English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 139 lectures (18h 49m) | 7.21 GB
Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks
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 specialists—with an average salary of $159,000 as reported by Glassdoor—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 130 lectures and 18+ 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.
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.
- 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.
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
Table of Contents
Getting Started
1 Introduction
2 Udemy 101 Getting the Most From This Course
3 Important note
4 Installation Getting Started
5 [Activity] WINDOWS Installing and Using Anaconda & Course Materials
6 [Activity] MAC Installing and Using Anaconda & Course Materials
7 [Activity] LINUX Installing and Using Anaconda & Course Materials
8 Python Basics, Part 1 [Optional]
9 [Activity] Python Basics, Part 2 [Optional]
10 [Activity] Python Basics, Part 3 [Optional]
11 [Activity] Python Basics, Part 4 [Optional]
12 Introducing the Pandas Library [Optional]
Statistics and Probability Refresher, and Python Practice
13 Types of Data (Numerical, Categorical, Ordinal)
14 Mean, Median, Mode
15 [Activity] Using mean, median, and mode in Python
16 [Activity] Variation and Standard Deviation
17 Probability Density Function; Probability Mass Function
18 Common Data Distributions (Normal, Binomial, Poisson, etc)
19 [Activity] Percentiles and Moments
20 [Activity] A Crash Course in matplotlib
21 [Activity] Advanced Visualization with Seaborn
22 [Activity] Covariance and Correlation
23 [Exercise] Conditional Probability
24 Exercise Solution Conditional Probability of Purchase by Age
25 Bayes’ Theorem
Predictive Models
26 [Activity] Linear Regression
27 [Activity] Polynomial Regression
28 [Activity] Multiple Regression, and Predicting Car Prices
29 Multi-Level Models
Machine Learning with Python
30 Supervised vs. Unsupervised Learning, and TrainTest
31 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression
32 Bayesian Methods Concepts
33 [Activity] Implementing a Spam Classifier with Naive Bayes
34 K-Means Clustering
35 [Activity] Clustering people based on income and age
36 Measuring Entropy
37 [Activity] WINDOWS Installing Graphviz
38 [Activity] MAC Installing Graphviz
39 [Activity] LINUX Installing Graphviz
40 Decision Trees Concepts
41 [Activity] Decision Trees Predicting Hiring Decisions
42 Ensemble Learning
43 [Activity] XGBoost
44 Support Vector Machines (SVM) Overview
45 [Activity] Using SVM to cluster people using scikit-learn
Recommender Systems
46 User-Based Collaborative Filtering
47 Item-Based Collaborative Filtering
48 [Activity] Finding Movie Similarities using Cosine Similarity
49 [Activity] Improving the Results of Movie Similarities
50 [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering
51 [Exercise] Improve the recommender’s results
More Data Mining and Machine Learning Techniques
52 K-Nearest-Neighbors Concepts
53 [Activity] Using KNN to predict a rating for a movie
54 Dimensionality Reduction; Principal Component Analysis (PCA)
55 [Activity] PCA Example with the Iris data set
56 Data Warehousing Overview ETL and ELT
57 Reinforcement Learning
58 [Activity] Reinforcement Learning & Q-Learning with Gym
59 Understanding a Confusion Matrix
60 Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
Dealing with Real-World Data
61 BiasVariance Tradeoff
62 [Activity] K-Fold Cross-Validation to avoid overfitting
63 Data Cleaning and Normalization
64 [Activity] Cleaning web log data
65 Normalizing numerical data
66 [Activity] Detecting outliers
67 Feature Engineering and the Curse of Dimensionality
68 Imputation Techniques for Missing Data
69 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE
