EE-Learning
Handling Non-Numeric Data
Catogry:
Computing
Subject:
Computer Programming
Course:
Practical Machine Learning
Lecture List
TFLearn
Convolutional Neural Networks with TensorFlow
Convolutional Neural Networks Basics
RNN Example in Tensorflow
Recurrent Neural Networks (RNN)
Installing CPU and GPU TensorFlow on Windows
Installing the GPU version of TensorFlow for making use of your CUDA GPU
Using More Data
Training/Testing on our Data
Preprocessing cont'd
Processing our own Data
Running our Network
Neural Network Model
TensorFlow Basics
Installing TensorFlow (OPTIONAL)
Deep Learning with Neural Networks and TensorFlow Introduction
Mean Shift Dynamic Bandwidth
Mean Shift from Scratch
Mean Shift with Titanic Dataset
Mean Shift Intro
K Means from Scratch
Custom K Means
K Means with Titanic Dataset
Handling Non-Numeric Data
Clustering Introduction
SVM Parameters
Soft Margin SVM and Kernels with CVXOPT
Soft Margin SVM
Why Kernels
Kernels Introduction
Completing SVM from Scratch
Completing SVM from Scratch
SVM Optimization
SVM Training
Creating an SVM from scratch
Support Vector Machine Optimization
Support Vector Machine Fundamentals
Support Vector Assertion
Understanding Vectors
Support Vector Machine Intro and Application
Final thoughts on K Nearest Neighbors
Applying our K Nearest Neighbors Algorithm
Writing our own K Nearest Neighbors in Code
Creating Our K Nearest Neighbors Algorithm
Euclidean Distance
K Nearest Neighbors Application
Classification w/ K Nearest Neighbors Intro
Testing Assumptions
Programming R Squared
R Squared Theory
How to program the Best Fit Line
How to program the Best Fit Slope
Regression How it Works
Pickling and Scaling
Regression forecasting and predicting
Regression Training and Testing
Regression Features and Labels
Python p.2
Python Intro p.1