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The Complete Machine Learning Online Course 4.0 + Bundle
LEVEL 1 - Learn to Code in Python: Fundamentals
00. Intro To Course And Python (9:57)
01. Variables (19:19)
02. Type Conversion Examples (10:06)
03. Operators (28:54)
04. Collections (8:25)
05. List Examples (19:41)
06. Tuples Examples (8:36)
07. Dictionaries Examples (14:26)
08. Ranges Examples (8:32)
09. Conditionals (6:43)
10. If Statement Examples (21:32)
11. Loops (29:42)
12. Functions (17:01)
13. Parameters And Return Values Examples (13:54)
14. Classes And Objects (34:11)
15. Inheritance Examples (17:29)
16. Static Members Examples (11:05)
17. Summary And Outro (4:08)
LEVEL 2 - Data Science with Python and NumPy
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)
Source Files
Machine Learning theory
00. Course Intro.mp4 (6:05)
01. Quick Intro to Machine Learning (9:01)
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
00. Course Intro (6:10)
01. Intro to Tensorflow.mov (5:33)
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 (7:22)
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)
Intro to Tensorflow Slides
Linear_Regression
Source Files
Review Sentiment Analysis
00. Course Intro (6:19)
01. How Machines Interpret Text (15:23)
02. Building the Model Part 1 - Examining Dataset (12:27)
03. Building the Model Part 2 - Formatting Dataset (15:14)
04. Building the Model Part 3 - Building the Model (10:30)
05. Building the Model Part 4 - Training the Model (5:42)
06. Building the Model Part 5 - Testing the Model.mp4 (9:26)
07. Course Summary and Outro (3:29)
Learn to Graph Data with Python and Matplotlib
00. Course Intro (5:30)
01. Intro to Pyplot (5:11)
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
Complete Beginners Data Analysis with Pandas and Python
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 (19:21)
06. Getting Properties of Series (13:04)
07. Modifying Series (19:02)
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. Reading CSVs (12:00)
16.Summary and Outro (4:14)
Source Files
Beginners R Programming: Data Science and Machine Learning
1st Hour - Intro to R (51:17)
2nd Hour- Control Flow and Core Concepts (64:28)
3rd Hour Matrices, Dataframes, Lists and Data ManipulationB (77:00)
4th Hour - GGplot and Intro to Machine learning (68:55)
5th Hour - Conclusion (47:25)
Source Code
R Programming: Practical Data Science and Modeling
1) 1st Hour - Course Overview and Data Setup (57:35)
2) 2nd Hour - Functions in R (54:57)
3) 3rd Hour - Regression Model (63:39)
4) 4th Hour - Regression Models Continued and Classification Models (57:04)
5) 5th Hour - Classification Models Continued, RMark Down and Excel (78:31)
Source Code
LEVEL 3 - Introduction to Machine Learning - Machine Learning Theory for Business - 01 What is Machine Learning
00 What Is Machine Learning (5:26)
01 Hash Table Or Dictionary Visualized With Time And Space Complexity (4:19)
02 Types Of Machine Learning (12:09)
03 What Is Supervised Learning (9:59)
04 What Is Unsupervised Learning (7:43)
05 How Does A Machine Learning Agent Learn (7:38)
06 Performance Of A Machine Learning Algorithm (4:14)
02 What machine learning can and cannot do
01 What Machine Learning Can And Cannot Do (11:27)
03a Machine learning model examples
01 What Is Linear Regression (4:37)
02 What Is Logistic Regression (3:54)
03 Make Decisions With Decision Trees (10:31)
03b Deep Learning and Neural Networks
01 What Is Deep Learning (5:44)
02 What Is A Neural Network (7:07)
04 What are machine learning libraries
01 What Are Machine Learning Libraries (11:59)
Microsoft Certified Azure Data Scientist Associate Preparation - 00a Course Overview
