Introduction to Machine Learning
•Course Introduction
•Installing Dependencies
•Introduction to Supervised Learning
•Introduction to Unsupervised and Reinformcement Learning
•Introduction to Deep Learning
•Linear Regression with Machine Learning
•Introduction to Linear Regression using Machine Learning
•Understanding the Linear Regression Process
•Coding a Linear Regression with Machine Learning Model
•Visualizing Linear Regression with Machine Learning
•Problem
•Answer
•Random Forest Modeling
•Introduction to Random Forest Models
•Understanding Decision Trees
•Coding a Random Forest Model
•Visualizing a Random Forest Decision Tree
•Support Vector Machines
•Introduction to Support Vector Machines
•Understanding the SVM Kernel
•Coding a SVM Model
•Visualizing Classification Boundaries
•Naive Bayes
•Introduction to Naive Bayes
•Understanding Bayesian Probability
•Coding with Natural Language Processing
•Building Naive Bayes Classifier with NLP
•Validation Techniques
•Over and Under fitting
•Cross Validation Techinques
•Coding Cross Validation techniques
•K-Nearest Neighbors
•Introduction to KNNs
•Distance Measurements and KNNs
•Building a KNN Model
•Calculating Squared Error and Learning with KNN
•K-Means Clustering
•Introduction to Unsupervised Learning and K-Means Clustering
•Introduction to Heirarchical K-Means Clustering
•Building a K-Means Clustering Model
•Visualizing a Dendrogram
•Hidden Markov Models
•Introduction to Markov Chains
•Introduction to Latent Variables and HMM
•Coding a simple HMM
•Gaussian Mixture Models
•Introduction to GMMs and Distributions
•GMMs and Joint Probability Distributions
•Building a Simple GMM
•Visualizing Boundary Spaces with GMMs
•Collaborative Filtering
•Introduction to Collaborative Filtering
•Introduction to Model-Based CFs and Matrix Factorization
•Building a Memory Based CF Model
•Building a Model Based CF Model
•Project 1
•Project 2
•Project 3
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