Autoplay
Autocomplete
Previous Lesson
Complete and Continue
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
02 Visualize Data For Image To Image Translation
Lesson content will be unlocked within 30 minutes.
Teachable is working on this bug.
No further action will be required on your part
.
Thank you for your patience