00a Course Overview - Microsoft Certified Azure Data Scientist Associate (3:09)
01 What is Microsoft Azure Machine Learning (3:24)
02 What is Microsoft Certified Azure Data Scientist Associate (5:10)
01 (Prerequisite) Introduction to Machine Learning
00A What Is Machine Learning (5:26)
00B Types Of Machine Learning Models (12:17)
00C What Is Supervised Learning (11:04)
01 How Does A Machine Learning Agent Learn (7:38)
02 Performance Of A Machine Learning Algorithm (4:14)
02 Introduction to Cloud Computing for Machine Learning
01 Why use the cloud for machine learning (2:38)
03 Public vs private cloud computing (3:18)
04 Managed vs unmanaged cloud computing (1:30)
05 IaaS vs PaaS vs SaaS in cloud computing (3:33)
06 Google Cloud vs AWS vs Azure for Machine Learning (3:32)
Source Files
03 Introduction to Azure Machine Learning
01 What is Azure Machine Learning studio (2:17)
Source Files
04 Build a cluster and pipeline in Azure Machine Learning
01 Build an Azure Machine Learning workspace (12:51)
02 Build a new compute cluster in Microsoft Azure ML (6:08)
03 Build a pipeline in Microsoft Azure ML Designer (4:25)
03a What is Azure Machine Learning designer (3:16)
Source Files
05 Build a dataset in Microsoft Azure ML Studio
01 Build a dataset in Microsoft Azure ML Designer (3:48)
02 Clean missing data in Microsoft Azure ML Designer (10:26)
03 Normalize data in Microsoft Azure ML Studio (4:24)
04 Run a data transformation pipeline in Microsoft Azure ML Designer (2:09)
Source Files
06 Build a regression machine learning model with Azure Machine Learning
00 What is Linear Regression (5:03)
01 Build a model training pipeline in Microsoft Azure ML Studio (5:03)
02 Evaluate a machine learning model in Microsoft Azure ML (7:08)
Source Files
Google Cloud Professional Machine Learning Engineer Certification - 00a Course Overview
Course Preview (4:02)
02a Introduction to Cloud Computing for Machine Learning
01 Why use the cloud for machine learning (2:38)
02 Benefits of cloud computing- (1:23)
03 Public vs private cloud computing (3:18)
04 Managed vs unmanaged cloud computing (1:30)
05 IaaS vs PaaS vs SaaS in cloud computing (3:33)
06 Google Cloud vs AWS vs Azure for Machine Learning (3:32)
Source Files
02b Image classification with AutoML and Vertex AI in Google Cloud
01 Build a Google Cloud project for machine learning (6:45)
02a What is a service account in Google Cloud Platform (1:59)
02b Build a service account and key in Google Cloud (6:52)
Source Code
02c image datasetclassification Cloud Storage
01 Image dataset for machine learning from Cloud Storage (2:12)
02 Build an image dataset for classification from a Cloud Storage bucket (5:36)
Source Code
02d Train an AutoML image classifier machine learning model
01 Train an AutoML image classifier machine learning model (6:27)
02 Deploy machine learning model to Cloud endpoint (3:38)
03 Make a prediction with a Cloud machine learning model (5:14)
Source Files
03 Build a streaming data pipeline in Google Cloud with BigQuery
01 Sign in to Google Cloud (2:46)
02 Build a BigQuery dataset in Google Cloud Console (8:24)
03 Build a Cloud Storage bucket in Google Cloud (8:15)
Source Files
04 Build data streaming Dataflow Pipeline with Google Cloud API
01 What is Dataflow API in Google Cloud (2:44)
02 What is PubSub in Google Cloud (4:24)
03 Build data streaming Dataflow Pipeline with Google Cloud API
Source Files
05 Query and visualize data with BigQuery SQL
01 Analyze streaming data with BigQuery Google Standard SQL (6:39)
02 Visualize BigQuery Cloud data with Google Data Studio (3:54)
Source Files
Machine Learning Fundamentals - 00b Course Overview
