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The Complete Python and Machine Learning for Everybody 2.0
Python Language Basics
00. Introduction (4:47)
01. Intro To Python (5:46)
02. Variables (19:34)
03. Type Conversion Examples (10:21)
04. Operators (7:21)
05. Operators Examples (22:09)
06. Collections (8:39)
07. Lists (11:55)
08. Multidimensional List Examples (8:22)
09. Tuples Examples (8:51)
10. Dictionaries Examples (14:41)
11. Ranges Examples (8:46)
12. Conditionals (6:58)
13. If Statement Examples (10:32)
14. If Statement Variants Examples (11:35)
15. Loops (7:17)
16. While Loops Examples (11:47)
17. For Loops Examples (11:35)
18. Functions (8:04)
19. Functions Examples (9:33)
20. Parameters And Return Values Examples (14:08)
21. Classes and Objects (11:30)
22. Classes Example (13:28)
23. Objects Examples (10:10)
24. Inheritance Examples (17:43)
25. Static Members Example (11:20)
26. Summary and Outro (4:23)
Intro to Python Slides
Python_Language_Basics
Numpy 2020
00. Course Intro (5:11)
01. Intro to Numpy (6:20)
02. Installing Numpy (3:59)
03. Creating Numpy Arrays (16:55)
04. Creating Numpy Matrices (11:57)
05. Getting and Setting Numpy Elements (16:59)
06. Arithmetic Operations on Numpy Arrays (11:56)
07. Numpy Functions Part 1 (19:13)
08. Numpy Functions Part 2 (12:36)
09. Summary and Outro (3:01)
PyPlot 2020
00. Course Intro (5:30)
01. Intro to Pyplot (5:10)
02. Installing Matplotlib (5:51)
03. Basic Line Plot (7:53)
04. Customizing Graphs (10:47)
05. Plotting Multiple Datasets (8:10)
06. Bar Chart (6:26)
07. Pie Chart (9:13)
08. Histogram (10:14)
09. 3D Plotting (6:28)
10. Course Outro (4:09)
Pyplot Code
Pandas 2020
00. Panda Course Introduction (5:43)
01. Intro To Pandas (7:55)
02. Installing Pandas (5:28)
03. Creating Pandas Series (20:34)
04. Date Ranges (11:29)
05. Getting Elements From Series (19:20)
06. Getting Properties Of Series (13:04)
07. Modifying Series (19:01)
08. Operations On Series (11:48)
09. Creating Pandas Dataframes (22:57)
10. Getting Elements From Dataframes (25:12)
11. Getting Properties From Dataframes (17:44)
12. Dataframe Modification (36:24)
13. Dataframe Operations (20:09)
14 Dataframe Comparisons And Iteration (15:35)
15. Reading Csvs (12:00)
16.summary And Outro (4:14)
Source Files
Machine Learning Theory 2020
00. Course Intro (6:04)
01. Quick Intro to Machine Learning (9:00)
02. Deep Dive into Machine Learning (6:01)
03. Problems Solved with Machine Learning Part 1 (13:26)
04. Problems Solved with Machine Learning Part 2 (16:25)
05. Types of Machine Learning (10:15)
06. How Machine Learning Works (11:40)
07. Common Machine Learning Structures (13:51)
08. Steps to Build a Machine Learning Program (16:34)
09. Summary and Outro (2:49)
Intro to Machine Learning Slides
Intro to Tensorflow 2
00. Course Intro (6:10)
01. Intro to Tensorflow (5:32)
02. Installing Tensorflow (3:52)
03. Intro to Linear Regression (9:26)
04. Linear Regression Model - Creating Dataset (5:49)
05. Linear Regression Model - Building the Model (2:21)
06. Linear Regression Model - Creating a Loss Function (5:57)
07. Linear Regression Model - Training the Model (12:42)
08. Linear Regression Model - Testing the Model (5:22)
09. Summary and Outro (2:55)
Source Files
Image Recognition with MNIST
00. Course Intro (6:57)
01. Intro To Image Recognition (14:07)
02. Intro To Mnist (4:42)
03. Building A CNN Part 1 - Obtaining Data (15:40)
04. Building A CNN Part 2 - Building The Model (10:14)