70 Binning, Transforming, Encoding, Scaling, and Shuffling
Apache Spark Machine Learning on Big Data
71 Warning about Java 21+ and Spark 3!
72 Spark installation notes for MacOS and Linux users
73 [Activity] Installing Spark
74 Spark Introduction
75 Spark and the Resilient Distributed Dataset (RDD)
76 Introducing MLLib
77 Introduction to Decision Trees in Spark
78 [Activity] K-Means Clustering in Spark
79 TF IDF
80 [Activity] Searching Wikipedia with Spark
81 [Activity] Using the Spark DataFrame API for MLLib
Experimental Design ML in the Real World
82 Deploying Models to Real-Time Systems
83 AB Testing Concepts
84 T-Tests and P-Values
85 [Activity] Hands-on With T-Tests
86 Determining How Long to Run an Experiment
87 AB Test Gotchas
Deep Learning and Neural Networks
88 Deep Learning Pre-Requisites
89 The History of Artificial Neural Networks
90 [Activity] Deep Learning in the Tensorflow Playground
91 Deep Learning Details
92 Introducing Tensorflow
93 [Activity] Using Tensorflow, Part 1
94 [Activity] Using Tensorflow, Part 2
95 [Activity] Introducing Keras
96 [Activity] Using Keras to Predict Political Affiliations
97 Convolutional Neural Networks (CNN’s)
98 [Activity] Using CNN’s for handwriting recognition
99 Recurrent Neural Networks (RNN’s)
100 [Activity] Using a RNN for sentiment analysis
101 [Activity] Transfer Learning
102 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters
103 Deep Learning Regularization with Dropout and Early Stopping
104 The Ethics of Deep Learning
Generative Models
105 Variational Auto-Encoders (VAE’s) – how they work
106 Variational Auto-Encoders (VAE) – Hands-on with Fashion MNIST
107 Generative Adversarial Networks (GAN’s) – How they work
108 Generative Adversarial Networks (GAN’s) – Playing with some demos
109 Generative Adversarial Networks (GAN’s) – Hands-on with Fashion MNIST
110 Learning More about Deep Learning
Generative AI GPT, ChatGPT, Transformers, Self Attention Based Neural Networks
111 The Transformer Architecture (encoders, decoders, and self-attention.)
112 Self-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth
113 Applications of Transformers (GPT)
114 How GPT Works, Part 1 The GPT Transformer Architecture
115 How GPT Works, Part 2 Tokenization, Positional Encoding, Embedding
116 Fine Tuning Transfer Learning with Transformers
117 [Activity] Tokenization with Google CoLab and HuggingFace
118 [Activity] Positional Encoding
119 [Activity] Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT
120 [Activity] Using small and large GPT models within Google CoLab and HuggingFace
121 [Activity] Fine Tuning GPT with the IMDb dataset
122 From GPT to ChatGPT Deep Reinforcement Learning, Proximal Policy Gradients
123 From GPT to ChatGPT Reinforcement Learning from Human Feedback and Moderation
The OpenAI API (Developing with GPT and ChatGPT)
124 [Activity] The OpenAI Chat Completions API
125 [Activity] Using Tools and Functions in the OpenAI Chat Completion API
126 [Activity] The Images (DALL-E) API in OpenAI
127 [Activity] The Embeddings API in OpenAI Finding similarities between words
128 The Legacy Fine-Tuning API for GPT Models in OpenAI
129 [Demo] Fine-Tuning OpenAI’s Davinci Model to simulate Data from Star Trek
130 The New OpenAI Fine-Tuning API; Fine-Tuning GPT-3.5 to simulate Commander Data!
131 [Activity] The OpenAI Moderation API
132 [Activity] The OpenAI Audio API (speech to text)
Retrieval Augmented Generation (RAG)
133 Retrieval Augmented Generation (RAG) How it works, with some examples
134 Demo Using Retrieval Augmented Generation (RAG) to simulate Data from Star Trek
Final Project
135 Your final project assignment Mammogram Classification
136 Final project review
You made it!
137 More to Explore
138 Don’t Forget to Leave a Rating!
139 Bonus Lecture
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