00 Course Overview - Machine Learning Fundamentals (13:46)
Source Files
01 Code Python on the Web
02.01 What is Google Colab (4:24)
02.02 What If I Get Errors (2:40)
02.03 How Do I Terminate a Session (2:40)
03 Probability and Statistics for Machine Learning
01 Probability And Information Theory Overview (5:15)
02 Combinatorics For Probability (8:44)
03 Law Of Large Numbers (10:38)
04 Calculate Center Of Distribution (7:40)
Source Files
04 Distributions in Machine Learning
01 Uniform Distribution (5:25)
02 Gaussian Distribution (3:45)
03 Log-Normal Distribution (3:28)
04 Exponential Distribution (3:04)
05 Laplace Distribution (1:54)
06 Binomial Distribution (9:05)
07 Multinomial Distribution (3:59)
08 Poisson Distribution (4:21)
Source Files
05 Machine Learning Optimization
01 Calculate Error Of Machine Learning Model (8:44)
Source Files
Data Engineering and Machine Learning Masterclass - 00b Course Overview
00 Course Overview (3:26)
Source Files - Course Overview
03 Load, clean and encode data
01 Load And Clean A Public Dataset (8:55)
01B What Is One-Hot Encoding (10:02)
02 Build X And Y Data With One Hot Encoding (4:57)
03 Logistic Regression With One Hot Encoding (2:20)
04 Data engineering for machine learning
04 Scale And Encode Data With Scikit-Learn (3:47)
04.04 What Is Scaling Data (6:36)
05 Build, Train And Test A Machine Learning Model (4:37)
05 Build regression and discretizer models
01 Compare Decision Tree And Linear Regression Models (6:26)
01C What Is The Kbins Discretizer (4:54)
02 Bin Data With Kbins Discretizer (3:42)
03 Compare Binned Regression Models (3:39)
04 Build A Linear Regression Model On Stacked Data (3:20)
05A What Is K Means Clustering (11:58)
06 Data transformation and feature selection for ridge regression
01 Build Univariate Nonlinear Transformatio (1:55)
01 What Is Gaussian Probability Distribution- (2:31)
01B What Is Poisson Distribution (1:08)
02 Build X and Y Data With Poisson Distribution In Numpy (3:34)
02C What Is Logarithmic Data Transformation (2:34)
03 Build A Ridge Regression Model (3:41)
Build Machine Learning Models - 00b Image Recognition with MNIST
00. Course Intro (6:57)
01. Intro to Image Recognition (6:40)
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
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 (13:24)
09. Summary and Outro (2:55)
Image Recognition with MNIST
Source Files
01 Build Beginner Models in TensorFlow 2.0
01 Course Overview-1 (3:30)
02 Build Models On The Web-2 (5:06)
Source Files
02 AI Uninformed Search Algorithms
01 What Are Search Algorithms (7:21)
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 (6:56)
04 Depth Limited Search (3:58)
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 (4:44)
Source Files
03 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
04 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-3 (10:50)
04 Performance Of A Machine Learning Algorithm-4 (4:13)
05 Handle Noise In Data-5 (5:20)
06 Statistical Learning-6 (3:56)
Source Files
05 Logistic Regression
05.01 What Is Logistic Regression-1 (4:26)
05.03 Prepare Data For Logistic Regression-2 (12:19)
05.03a How To Prepare Data-3 (8:52)
05.04 Build A Logistic Regression Model-4
05.04a How To Build A Logistic Regression Model-5 (3:28)
05.04b What Is Optimization-6 (12:10)
05.05 Optimize The Logistic Regression Model-7 (12:44)
05.05a How To Optimize A Logistic Regression Model-8
05.06 Train The Model-9 (10:09)
05.07 Test The Model-10
05.08 Visualize Results-11 (5:38)
05 Source Files
06 Gradient Boosted Classification
06.01 What Is Gradient Boosting-1 (1:54)
06.02 Prepare Data For Gradient Boosted Classification-2 (7:19)
06.03 Build Binary Classes-3 (6:12)