05. Building A CNN Part 3 - Adding Loss And Optimizer Functions (4:57)
06. Building A CNN Part 4 - Train And Test Functions (10:58)
07. Building A CNN Part 5 - Train And Test The Model (9:17)
08. Mnist Image Recognition With Keras Sequential Model (13:23)
09. Summary And Outro (3:40)
Source Files
Machine Learning: Beginners - Build Beginner Models in TensorFlow 2.0
01 Course Overview (3:30)
02 Build Models On The Web (5:06)
Source Files
Machine Learning: Beginners - AI Uninformed Search Algorithms
01 What Are Search Algorithms (7:20)
02 Depth First Search (9:00)
02b Build A Depth First Search Algorithm (8:26)
03 What Is Breadth First Search (bfs) (5:08)
03b Build A Breadth First Search Algorithm (6:55)
04 Depth Limited Search (3:57)
05 Iterative Deepening Depth First Search (5:32)
06 What Is Uniform Cost Search (6:04)
06b Build A Uniform Cost Search Algorithm (8:07)
07 Bidirectional Search (4:43)
Source Files
Machine Learning: Beginners - AI Informed Search Algorithms
01 What Are Informed Search Algorithms (4:07)
02 What Is Greedy Best-first Search (8:16)
02b Build A Greedy Best First Search Algorithm (10:43)
03 What Is A Search (5:10)
Source Files
Machine Learning: Beginners - How Machine Learning Works
01 How Does A Machine Learning Agent Learn (7:37)
02 What Is Inductive Learning (4:10)
03 Make Decisions With Decision Trees (10:49)
04 Performance Of A Machine Learning Algorithm (4:13)
05 Handle Noise In Data (5:20)
06 Statistical Learning (3:56)
Source Files
Machine Learning: Beginners - Logistic Regression
01 What Is Logistic Regression (4:26)
02a How To Prepare Data (8:52)
02 Prepare Data For Logistic Regression (12:19)
03 Build A Logistic Regression Model (5:29)
03a How To Build A Logistic Regression Model (3:28)
03b What Is Optimization (12:10)
04a How To Optimize A Logistic Regression Model (12:45)
04 Optimize The Logistic Regression Model (12:44)
05 Train The Model (10:09)
06 Test The Model (2:33)
07 Visualize Results (5:38)
Source Files
Machine Learning: Beginners - Gradient Boosted Classification
01 What Is Gradient Boosting (1:54)
02 Prepare Data For Gradient Boosted Classification (7:19)
03 Build Binary Classes (6:12)
04a How To Shape Data For Classification (2:57)
04b Shape Data For Classification (7:06)
05a How To Build A Boosted Trees Classifier (4:03)
05b Build A Boosted Trees Classifier (4:37)
06 Source Files
Machine Learning: Beginners - Gradient Boosted Regression
01 Build Input Functions (3:55)
02 Build A Boosted Trees Regressor (3:02)
03 Train And Evaluate The Model (4:07)
Source Files
Machine Learning Intermediate - Supervised Learning Introduction
01 What You'll Learn (8:47)
02 What Is Supervised Learning (14:41)
03 Build Models On The Web (5:06)
Source Files
Machine Learning Intermediate - Linear Algebra for Deep Learning
01 Scalar (4:40)
02 Vector (7:36)
03 Matrix (8:21)
04 Tensor (6:51)
Source Files
Machine Learning Intermediate - Matrix Operations
01 Matrix-matrix Addition (4:53)
02 Matrix-scalar Addition (1:59)
03 Matrix-scalar Multiplication (2:06)
04 Matrix Multiplication (2:34)
Source Files
Machine Learning Intermediate - Build a Neural Network from Scratch
01 What Is A Neural Network (8:02)
02 Prepare Data (8:31)
03 Shuffle And Batch Data (3:26)
04 Build Weights And Biases (6:25)
05 Build A Neural Network From Scratch (5:28)
06 Optimize The Model (10:20)
07 Train And Evaluate The Model (11:36)
08 Test And Visualize The Neural Network (9:57)
