Online training of Machine Learning

Machine Learning

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods);

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Online training of Machine Learning
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Course Details

Duration: 30 hours Effort:5 hours per week

Price With GST: ₹23600/-

Subject: Data Science Level: Beginner
Prerequisites
Students are expected to have the following background: Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. Familiarity with the probability theory. Familiarity with linear algebra.
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About this course

What you'll learn

01: Introduction - Introduction to the course - What is machine learning? - Supervised learning - introduction - Unsupervised learning - introduction

02: Regression Analysis and Gradient Descent - Linear Regression - Linear regression - implementation (cost function) - A deeper insight into the cost function - simplified cost function - Gradient descent algorithm - So no need to change alpha over time - Linear regression with gradient descent

03: Linear Algebra - review - Matrices - overview - Vectors - overview - Matrix manipulation - Implementation/use - Matrix multiplication properties - Inverse and transpose operations

04: Linear Regression with Multiple Variables - Linear regression with multiple features - Gradient descent for multiple variables - Gradient Decent in practice: 1 Feature Scaling - Learning Rate a - Features and polynomial regression - Normal equation

05: Logistic Regression - Classification - Hypothesis representation - Decision boundary - Non-linear decision boundaries - Cost function for logistic regression - Simplified cost function and gradient descent - Advanced optimization - Multiclass classification problems

06: Regularization - The problem of overfitting - Cost function optimization for regularization - Regularized linear regression - Regularization with the normal equation - Advanced optimization of regularized linear regression

07: Neural Networks - Representation - Neural networks - Overview and summary - Model representation 1 - Model representation II - Neural network example - computing a complex, nonlinear function of the input - Multiclass classification

08: Neural Networks - Learning - Neural network cost functionx - Summary of what's about to go down - Back propagation algorithm - Back propagation intuition - Implementation notes - unrolling parameters (matrices) - Gradient checking - Random initialization - Putting it all together

09: Advice for applying machine learning techniques - Deciding what to try next - Evaluating a hypothesis - Model selection and training validation test sets - Diagnosis - bias vs. variance - Regularization and bias/variance - Learning curves

10: Machine Learning System Design - Machine learning systems design - Prioritizing what to work on - spam classification example - Error metrics for skewed analysis - Trading off precision and recall - Data for machine learning

11: Support Vector Machines - Support Vector Machine (SVM) - Optimization objective - Large margin intuition - Large margin classification mathematics (optional) - Kernels - 1: Adapting SVM to non-linear classifiers - Kernels II

12: Clustering - Unsupervised learning - introduction - K-means algorithm - K means optimization objective - How do we choose the number of clusters?

13: Dimensionality Reduction - Motivation 1: Data compression - Motivation 2: Visualization - Principle Component Analysis (PCA): Problem Formulation - PCA Algorithm - Reconstruction from Compressed Representation - Choosing the number of Principle Components - Advice for Applying PCA

14: Anomaly Detection - Anomaly detection - problem motivation - The Gaussian distribution (optional) - Anomaly detection algorithm - Developing and evaluating and anomaly detection system - Anomaly detection vs. supervised learning - Choosing features to use - Multivariate Gaussian distribution - Applying multivariate Gaussian distribution to anomaly detection

15: Recommender Systems - Recommender systems - introduction - Content based recommendation - Collaborative filtering - overview - Collaborative filtering Algorithm - Vectorization: Low rank matrix factorization - Implementation detail: Mean Normalization

16: Large Scale Machine Learning - Learning with large datasets - Stochastic Gradient Descent - Mini Batch Gradient Descent - Stochastic gradient descent convergence - Online learning - Map reduce and data parallelism

17: Application Example - Photo OCR - Problem description and pipeline - Sliding window image analysis

18: Course Summary

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