06.04a How To Shape Data For Classification-4 (2:58)
06.04b Shape Data For Classification-5 (7:06)
06.05a How To Build A Boosted Trees Classifier-6 (4:03)
06.05b Build A Boosted Trees Classifier-7 (4:37)
06 Source Files
07 Gradient Boosted Regression
07.01 Build Input Functions-1 (3:55)
07.02 Build A Boosted Trees Regressor-2 (3:02)
07.03 Train And Evaluate The Model-3 (4:07)
07 Source Files
08 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
LEVEL 4 - Deep Learning Neural Networks - Creative Machine Learning - Draw and Paint with 3 Neural Network Projects - Overview
00 Project Preview (1:47)
02 Project 2 Preview (1:06)
03 Project 3 Overview (0:47)
04 What You'll Need (2:43)
Source Files
02 Collect and Process Data
01 Load Drawings Dataset (10:03)
02 Label Data (12:17)
03 Build A Training Dataset (8:30)
04 Visualize Dataset (6:20)
05 Batch And Shuffle Data (4:39)
Source Files
03 Build a Generative Neural Network
01 Build A Generator (13:46)
02 Generate Noise (5:41)
Source Files
03a Generative Neural Network Fundamentals
01 What Is A Generative Neural Network (7:26)
02 What Is A Convolutional Neural Network (7:04)
03 How To Build A Convolutional Neural Network (14:04)
04 How To Build A Dense Layer (2:42)
05 How To Build A Batch Normalization Layer (1:52)
06 Leaky Relu Activation Function (6:04)
07 Transposed Convolution Layer (5:17)
08 Hyperbolic Tangent (Tanh) Activation Function (2:59)
Source Files
04 Build a Discriminator Neural Network
00 How Do You Build A Discriminator (10:19)
01 Build A Discriminator (10:53)
Source Files
05 Evaluate the Model's Performance
00 Performance Of A Machine Learning Algorithm (4:14)
01 Calculate Loss (7:11)
02 Assign Optimizers (3:02)
02A What Is The Adam Optimizer (6:55)
Source Files
06 Train the Model to Draw
01 Build A Training Step (11:03)
02 Train The Model (6:54)
03 Visualize Training (14:35)
Source Files
07 Test the Model's Drawing Ability
01 Test The Model (9:22)
Source Files
08 Build an Image Style Transfer Project
00 Style Transfer Project Overview (5:36)
01 Load The Model (4:57)
02 Load Images (6:53)
03 Reformat Image For Machine Learning (7:03)
04 Load Original And Style Images (6:27)
05 Display Processed Images (10:58)
06 Extract Image Features (6:59)
07 Calculate The Style Representation (6:01)
08 Optimize The Model (5:27)
09 Use Machine Learning To Transfer Image Style (13:54)
Source Files
09 Build an Image Approximation Project
01 Load And Process Image (7:14)
02 Build A Training Dataset (6:49)
03 Visualize Training Dataset (5:36)
04 Build A Testing Dataset (4:04)
05 Build A Neural Network (7:25)
06 Train The Neural Network (4:40)
07 Visualize Image Approximation Results (5:14)
Source Files
The Deep Learning Masterclass - Convert Sketch to Photo - Overview
01 Project Preview (2:19)
02 What You'll Need (2:44)
02 Data Processing
01 Load Dataset (11:07)
02 Process Photos And Sketches (15:05)
Source Files
03a Generative Neural Network Fundamentals
01 What Is A Generative Neural Network (7:26)
02 What Is A Convolutional Neural Network (7:04)
03 How To Build A Convolutional Neural Network (14:04)
04 How Do You Build A Generator (9:13)
Source Files
03b Build Neural Networks to Convert a Sketch to a Photograph
01 Build A Generator (16:48)
02 Build A Discriminator (9:07)
03 Build A Combined Model (4:05)
Source Files
04a Discriminator Neural Network Fundamentals
01 How Do You Build A Discriminator (8:48)
Source Files
04b Train the Model
01 Performance Of A Machine Learning Algorithm (4:14)
02 What Is Error (6:39)
03 What Is The Adam Optimizer (6:15)
04 Define Loss And Optimizers (11:18)
05 Build A Training Epoch (11:01)
Source Files
05 Test the Model
01 Test The Model (7:21)