Source Files
Machine Learning Intermediate - Build a Convolutional Neural Network from Scratch
01 What Is A Convolutional Neural Network (4:32)
02 Prepare Data For A Convolutional Neural Network (4:09)
03 Shuffle And Batch Data (2:17)
04 Build Weights And Biases (8:48)
05 What Are Wrappers (18:09)
06 Build A Convolutional Neural Network From Scratch (9:57)
07 What Is The Adam Optimizer (13:20)
08 Train And Evaluate The Model (10:32)
09 Test And Visualize The Convolutional Neural Network (7:49)
Source Files
Machine Learning Intermediate - Convolutional Neural Networks CIFAR-Image Classification
01 Prepare Data For CIFAR-image Classification (9:48)
02 Normalize Image Values (2:21)
03 Define Classes And Visualize Dataset (8:31)
Source Files
04A 2D Convolution Layer (10:14)
04B Relu Activation Function (6:40)
04C 2D Max Pooling Layer (9:34)
04D Flatten And Dense Layers (5:33)
04E Build A CNN For CIFAR-image Classification (13:42)
Source Files
05A How Do You Build An Optimizer For CIFAR-image Classification (12:53)
05B How Do You Calculate Loss For CIFAR-image Classification (12:05)
05C Build An Optimizer For CIFAR-image Classification (3:01)
Source Files
06 Train The CNN For CIFAR-image Classification (8:20)
07 Evaluate And Visualize The CNN (8:07)
Source Files
Machine Learning Intermediate - Build a Recurrent Neural Network
01 What Is A Recurrent Neural Network (4:58)
02 Prepare Data For A Recurrent Neural Network (7:25)
03 Shuffle And Batch Data (2:43)
04 Build A Recurrent Neural Network (7:42)
05 Calculate Accuracy And Loss (4:53)
06 Optimize The Neural Network (5:08)
07 Train A Recurrent Neural Network (6:09)
Source Files
Machine Learning Intermediate - Build a Dynamic Recurrent Neural Network
01 What Is A Dynamic Neural Network (6:09)
02 Generate Sample Data (13:39)
03 Shuffle And Batch Data (4:23)
04 Build A Dynamic Neural Network (7:34)
05 Calculate Accuracy And Loss (5:15)
06 Optimize The Neural Network (7:29)
07 Train A Dynamic Neural Network (11:55)
Source Files
Machine Learning Intermediate - Build a Bi-directional Recurrent Neural Network
01 What Is A Bi-directional Neural Network (5:46)
02 Prepare Data For A Bi-directional Neural Network (8:54)
03 Build A Bi-directional Neural Network (8:43)
04 Calculate Accuracy And Loss (5:51)
05 Optimize The Bi-directional RNN (5:29)
06 Train A Recurrent Neural Network (6:44)
Source Files
Machine Learning Intermediate - Prepare Data For Image Segmentation
01 Load Data For Image Segmentation (6:01)
02 Normalize Images (2:38)
03 Load Training Images (7:11)
04 Load Testing Images (4:38)
05 Prepare Data For Image Segmentation (6:25)
06 Visualize Images And Masks (5:20)
Source Files
Machine Learning Intermediate - Build A Neural Network For Image Segmentation
01 How Do You Build A Neural Network For Image Segmentation (10:04)
02 Set Up A Neural Network (6:36)
03 Build Neural Network Layers (6:32)
04 Compile Optimizer And Loss (2:07)
Source Files
Machine Learning Intermediate - Train And Test Image Segmentation
01 Build A Mask (1:34)
02 Visualize Model Progress (5:13)
03 Visualize Model Results (13:36)
04 Plot Model Accuracy (7:50)
05 Test The Neural Network (3:37)
Source Files
Machine Learning Intermediate - Word2Vec Sentiment Classification of Words
00 What Is Word2vec (5:19)
Source Files
Machine Learning Intermediate - Prepare Data for Word2Vec Sentiment Classification
01 Load Data For Word2vec (7:37)
