Dive into a comprehensive 19-hour tutorial series on machine learning with Python. Learn regression techniques, K-Nearest Neighbors, Support Vector Machines, clustering methods, and deep learning with neural networks. Explore practical applications using TensorFlow, including recurrent and convolutional neural networks. Gain hands-on experience by building models from scratch, working with real-world datasets like Titanic, and tackling advanced projects such as training a neural network to play games and identifying dogs vs cats using CNNs. Conclude with an in-depth look at 3D Convolutional Neural Networks applied to medical imaging in a Kaggle lung cancer detection competition.
Overview
Syllabus
Practical Machine Learning Tutorial with Python Intro p.1.
Regression Intro - Practical Machine Learning Tutorial with Python p.2.
Regression Features and Labels - Practical Machine Learning Tutorial with Python p.3.
Regression Training and Testing - Practical Machine Learning Tutorial with Python p.4.
Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5.
Pickling and Scaling - Practical Machine Learning Tutorial with Python p.6.
Regression How it Works - Practical Machine Learning Tutorial with Python p.7.
How to program the Best Fit Slope - Practical Machine Learning Tutorial with Python p.8.
How to program the Best Fit Line - Practical Machine Learning Tutorial with Python p.9.
R Squared Theory - Practical Machine Learning Tutorial with Python p.10.
Programming R Squared - Practical Machine Learning Tutorial with Python p.11.
Testing Assumptions - Practical Machine Learning Tutorial with Python p.12.
Classification w/ K Nearest Neighbors Intro - Practical Machine Learning Tutorial with Python p.13.
K Nearest Neighbors Application - Practical Machine Learning Tutorial with Python p.14.
Euclidean Distance - Practical Machine Learning Tutorial with Python p.15.
Creating Our K Nearest Neighbors Algorithm - Practical Machine Learning with Python p.16.
Writing our own K Nearest Neighbors in Code - Practical Machine Learning Tutorial with Python p.17.
Applying our K Nearest Neighbors Algorithm - Practical Machine Learning Tutorial with Python p.18.
Final thoughts on K Nearest Neighbors - Practical Machine Learning Tutorial with Python p.19.
Support Vector Machine Intro and Application - Practical Machine Learning Tutorial with Python p.20.
Understanding Vectors - Practical Machine Learning Tutorial with Python p.21.
Support Vector Assertion - Practical Machine Learning Tutorial with Python p.22.
Support Vector Machine Fundamentals - Practical Machine Learning Tutorial with Python p.23.
Support Vector Machine Optimization - Practical Machine Learning Tutorial with Python p.24.
Creating an SVM from scratch - Practical Machine Learning Tutorial with Python p.25.
SVM Training - Practical Machine Learning Tutorial with Python p.26.
SVM Optimization - Practical Machine Learning Tutorial with Python p.27.
Completing SVM from Scratch - Practical Machine Learning Tutorial with Python p.28.
Kernels Introduction - Practical Machine Learning Tutorial with Python p.29.
Why Kernels - Practical Machine Learning Tutorial with Python p.30.
Soft Margin SVM - Practical Machine Learning Tutorial with Python p.31.
Soft Margin SVM and Kernels with CVXOPT - Practical Machine Learning Tutorial with Python p.32.
SVM Parameters - Practical Machine Learning Tutorial with Python p.33.
Clustering Introduction - Practical Machine Learning Tutorial with Python p.34.
Handling Non-Numeric Data - Practical Machine Learning Tutorial with Python p.35.
K Means with Titanic Dataset - Practical Machine Learning Tutorial with Python p.36.
Custom K Means - Practical Machine Learning Tutorial with Python p.37.
K Means from Scratch - Practical Machine Learning Tutorial with Python p.38.
Mean Shift Intro - Practical Machine Learning Tutorial with Python p.39.
Mean Shift with Titanic Dataset - Practical Machine Learning Tutorial with Python p.40.
Mean Shift from Scratch - Practical Machine Learning Tutorial with Python p.41.
Mean Shift Dynamic Bandwidth - Practical Machine Learning Tutorial with Python p.42.
Deep Learning with Neural Networks and TensorFlow Introduction.
Installing TensorFlow (OPTIONAL) - Deep Learning with Neural Networks and TensorFlow p2.1.
TensorFlow Basics - Deep Learning with Neural Networks p. 2.
Neural Network Model - Deep Learning with Neural Networks and TensorFlow.
Running our Network - Deep Learning with Neural Networks and TensorFlow.
Processing our own Data - Deep Learning with Neural Networks and TensorFlow part 5.
Preprocessing cont'd - Deep Learning with Neural Networks and TensorFlow part 6.
Training/Testing on our Data - Deep Learning with Neural Networks and TensorFlow part 7.
Using More Data - Deep Learning with Neural Networks and TensorFlow part 8.
Installing the GPU version of TensorFlow for making use of your CUDA GPU.
Installing CPU and GPU TensorFlow on Windows.
Recurrent Neural Networks (RNN) - Deep Learning with Neural Networks and TensorFlow 10.
RNN Example in Tensorflow - Deep Learning with Neural Networks 11.
Convolutional Neural Networks Basics - Deep Learning withTensorFlow 12.
Convolutional Neural Networks with TensorFlow - Deep Learning with Neural Networks 13.
TFLearn - Deep Learning with Neural Networks and TensorFlow p. 14.
Intro - Training a neural network to play a game with TensorFlow and Open AI.
Training Data - Training a neural network to play a game with TensorFlow and Open AI p.2.
Training Model - Training a neural network to play a game with TensorFlow and Open AI p.3.
Testing Network - Training a neural network to play a game with TensorFlow and Open AI p.4.
Intro and preprocessing - Using Convolutional Neural Network to Identify Dogs vs Cats p. 1.
Building the Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 2.
Training - Using Convolutional Neural Network to Identify Dogs vs Cats p. 3.
Using our Network - Using Convolutional Neural Network to Identify Dogs vs Cats p. 4.
Introduction - 3D Convolutional Neural Network w/ Kaggle Lung Cancer Detection Competiton p.1.
Reading Files - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.2.
Visualizing - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.3.
Resizing Data - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.4.
Preprocessing data - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.5.
Running the Network - 3D Convolutional Neural Network w/ Kaggle and 3D medical imaging p.6.
Taught by
sentdex