02 How To Improve The Model (4:48)
Source Files
Machine Learning and Deep Learning for Biology with Python and TensorFlow - 01 Course Overview
00 Course Overview - Machine Learning For Biology (6:49)
01 What You'll Need (3:27)
Source files - Course overview
04 Regression Fundamentals - Theory Behind the Code
01 Regression Introduction (8:58)
02 What Is Regression (19:55)
03 What Is Linear Regression (5:03)
Source files
05 Build a K Nearest neighbors regression model to predict diabetes
00 Project Preview (2:14)
01 Load And Analyze Data (8:25)
01 What Is K Nearest Neighbours (8:07)
02 Build A K Nearest Neighbors Regression Model To Predict Diabetes (10:24)
Source files
06 Build Regression Machine Learning Models to Detect Diabetes
03A What Is The Random Forest Classifier Model (5:42)
03B Build More Regression Models And Find The Best One (4:08)
04 Select Top Features Via Variance Threshold (12:42)
05 Visualize Linear Regression With Matplotlib Pyplot (6:14)
Source Files
07 Data analysis and transformation on blood cell data
00 Project Preview (2:56)
01 Load And Analyze Blood Cell Data (10:39)
02 Clean Data With Missing Values (12:28)
03 Process Data For Machine Learning (8:53)
04A What Is Principal Component Analysis (7:27)
04B Reduce Data Dimensionality With Principal Component Analysis (4:55)
Source Files
08 Cluster blood cells based on fluorescent intensities
05A What Is Unsupervised Learning (8:17)
05B What Is K Means Clustering (11:58)
05C Build A Kmeans Clustering Model (12:23)
06 Visualize Clusters Found Via Kmeans (8:09)
Source files
09 Preprocess a malignant vs benign cancer mass dataset
00 Project Preview (2:22)
01 Load And Analyze Cancer Dataset (5:46)
02 Preprocess Cancer Data For Machine Learning (5:24)
Source files
10 Build an SVM model to classify malignant vs benign cancer mass
03A Why Do We Need Svm (7:15)
03B How Does Svm Work (6:28)
03C Svm On Non-Linear Data (4:48)
03D What Are Svm Kernels (4:44)
03E What Is The Precision-Recall Score (4:42)
03F Build An Svm Model To Classify Malignant Vs Benign Mass (4:08)
Source Files
11 Build a logistic regression model to classify malignant vs benign cancer mass
04A What Is Logistic Regression (4:32)
04B Build A Logistic Regression Model (3:44)
Source files
12 Improve model accuracy with tuning methods
01A What Is Cross Validation (8:25)
01B Find Model Error With Cross Validation (3:46)
02A What Is Grid Search Cross Validation (5:47)
02B Find Optimal Hyperparameters With Grid Search (9:37)
03A What Is Nested Cross Validation (14:29)
03B Find Best Model Parameters With Nested Cross Validation (4:43)
04A What Is The Decision Tree Model (10:51)
04B Compare Models With Nested Cross Validation (4:00)
Source Files
13 Prepare heart disease data for machine learning
01 Load Data Via Data File (11:11)
02 Clean And Preprocess Heart Disease Data For Machine Learning (11:44)
03 Process Heart Disease Data For Machine Learning (8:39)
Source Files
14 Predict heart disease with machine learning
04A What Is Stochastic Gradient Descent (11:28)
04B Build A Linear Classifier With Stochastic Gradient Descent (8:05)
05A What Is Ada Boost (5:48)
05B Build An Ada Boost Classifier (7:17)
06 Build A K Nearest Neighbors Machine Learning Model (8:03)
Source Files
16 Build a neural network to find malaria in cells
00 Project Preview (1:15)
01 Load Data Via Tensorflow (4:06)
02 Visualize Malaria Cell Images (8:49)
03 Extract A Subset Of Samples (8:03)
04 Build A Neural Network (5:41)
05 Train And Evaluate Model Accuracy (9:23)
Source Files
Text to Speech with Python Machine Learning, Deep Learning and Neural Networks - Overview
00 Course Overview - Text To Speech (1:13)
