Machine Learning

Machine Learning
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Machine Learning

Machine Learning Introduction

Topics:
  • Well-Posed Learning Problems
  • Designing a Learning System:
    • Choosing the Training Experience;
    • Choosing the Target Function;
    • Choosing a Representation for the Target Function;
    • Choosing a Function Approximation Algorithm;
    • The Final Design
  • Perspectives and issues in machine learning.

Concept Learning

Topics:
  • Notation
  • The Inductive Learning Hypothesis
  • Concept Learning as Search
  • FIND-S: Algorithm for finding a Maximally Specific Hypothesis:
    • Version Spaces and the CANDIDATE-ELIMINATION Algorithm
    • Convergence of CANDIDATE-ELIMINATION Algorithm to the correct Hypothesis
    • Appropriate Training Examples for learning
    • Applying Partially Learned Concept,
  • Inductive Bias: A Biased Hypothesis Space
    • An Unbiased Learner
    • The Futility of Bias-Free Learning.

Decision Tree Learning

Topics:
  • Decision Tree Representation
  • Appropriate problems for decision tree learning
  • The basic decision tree Learning Algorithm
  • Hypothesis Space Search in decision tree learning, Inductive Bias in Decision Tree Learning
  • Issues in Decision Tree Learning:
    • Over fitting the Data
    • Incorporating Continuous-Valued Attributes
    • Alternative Measures for Selecting Attributes
    • Handling Training Examples with Missing Attribute Values
    • Handling Attributes with differing Costs.

Evaluating Hypotheses

Topics:
  • Estimating Hypothesis Accuracy
    • Sample Error and True Error
    • Confidence Intervals for Discrete-Valued Hypotheses.
    • Basics of Sampling Theory:
      • Error Estimation and Estimating Binomial Proportions
      • The Binomial Distribution
      • Mean and Variance
      • Estimators, Bias and Variance
      • Confidence Intervals
      • Two-sided and one-sided bounds. A General approach for deriving confidence intervals: Central Limit Theorem.
      • Difference in Error of two hypotheses
      • Hypothesis Testing.
      • Comparing Learning Algorithms: Paired t Tests; Practical Considerations.

Computational Learning Theory

Topics:
  • Introduction
  • Probably learning an approximately correct hypothesis:
    • The Problem Setting
    • Error of a Hypothesis
    • PAC-Learnability.
    • Sample Complexity for Finite Hypothesis Spaces:
      • Agnostic Learning and Inconsistent Hypotheses
      • Conjunctions of Boolean Literals Are PAC-Learnable
      • PAC-Learnability of Other Concept Classes.
      • Sample Complexity for infinite hypothesis spaces: Shattering a set of Instances
        • The Vapnik-Chervonenkis Dimension Sample Complexity and the VC Dimension. The mistake bound model of learning: Mistake bound for the FIND-S Algorithm
        • Mistake bound for the HALVING Algorithm; Optimal Mistake Bounds
        • WEIGHTED-MAJORITY Algorithm.

Neural Networks

Topics:
  • Understand the major technology trends driving Deep Learning
    • Be able to build, train and apply fully connected deep neural networks
    • Know how to implement efficient (vectorized) neural networks

What if I miss a class?

You will never lose any lecture. You can choose either of the two options:

  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.

What is duration of the course ?

Course duration will be from 25 to 30 hours but that vary based upon the question and answer session and assignments sessions.

Can I attend a demo session before enrollment?

Yes, we can share first session of previous batch that you can go over and judge the quality of trainer.

Where I can send my queries ?

You can send all your queries to our email id's or simply add to the forum where you will get reply to queries from expert.

Timing of the session ?

Generally all the live sessions are conducted during that time that is convenient to US and Indian audience.

There are two types of batches, one conducted during weekday and other during weekend.

Is there any option for 1 to 1 training session ?

yes, we also conduct one to one as per the need but candidate need to pay high for that requirement.

Will I get on job support after training?

Yes, support will be provided for 3 months where you can send your queries to us and our trainer will help and guide you.

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes. Weekend Class : 10 sessions of 3 hours each. Weekday Class : 30 sessions of 1 hours each.

