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Machine Learning Masterclass [Free Online Course] - TechCracked

Machine Learning Masterclass

Top University Professor

This course includes
  • 54 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV


What you'll learn
  • Hypothesis Space and Inductive Bias
  • Evaluation and Cross-Validation
  • Linear Regression
  • Learning Decision Tree
  • Python Exercise on Decision Tree and Linear Regression
  • and Much MUch More!!


Description

In this course we will have a quick introduction to machine learning and this will not be very deep in a mathematical sense but it will have some amount of mathematical trigger and what we will be doing in this course is covering different paradigms of machine learning and with special emphasis on classification and regression tasks and also will introduce you to various other machine learning paradigms. In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning. )

A brief introduction with emphasis on brief right, so the rest of the course would be a more elongated introduction to machine learning right.


Curriculum

Course A

Week 1:
Introduction
Different types of Learning
Hypothesis Space and Inductive Bias
Evaluation and Cross-Validation

Week 2:
Linear Regression
Introduction to Decision Trees
Learning Decision Tree
Overfitting
Python Exercise on Decision Tree and Linear Regression

Week 3:
k-Nearest Neighbour
Feature Selection
Feature Extraction
Collaborative Filtering
Python Exercise on kNN and PCA

Week 4:
Bayesian Learning
Naive Bayes
Bayesian Network
Python Exercise on Naive Bayes

Week 5:
Logistic Regression
Introduction Support Vector Machine.
SVM _ The Dual Formulation
SVM _ Maximum Margin with Noise
Nonlinear SVM and Kernel Function
SVM _ Solution to the Dual Problem
Python Exercise on SVM.

Week 6:
Introduction.
Multilayer Neural Network
Neural Network and Backpropagation Algorithm
Deep Neural Network
Python Exercise on Neural Network

Week 7:
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Week 8:
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Week 9:
Agglomerative Hierarchical Clusteringd
Python Exercise on kmeans clustering

Course B
Week 1:
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning

Week 2:
Probability Basics
Probability Basics part 2

Week 3:
Linear Algebra
Linear Algebra part 2

Week 4:
Statistical Decision Theory - Classification
part two
Bias Variance

Week 5:
Linear Regression
Multivarite Regression

Week 6:
Subset Selection
Subset Selection part 2
Shrinkage Methods
Principal Components
Partial least squeres

Week 7:
Sampling
Linear Classification
Student
Logistic Regression
Sample
Linear Discrinimant
Confidence intervals

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