01 How Text To Speech Works (5:43)
02 What You'll Need - Text To Speech (3:25)
03 Super simple text to speech with Google Text to Speech
01 Convert Text To Speech With GTTS (5:45)
04 Text to speech with PyTorch, Tacotron 2 and WaveGlow
00 What Are Pytorch, Tacotron 2 And Waveglow (4:29)
01 Load Models (3:50)
02 Convert Text To Speech With Pytorch (7:45)
05 Text to speech with pyttsx3
00 What Is Pyttsx3 (1:20)
01 Load Available Voices (4:32)
02 Convert Text To Speech With Pyttsx3 (4:48)
LEVEL 5 - Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects
01 Course Overview - Opencv (4:51)
02 What You'll Need (2:38)
03 Analyze Images with OpenCV
01 Detect Edges In An Image (8:19)
02 Detect Contours In An Image (11:23)
03 Detect Corners In An Image (9:37)
04 Restore Images with Computational Photography
01 Restore A Damaged Image (18:57)
05 Detect objects in images
01 Detect Objects In An Image With Masking (15:39)
02 Detect Faces In Images (11:31)
03 Extract Foreground In An Image (18:30)
04 Find Object In Image With Template Matching (12:27)
06 Machine learning and neural networks introduction
01 What Is Machine Learning (5:26)
04 What Is Ml-Agents (5:16)
07 Read text in an image with OCR and Tesseract
01 Extract Text From An Image With Tesseract (13:31)
02 Improve Accuracy With Thresholding (8:10)
03 Change Perspective Of An Image With Foreign Text (15:30)
04 Extract Foreign Language Text From An Image (8:03)
08 Build your first neural network with OpenCV
01 Generate Data (7:09)
02 Build An Artificial Neural Network (9:16)
03 Visualize Model Results (14:30)
09 Object detection with OpenCV Deep Learning
01 Load Yolo Dnn Model (3:18)
02 Build A Neural Network With Opencv (7:44)
03 Print Out Detected Objects (6:44)
04 Outline Objects In The Original Image (21:57)
10 Outline objects in a video
01 Outline Objects In A Video (10:55)
02 Draw Contours On Video (16:30)
03 Save New Frames As A Video (5:04)
11 Detect faces in video
01 Load A Video From Drive (7:39)
02 Detect Faces In Video (10:32)
03 Detect Eyes In Video (6:40)
04 Save New Frames As A Video (7:40)
12 Track a color in videos
01 Track Color In A Video (20:06)
02 Save New Frames As A Video (7:14)
13 Detect lanes for autonomous vehicle computer vision
01 Load A Driving Dash Cam Video (4:05)
02 Process Each Video Frame (14:54)
03 Outline Lanes Detected (12:21)
04 Save New Frames As A Video (13:52)
14 Build a motion alert video monitoring system
01 Load A Video From Drive (5:28)
02 Detect Objects In A Video With Contours (10:05)
03 Detect When Motion Begins And Ends (15:17)
04 Record Each Time Motion Begins (16:36)
15 Detect emotion in a video
Detect Emotion In A Video (12:11)
16 Swap faces with machine learning
01 Load Images From The Web Into Colab (3:00)
02 Get Facial Landmarks From Image (11:55)
03 Build A Matrix From Landmark Points (10:08)
04 Draw A Mask Over Facial Landmarks (7:07)
05 Build A Warped Mask (4:09)
06 Combine Face Masks (8:15)
LEVEL 6 - ML App Development
01 What Is Machine Learning (5:27)
03 What Is Supervised Learning (10:39)
CoreML SwiftUI Masterclass - Machine Learning App Development - Overview
00 Course Overview (6:54)
01 What You'll Need (5:56)
02 What Is Coreml (6:43)
Source files
00b (Prerequisite) Introduction to Xcode
00. Intro And Demo-1 (6:48)
01. General Interface Intro-2 (15:06)
02. File System Introduction-3 (13:24)
03. Viewcontroller Intro-4 (6:53)
04. Storyboard File Intro-5 (17:28)
05. Connecting Outlets And Actions-6 (14:12)
06. Running An Application-7 (10:06)
07. Debugging An Application-8 (11:40)
XCode Intro
00c (Prerequisite) Swift Language Basics - 01. Variable and Constants
00. Language Basics Topics List (5:14)
00. Learning Goals (4:24)
