Advance Machine Learning

Machine Learning

  • 5.0 (1 Reviews) , 1 students enrolled

Course Overview

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots).This course covers the theory and practical algorithms for machine learning from a variety of perspectives.We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning.  

What are the requirements?

  • Students entering the class are expected to have a pre-existing working knowledge of probability, linear algebra, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.
  • In addition, recitation sessions will be held to review some basic concepts.

What am I going to get from this course?

  • The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor.
  • Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice.
  • This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.

What is the target audience?

  • Anyone who is interested in learning Machine Learning.
  • Anyone who wishes to add value to their business using Machine Learning tools.

About the Author

We at SkillRary strive to provide simple yet powerful training or tuition on all domains. This organization has started with a mindset to share the knowledge that the internet or an individual has in a progressive manner. SkillRary is an online training programme, trying to get the best content for all on a very low cost and thereby helping everyone with a digital schooling and online education.  

SkillRary provides computer based training (CBT), distance learning or e-learning, that takes place completely on the internet. The courses involve a variety of multimedia elements, including graphics, audio, video, and web-links which can be accessed to the enrolled clients.

In addition to presenting course materials and content, SkillRary gives the students the opportunity for live interactions and real-time feedback in the form of quizzes and tests. Interactions between the instructor and students are also conducted via chat, e-mail or other web-based communication. Unlike any other, we here also let the students know which module has to be gone through first. All the modules are placed according to the lesson plans so that students will know what to refer first.

SkillRary is self-paced and customizable to suit an individual's specific learning needs. Therefore it can be conducted at any time and place, provided there is a computer or smartphone with high-speed internet access. This makes it very convenient to the users who can modify their training to fit into their day-to-day schedule. All our users will be able to use our eLearning system to its full capacity.

Course Curriculum

Table of Content
1 Video Lectures | 00:06:59

  • Table of Content
    06:59
     

Machine Learning Introduction
4 Video Lectures | 1 Quiz | 00:55:08

  • Introduction
    16:18
     
  • Well posed learning probles
    09:20
     
  • Desgning a learning system
    25:13
     
  • issune in machine learning
    04:17
     
  • Quiz
    5 Questions
     

Concept Learning
10 Video Lectures | 1 Quiz | 01:35:11

  • concept learning
    11:57
     
  • general to specific ordering of hypotheses
    09:28
     
  • find S algorithm
    14:16
     
  • version space
    03:49
     
  • list then eliminate algorithm
    10:56
     
  • candidate elimination algorithm
    09:01
     
  • candidate elimination algorithm Weather example
    17:31
     
  • Remarks on Version Space
    08:34
     
  • unbiased learner
    02:12
     
  • Inductive learner
    07:27
     
  • Quiz
    5 Questions
     

Decision Tree Learning
12 Video Lectures | 1 Quiz | 02:15:29

  • Decision Tree
    06:23
     
  • Decision Tree Representation
    10:40
     
  • Problems for Decision Tree Learning
    10:20
     
  • Entropy
    07:25
     
  • Information Gain
    16:01
     
  • ID3 Algorithm
    08:29
     
  • IDE weather Example
    31:55
     
  • IDE Classification Example
    05:27
     
  • Some Insight of DL
    04:05
     
  • Inductive Bias of DL
    06:41
     
  • Restriction Biases and Preference Biases
    05:49
     
  • Issues in Decision Tree
    22:14
     
  • Quiz
    5 Questions
     

Perceptrons
5 Video Lectures | 1 Quiz | 01:11:34

  • Introduction to Perceptrons
    18:52
     
  • Activation Function
    10:27
     
  • Backpropagation
    11:58
     
  • Backpropagation XOR Example
    20:03
     
  • Backpropagation Numerical Example
    10:14
     
  • Quiz
    10 Questions
     

Bayesian Networks
5 Video Lectures | 1 Quiz | 00:47:31

  • Bayesian Networks
    15:54
     
  • Conditional probability table (CPTs)
    07:24
     
  • Burglary-Alarm Example
    10:49
     
  • why Bayesian Networks
    04:43
     
  • Condition Independence
    08:41
     
  • Quiz
    5 Questions
     

Bayesian Learning
13 Video Lectures | 1 Quiz | 01:36:28

  • Three Approaches of Classification
    04:23
     
  • Basics of Bayesian Learning
    12:32
     
  • Bayes Theorem
    06:21
     
  • Maximum likelihood Estimate
    04:01
     
  • Patient Example
    04:55
     
  • Coin toss Example
    07:53
     
  • Relation to concept learnig Example
    03:31
     
  • Evolution of Posterior probabilities
    05:22
     
  • Predict Probabilities
    02:57
     
  • H(MAP)
    05:06
     
  • Minimum Description Length Principle
    03:02
     
  • Bayes optimal Classifier Example
    26:57
     
  • Bayesian Belif Networks
    09:28
     
  • Quiz
    5 Questions
     

Machine Learning Algorithms
5 Video Lectures | 1 Quiz | 01:26:06

  • K-Nearest Neighbors Algorithm
    26:44
     
  • Use-Case
    10:48
     
  • Naïve Bayes Algorithm
    25:01
     
  • Works
    16:07
     
  • Zero freuency problem
    07:26
     
  • Quiz
    5 Questions
     

reviews

  • Sonnet Paul
    This course is highly recommended for someone who wants to learn ML concepts in deep.