02 Build Datasets For Word2vec (5:56)
03 Cache And Prefetch Data For Word2vec (1:56)
Source Files
Machine Learning Intermediate - Build Layers for Word2Vec
00 How Do You Build An Embedding Layer (2:06)
01 Build A Word2vec Embedding Layer (2:35)
02 Clean Data For Word2vec (4:29)
03 How Do You Vectorize Text (10:20)
04 How Do You Build A Word2vec Sequential Layer (27:14)
Source Files
Machine Learning Intermediate - Train And Test Word2Vec
01 Optimizer And Loss For Word2vec (2:24)
02 Train And Test The Word2vec Model (2:50)
03 Visualize Word Embeddings (9:02)
Source Files
Machine Learning Advanced - Unsupervised Learning Introduction
01 What You'll Learn (5:27)
02 What Is Unsupervised Learning (14:56)
03 Build Models On The Web (5:06)
Source Files
Machine Learning Advanced - Build an Auto-Encoder
01 What Is An Auto-encoder (4:44)
02 Prepare Data For An Auto-encoder (10:23)
03 Build Weights And Biases (6:40)
04 Build Encoder And Decoder (6:00)
05 Optimize The Auto-encoder (7:48)
06 Train The Auto-encoder (5:36)
07 Visualize Reconstructed Images (14:15)
Source Files
Machine Learning Advanced - Deep Convolutional Generative Adversarial Network
01 What Is A Generative Adversarial Network (8:21)
02 Prepare Data For A Deep Convolutional Generative Adversarial Network (6:48)
Source Files
03A Dense Layer (5:04)
03B Batch Normalization Layer (4:07)
03C Leaky ReLu Activation Function (15:55)
03D Transposed Convolution Layer (10:07)
03E Hyperbolic Tangent (tanH) (6:16)
03F Build A Generator (11:15)
Source Files
04A How Do You Build A Discriminator (8:22)
04B Build A Discriminator (11:39)
05 Calculate Losses (6:47)
06 Optimize The Deep Convolutional Generative Adversarial Network (12:26)
07 Train The Deep Convolutional Generative Adversarial Network (10:55)
08 Visualize Test Results (8:42)
Source Files
Machine Learning Advanced - Image To Image Translation With A GAN
01 Load Data For Image To Image Translation (8:56)
02 Visualize Data For Image To Image Translation (3:31)
Source Files
Machine Learning Advanced - Prepare Data For Image To Image Translation
01 Resize Data (2:00)
02 Crop Data (2:15)
03 Diversify Image Data (3:16)
04 Visualize Diversification (3:59)
05 Normalize Image Data (1:28)
06 Load Training Images (2:16)
07 Load Testing Images (2:51)
Source Files
Machine Learning Advanced - Build Datasets For Image To Image Translation
01 Build Training Dataset (4:29)
02 Build Testing Dataset (2:48)
Source Files
Machine Learning Advanced - Build A Generator For Image To Image Translation
01 Downsample Images (3:58)
02 Upsample Images (4:35)
03 Build A Downstack (3:20)
04 Build An Upstack (3:02)
05 Build More Layers For The Generator (3:31)
06 Downsample Through The Downstack (1:49)
07 Upsample Through The Upstack (3:36)
08 Visualize The Generator (2:50)
Source Files
Machine Learning Advanced - Build A Discriminator For Image To Image Translation
01 Build A Discriminator (8:49)
02 Visualize The Discriminator (8:49)
Source Files
Machine Learning Advanced - Optimizer and Loss For Image To Image Translation
01 Calculate Generator Loss (4:05)
02 Calculate Discriminator Loss (3:35)
03 Build Optimizers For The GAN (1:32)
Source Files
Machine Learning Advanced - Train And Test The GAN
01 Generate Images For The GAN (3:06)
02 Build A Training Step For The GAN (3:06)
03 Train The Gan (5:50)
04 Test The Gan (2:30)
Source Files
01 Generate Images For The GAN
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