01. Intro To Variables And Constants (16:16)
02. Primitive Types (19:07)
03. Strings (19:11)
04. Nil Values (13:16)
05. Tuples (14:39)
06. Type Conversions (23:40)
07. Assignment Operators (11:43)
08. Conditional Operators (12:51)
Variables and Constants Text.playground
02. Collection Types
00.Topics-List-And-Learning-Objectives (3:37)
01. Intro To Collection Types (10:57)
02. Creating Arrays (5:18)
03. Common Array Operations (25:26)
04. Multidimensional Arrays (8:03)
05. Ranges (9:59)
Collection Types Text.playground
03. Control flow
00. Topics List And Learning Objectives (4:06)
01. Intro To If And Else Statements (10:07)
02. Else If Statements (9:13)
03. Multiple Simultaneous Tests (12:58)
04. Intro To Switch Statements (9:47)
05. Advanced Switch Statement Techniques (16:25)
06. Testing For Nil Values (12:15)
07. Intro To While Loops (14:51)
08A. Intro To For...In Loops (14:39)
08B Intro To For...In Loops (Cont'd) (11:19)
09. Complex Loops And Loop Control Statements (20:05)
Control Flow Text.playground
04. Functions
00. Topics List And Learning Objectives (4:16)
01. Intro To Functions (20:19)
02. Function Parameters (12:01)
03. Return Statements (14:26)
04A. Parameter Variations - Argument Labels (9:23)
04B. Parameter Variations - Default Values (5:50)
04C. Parameters Variations - Inout Parameters (9:03)
04D. Parameter Variations - Variadic Parameters (11:12)
05. Returning Multiple Values Simultaneously (7:46)
Functions Text.playground
05. Classes, Struct and Enums
00. Topics List And Learning Objectives (5:25)
01. Intro To Classes (16:24)
02A. Properties As Fields - Add To Class Implementation (13:43)
02B. Custom Getters And Setters (8:44)
02C. Calculated Properties (24:12)
02D. Variable Scope And Self (13:15)
02E. Lazy And Static Variables (14:35)
03A. Behaviour And Instance Methods (16:38)
03B. Class Type Methods (7:42)
04. Class Instances As Field Variables (8:52)
05A. Inheritance, Subclassing And Superclassing (24:06)
05B. Overriding Initializers (13:41)
05C. Overriding Properties (16:30)
05D. Overriding Methods (10:33)
06. Structs Overview (20:24)
07. Enumerations (16:30)
08. Comparisons Between Classes, Structs And Enums (14:40)
Source files
00d (Prerequisite) Introduction to SwiftUI - 01 SwiftUI Overview
01 What Is SwiftUI (4:54)
Source Files
02 Build an App with Basic View Objects
01 Build A Text Object-1 (9:25)
02 Build An Image Object-2 (3:41)
03 Add An Image From The Web-3 (3:32)
04 Add An Image From The Web-4 (10:32)
05 Build A Button-5 (5:07)
06 Build A Toggle Button-6 (7:09)
07 Build A Slider-7 (9:09)
08 Build A View From A Collection-8 (7:10)
Source Files
03 Build Layout Objects
01 Customize Stack Layouts-1 (4:29)
02 Control Spacing Around Views-2 (7:55)
03 Force Views To One Side-3 (5:29)
04 Layer Views On Top Of Each Other-4 (4:50)
Source Files
04 Build Events
01 Read Text From A Textfield (6:23)
02 Build A Secure Password Field (4:49)
03 Read Values From A Slider (3:26)
Source Files
01 Sentiment analysis with Natural Language CoreML
00A What Is Sentiment Analysis (4:39)
00B Natural Language Framework (4:32)
01 Build A New Swiftui App For Sentiment Analysis (8:59)
02 Perform Sentiment Analysis In SwiftUI (7:38)
03 Change Color Depending On Sentiment (4:56)
Source files
02 Tabular Classification with CreateML
01 Train A Model With CreateML (12:13)
02 Test The Model With CoreML In An App (14:17)
03 Display Prediction Accuracy (6:41)
04c Source files
04 Classify images with SwiftUI app and MobileNetV2 neural network
01 Load A CoreML Model Into A New Xcode Project (11:00)
02 Add Images For Classification (6:31)
03 Enable User To Loop Through Image (5:40)
04 Import CoreML Model Into The View (5:28)
