Techie Lessons

Welcome to this free online class on machine learning. Machine learning is one of the most exciting recent technologies. And in this class, you learn about the state of the art and also gain practice implementing and deploying these algorithms yourself. You've probably use a learning algorithm dozens of times a day without knowing it. Every time you use a web search engine like Google or Bing to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time you use Facebook or Apple's photo typing application and it recognizes your friends' photos, that's also machine learning. Every time you read your email and your spam filter saves you from having to wade through tons of spam email, that's also a learning algorithm.
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This is one of the best Machine learning course created by Sir, Andrew Ng. It provides complete overview and deep understanding in Machine Learning Field. You can take this free course from :::: https://www.coursera.org/learn/machin...
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This video is intended to help students to learn machine Learning or to provide quick overview of complete picture of machine learning those who are experts.
All credit to my Lecturer: Andrew Ng
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Topic Covered:
What is Machine Learning?
Supervised Learning
Unsupervised Learning
Linear Regression with One Variable
Model Representation
Model Representation
Cost Function
Cost Function - Intuition II
Gradient Descent
Gradient Descent Intuition
ReadingGradient Descent Intuition
Gradient Descent For Linear Regression
Linear Algebra Review
Matrices and Vectors
Matrix Vector Multiplication
Matrix Vector Multiplication
Matrix Matrix Multiplication
Matrix Multiplication Properties
Inverse and Transpose
Multivariate Linear Regression
Multiple Features
Gradient Descent for Multiple Variables
Gradient Descent in Practice I - Feature Scaling
ReadingGradient Descent in Practice I - Feature Scaling
Gradient Descent in Practice II - Learning Rate
Gradient Descent in Practice II - Learning Rate
Features and Polynomial Regression
Computing Parameters Analytically
Normal Equation Noninvertibility
Basic Operations
Moving Data Around
Computing on Data
Plotting Data
Control Statements: for, while, if statement
Vectorization
Classification and Representation
Hypothesis Representation
Decision Boundary
Logistic Regression Model
Cost Function
Simplified Cost Function and Gradient Descent
Advanced Optimization
Multiclass Classification
Problem of Overfitting
Cost Function
Regularized Linear Regression
Regularized Logistic Regression
Neural Networks
Model Representation I
Model Representation II
Examples and Intuitions I
Multiclass Classification
Multiclass Classification
Cost Function
Backpropagation Algorithm
Backpropagation Intuition
Implementation Note: Unrolling Parameters
Gradient Checking
Random Initialization
Application of Neural Networks
Evaluating a Learning Algorithm
Model Selection and Train/Validation/Test Sets
Bias vs. Variance
Regularization and Bias/Variance
Large Margin Classification
Optimization Objective
Large Margin Intuition
Mathematics Behind Large Margin Classification
SVMs in Practice
Clustering
Unsupervised Learning: Introduction
K-Means Algorithm
Random Initialization
Choosing the Number of Clusters
Density Estimation
Gaussian Distribution
Building an Anomaly Detection System
Anomaly Detection vs. Supervised Learning
Multivariate Gaussian Distribution (Optional)
Anomaly Detection using the Multivariate Gaussian Distribution

Machine Learning A To Z Complete Course by Andrew Ng

Machine Learning A To Z Complete Course by Andrew Ng

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