Assignments

Each class will be followed by practical assignments which will aggregate to minimum 20 hours.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Blockchain concepts.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
04 August Mon - Fri ( 30 Days ) 07:00 AM - 8:00 AM ( IST ) Link
Course Curriculm

Machine Learning Introduction

Topics:
  • Well-Posed Learning Problems
  • Designing a Learning System:
    • Choosing the Training Experience;
    • Choosing the Target Function;
    • Choosing a Representation for the Target Function;
    • Choosing a Function Approximation Algorithm;
    • The Final Design
  • Perspectives and issues in machine learning.

Concept Learning

Topics:
  • Notation
  • The Inductive Learning Hypothesis
  • Concept Learning as Search
  • FIND-S: Algorithm for finding a Maximally Specific Hypothesis:
    • Version Spaces and the CANDIDATE-ELIMINATION Algorithm
    • Convergence of CANDIDATE-ELIMINATION Algorithm to the correct Hypothesis
    • Appropriate Training Examples for learning
    • Applying Partially Learned Concept,
  • Inductive Bias: A Biased Hypothesis Space
    • An Unbiased Learner
    • The Futility of Bias-Free Learning.

Decision Tree Learning

Topics:
  • Decision Tree Representation
  • Appropriate problems for decision tree learning
  • The basic decision tree Learning Algorithm
  • Hypothesis Space Search in decision tree learning, Inductive Bias in Decision Tree Learning
  • Issues in Decision Tree Learning:
    • Over fitting the Data
    • Incorporating Continuous-Valued Attributes
    • Alternative Measures for Selecting Attributes
    • Handling Training Examples with Missing Attribute Values
    • Handling Attributes with differing Costs.

Evaluating Hypotheses

Topics:
  • Estimating Hypothesis Accuracy
    • Sample Error and True Error
    • Confidence Intervals for Discrete-Valued Hypotheses.
    • Basics of Sampling Theory:
      • Error Estimation and Estimating Binomial Proportions
      • The Binomial Distribution
      • Mean and Variance
      • Estimators, Bias and Variance
      • Confidence Intervals
      • Two-sided and one-sided bounds. A General approach for deriving confidence intervals: Central Limit Theorem.
      • Difference in Error of two hypotheses
      • Hypothesis Testing.
      • Comparing Learning Algorithms: Paired t Tests; Practical Considerations.

Computational Learning Theory

Topics:
  • Introduction
  • Probably learning an approximately correct hypothesis:
    • The Problem Setting
    • Error of a Hypothesis
    • PAC-Learnability.
    • Sample Complexity for Finite Hypothesis Spaces:
      • Agnostic Learning and Inconsistent Hypotheses
      • Conjunctions of Boolean Literals Are PAC-Learnable
      • PAC-Learnability of Other Concept Classes.
      • Sample Complexity for infinite hypothesis spaces: Shattering a set of Instances
        • The Vapnik-Chervonenkis Dimension Sample Complexity and the VC Dimension. The mistake bound model of learning: Mistake bound for the FIND-S Algorithm
        • Mistake bound for the HALVING Algorithm; Optimal Mistake Bounds
        • WEIGHTED-MAJORITY Algorithm.

Neural Networks

Topics:
  • Understand the major technology trends driving Deep Learning
    • Be able to build, train and apply fully connected deep neural networks
    • Know how to implement efficient (vectorized) neural networks
Course FAQs

What if I miss a class?

You will never lose any lecture. You can choose either of the two options:

  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.

What is duration of the course ?

Course duration will be from 25 to 30 hours but that vary based upon the question and answer session and assignments sessions.

Can I attend a demo session before enrollment?

Yes, we can share first session of previous batch that you can go over and judge the quality of trainer.

Where I can send my queries ?

You can send all your queries to our email id's or simply add to the forum where you will get reply to queries from expert.

Timing of the session ?

Generally all the live sessions are conducted during that time that is convenient to US and Indian audience.

There are two types of batches, one conducted during weekday and other during weekend.

Is there any option for 1 to 1 training session ?

yes, we also conduct one to one as per the need but candidate need to pay high for that requirement.

Will I get on job support after training?

Yes, support will be provided for 3 months where you can send your queries to us and our trainer will help and guide you.

Features

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes. Weekend Class : 10 sessions of 3 hours each. Weekday Class : 30 sessions of 1 hours each.

Assignments

Each class will be followed by practical assignments which will aggregate to minimum 20 hours.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Blockchain concepts.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
Forum
Schedule & Pricing
04 August Mon - Fri ( 30 Days ) 07:00 AM - 8:00 AM ( IST ) Link
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