05 Resize Image For Model (6:26)
05A Resizing Image Overview (7:44)
06 Convert Image To Buffer For Model (8:55)
06A Image To Buffer Overview (6:55)
07 Test The Model On Image Classification (14:31)
Source files
05 Object detection with Swift Playgrounds and YOLOv3
00 Tip - How To Unhide Library Folder (1:22)
01 Build A New Xcode Project To Compile Model (4:44)
02 Build A Playground With Object Detection Model (4:28)
03 Instantiate A Model Object (6:12)
04 Build An Image Analysis Request (7:23)
05 Resize Image For Model (9:36)
06 Convert Image To Buffer For Model (9:47)
07 Test Object Detection On Image (4:53)
Source Files
Python and Android TensorFlow Lite - Machine Learning for App Development - Overview
00 Course Overview (3:12)
01 What You'll Need (4:29)
Source files
01 (Prerequisite) Introduction to Machine Learning
00 What Is Python (4:47)
00D What Is Linear Regression (5:03)
01 What Is Machine Learning (5:26)
02 What Is Supervised Learning (11:03)
Source Files
02 Introduction to Python (Prerequisite)
00. Intro To Course And Python (9:57)
01. Variables (19:19)
02. Type Conversion Examples (10:06)
03. Operators (28:54)
04. Collections (8:25)
05. List Examples (19:41)
06. Tuples Examples (8:36)
07. Dictionaries Examples (14:26)
08. Ranges Examples (8:32)
09. Conditionals (6:43)
10. If Statement Examples (21:32)
11. Loops (29:42)
12. Functions (17:01)
13. Parameters And Return Values Examples (13:54)
14. Classes And Objects (34:11)
15. Inheritance Examples (17:29)
16. Static Members Examples (11:05)
17. Summary And Outro (4:08)
03 (Prerequisite) Introduction to Android Studio
00. Introduction (3:27)
01. Downloading And Installing Android Studio (6:53)
02. Exploring Android Studio Interface (12:59)
03. Understanding File Hierarchy (12:19)
04. Exploring Activity-Layout Relationship (19:36)
05. Setting Up An Emulator (7:01)
06. Running App And Implementing User Interaction (6:45)
07. Debugging An App (6:11)
08. Summary And Outro (4:07)
04a (Prerequisite) Introduction to Kotlin
00. Introduction (6:12)
01. Introduction To Variables (7:04)
02. Basic Operations (9:18)
03. Nullable Variables (5:24)
04. Collections Intro (7:27)
05. Mutable Lists And Arrays (6:53)
06. If Statements And Expressions (8:11)
07. When Statements And Expressions (3:30)
08. While Loops (6:46)
09. For In Loops (4:55)
10. Introduction To Functions (7:55)
11. Functions With Parameters And Return Values (7:29)
12. Classes And Objects Introductions (16:37)
13. Subclassing And Superclassing (13:12)
14. Summary And Outro (4:41)
Source FIles
04b Linear regression from scratch
00 Project Preview (2:17)
01 Build A Linear Regression Model With Python (15:06)
02 Convert Python Model To Tensorflow Lite (5:38)
03 Build A New Android Studio App (7:39)
04 Build App Layout (10:18)
05 Load Machine Learning Model (4:53)
06 Use Machine Learning Model (5:18)
Source files
05 Build a linear regression model with Python for TF Lite
01 Build A Linear Regression Model With Python (15:06)
02 Convert Python Model To Tensorflow Lite (5:38)
Source files 05
06 Build an Android app with TensorFlow machine learning model
03 Build A New Android Studio App (7:39)
04 Build App Layout (10:18)
Source files 06
07 Load and use model with Kotlin
05 Load Machine Learning Model (4:53)
06 Use Machine Learning Model (5:18)
Source files 07
08 Classification
00 Project Preview (1:49)
00 What Is Logistic Regression (4:32)
09 Build a logistic regression model with TensorFlow Keras
01 Load And Process Data For Logistic Regression With Scikit Learn (9:14)
02 Build A Logistic Regression Model With Python (8:01)
03 Convert Logistic Regression Model To Tensorflow Lite (2:38)