:21

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

06:06

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

001 chat-completions.zip

001 [Activity] The OpenAI Chat Completions API.mp4

11:45

002 functions.zip

002 [Activity] Using Tools and Functions in the OpenAI Chat Completion API.mp4

09:17

003 image.zip

003 [Activity] The Images (DALL-E) API in OpenAI.mp4

04:36

004 embedding.zip

004 [Activity] The Embeddings API in OpenAI Finding similarities between words.mp4

06:15

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

05:16

006 extract-script.zip

006 [Demo] Fine-Tuning OpenAIs Davinci Model to simulate Data from Star Trek.mp4

17:55

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

21:15

007 makingdata.zip

008 moderation.zip

008 [Activity] The OpenAI Moderation API.mp4

02:34

009 audio.zip

009 [Activity] The OpenAI Audio API (speech to text).mp4

03:58

14 - Retrieval Augmented Generation (RAG)

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

17:12

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

19:03

002 data-rag.zip

15 - Final Project

001 Your final project assignment Mammogram Classification.mp4

06:19

002 Final project review.mp4

10:26

16 - You made it!

001 More to Explore.mp4

02:59

002 Dont Forget to Leave a Rating!.html

003 Bonus Lecture.html

Description

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

What You'll Learn?

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

Who is this for?

Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.

Technologists curious about how deep learning really works

Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.

If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

What You Need to Know?

You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.

Some prior coding or scripting experience is required.

At least high school level math skills will be required.

More details

Description
Unlock the Power of Machine Learning & AI: Master the Art of Turning Data into Insight
Discover the Future of Technology with Our Comprehensive Machine Learning & AI Course - Featuring Generative AI, Deep Learning, and Beyond!
In an era where Machine Learning (ML) and Artificial Intelligence (AI) are revolutionizing industries across the globe, understanding how giants like Google, Amazon, and Udemy leverage these technologies to extract meaningful insights from vast data sets is more critical than ever. Whether you're aiming to join the ranks of top-tier AI specialistsâ€”with an average salary of $159,000 as reported by Glassdoorâ€”or you're driven by the fascinating challenges this field offers, our course is your gateway to an exciting new career trajectory.
Designed for individuals with programming or scripting backgrounds, this course goes beyond the basics, preparing you to stand out in the competitive tech industry. Our curriculum, enriched with over 145 lectures and 20+ hours of video content, is crafted to provide hands-on experience with Python, guiding you from the fundamentals of statistics to the cutting-edge advancements in generative AI.
Why Choose This Course?
Updated Content on Generative AI: Dive into the latest in AI with modules on transformers, GPT, ChatGPT, the OpenAI API, Advanced Retrieval Augmented GenerationÂ (RAG), LLMÂ agents, langchain, and self-attention based neural networks.
Real-World Application: Learn through Python code examples based on real-life scenarios, making the abstract concepts of ML and AI tangible and actionable.
Industry-Relevant Skills: Our curriculum is designed based on the analysis of job listings from top tech firms, ensuring you gain the skills most sought after by employers.
Diverse Topics Covered: From neural networks, TensorFlow, and Keras to sentiment analysis and image recognition, our course covers a wide range of ML models and techniques, ensuring a well-rounded education.
Accessible Learning: Complex concepts are explained in plain English, focusing on practical application rather than academic jargon, making the learning process straightforward and engaging.
Course Highlights:
Introduction to Python and basic statistics, setting a strong foundation for your journey in ML and AI.
Deep Learning techniques, including MLPs, CNNs, and RNNs, with practical exercises in TensorFlow and Keras.
Extensive modules on the mechanics of modern generative AI, including transformers and the OpenAI API, with hands-on projects like fine-tuning GPT, Advanced RAG, langchain, and LLMÂ agents.
A comprehensive overview of machine learning models beyond GenAI, including SVMs, reinforcement learning, decision trees, and more, ensuring you have a broad understanding of the field.
Practical data science applications, such as data visualization, regression analysis, clustering, and feature engineering, empowering you to tackle real-world data challenges.
A special section on Apache Spark, enabling you to apply these techniques to big data, analyzed on computing clusters.
No previous Python experience? No problem! We kickstart your journey with a Python crash course to ensure you're well-equipped to tackle the modules that follow.
Transform Your Career Today
Join a community of learners who have successfully transitioned into the tech industry, leveraging the knowledge and skills acquired from our course to excel in corporate and research roles in AI and ML.
"I started doing your course... and it was pivotal in helping me transition into a role where I now solve corporate problems using AI. Your course demystified how to succeed in corporate AI research, making you the most impressive instructor in ML I've encountered." - Kanad Basu, PhD
Are you ready to step into the future of technology and make a mark in the fields of machine learning and artificial intelligence? Enroll now and embark on a journey that transforms data into powerful insights, paving your way to a rewarding career in AI and ML.
Who this course is for:
Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
Technologists curious about how deep learning really works
Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

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Sundog Education by Frank Kane

Instructor's Courses

Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Frank Kane

Instructor's Courses

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

Sundog Education Team

Instructor's Courses

Our mission is to make highly valuable skills in machine learning, big data, AI, and data science accessible at prices anyone in the world can afford. Our current online courses have reached over 500,000 students worldwide. Sundog Education CEO, Frank Kane, spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaching others about big data analysis.

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Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.