Best Machine Learning training in chennai

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Machine Learning

We provide the Best Machine Learning Training in  chennai, it has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.  Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth.  Machine learning, course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, along with short reports describing your results Machine Learning Training in  chennai

Our technical team will present algorithms and proceeds in different way that strengthen them in huge systems as you learn about a different type of topics, that including:

“Reinforcement learning, randomized search algorithms, statistical supervised and unsupervised learning methods, Bayesian learning method”

INTRODUCTION:
1.Well-posed learning problems
2.Designing a learning system
3.Choosing the training experience
4.Choosing the target function
5.choosing a representation for the target function
6.Choosing a function approximation algorithm
7.The final design
8.Perspectives and isssues in machine learning
9.Issues in machine learning
10.How to read this book
11.Summary and further reading
12.Exercises
13.Reference

CONCEPT LEARNING AND THE GENERAL-TO-SPECIFIC ORDERING :
1.Introduction
2.A concept learning task
3.Notation
4.The inductive learning hypothesis
5.Concept learning as search
6.General-specific ordering to hypotheses
7.Finding a maximally specific hypothesis
8.Version spaces and the candidate -elimination algorithm
9.Representation
10.The list- then-eliminate algorithm
11.A more compact representation for version spaces
12.Candidate – elimination learning algorithm
13.An illustrative example
14.Remarks on version spaces and candidate – elimination
15.Will the candidate – elemination algorithm converge to the correct hypothesis?
16.What training example should the learner request next?
17.How can partially learned concept be used?
18.Inductive bias
19.A biased hypothesis space
20.An unbiased learner
21.The futility of bias – free learning
22.Summary and further reading
23.Exercise
24.References

DECISION TREE LEARNING:
1.Introduction
2.Decision tree representation
3.Appropriate problems for decision tree learning
4.The basic decision tree learning algorithm
5.Which attribute is the best classifier ?
6.An illustrate example
7.Hypothesis space search in decision tree learning
8.Inductive bias in decision tree learning
9.Restriction biases and preference biases
10.Why prefer short hypotheses?
11.Issues in decision tree learning
12.Avoiding overfitting the data
13.Incorporating continuous -valued attributes
14.Alternative measures for selecting attributes
15.Handling training examples with missing attribute values
16.Handling attributes with differing costs
17.Summary and further reading
18.Exercise
19.Reference

ARTIFICIAL NEURAL NETWORKS:
1.Introduction
2.Biological motivation
3.Neural network representations
4.Appropriate problems for neural network learning
5.Perceptrons
6.Representational power of perceptrons
7.The perceptron training rule
8.Gradient descent and the delta rule
9.Remarks
10.Multilayer networks and the backpropagation algorithm
11.A differentiable threshold unit
12.The backpropagation algorithm
13.Derivation of the backpropagation rule
14.Remarks on the backpropagation algorithm
15.Convergence and local minima
16.Representational power of feedforwad networks
17.Hypothesis space search and inductive bias
18.Hidden layer representation
19.Generalization ,overfitting,and stopping criterion
20.An illustrative example :face recognition
21.The task
22.Design choices
23.Learned hidden representations
24.Advanced topics in artificial neural networks
25.Alternative error functions
26.Recurrent networks
27.Dynamically modifying network structure
28.Summary and further reading
29.Exercise
30.References

EVALUATING HYPOTHESES
1.Motivation
2.Estimating hypothesis accuracy
3.Sample error and true error
4.Confidence intervals for discrete-valued hypotheses
5.Basics of sampling theory
6.Error estimation and estimating binomial proportions
7.The binomial distribution
8.Mean and variance
9.Estimators,bias,and variance
10.Confidence intervals
11.Two-sided and one -sided bounds
12.A general approach for deriving confidence intervals
13.Central limit theorem
14.Difference in error of two hypotheses
15.Hypothesis testing
16.Comparing learning algorithms
17.Paired t tests
18.Practical considerations
19.Summary and further reading
20.Exercises
21.References

BAYESIAN LEARNING:
1.Introduction
2.Bayes theorem
3.An example
4.Bayes theoram and concept learning
5.Brute-force bayes concept learning
6.MAP hypotheses and consistent learners
7.Maximum likelihood and least- squared error hypotheses
8.Maximum likelihood hypotheses for predicting probablities
9.Gradient search to maximize likelihood in neural net
10.minimum description length principal
11.Bayes optimal classifier
12.Gibbs algorithm
13.Naive bayes classifier
14.An illustrative example
15.An example :learning to classify textv
16.Experimental results
17.Bayesian belief networks
18.Conditional independence
19.Representation
20.Inference
21.Learning bayesian belief networks
22.Gradient ascent training of bayesian networks
23.Learning the structure of bayesian networks
24.The EM algorithm
25.Estimating means of k gaussians
26.General statement of EM algorithm
27.Derivation of the k means algorithm
28.Summary and further reading
29.Exercises
30.References

COMPUTATIONAL LEARNING THEORY :
1.Introduction
2.Probably learning an approximately correct hypothesis
3.The problem setting
4.Error of a hypothesis
5.PAC learnability
6.Sample complexity for finite hypothesis spaces
7.Agnostic learning and inconsistent hypotheses
8.Conjunctions of boolean literals are PAC =learnable
9.PAC -learnabilty of other concept classes
10.Sample complexity for infinite hypothesis spaces
11.Shattering a set of instances
12.The vapnik-chervonenkis dimension
13.Sample complexity and the VC dimension
14.VC dimension or neural networks
15.The mistake bound model for learning
16.Mistake bound for the FIND-S algorithm
17.Mistake bound for the HALVING algorithm
18.Optimal mistake bounds
19.WEIGHTED _MAJORITY algorithm
20.Summary and further reading
21.Exercise
22.References

INSTANCE -BASED LEARNING:
1.Introduction
2.K-nearest neighbor learning
3.Distance-weighted nearest neighbour algorithm
4.Remarks onn k-nearest neighbor algorithm
5.A note on terminology
6.Locally weighted regression
7.Locally weighted linear regression
8.Remarks on locally weighted regression
9.Redial basics functions
10.Case-based reasoning
11.Remarks on lazy and eager learning
12.Summary and further reading
13.Exercise
14.References

GENETIC ALGORITHMS :
1.Motivation
2.Genetic algorithms
3.Representing hypotheses
4.Genetic operators
5.Fitness function and selection
6.An llustrative example
7.Extensions
8.Hypothesis space search
9.Population evolution and the schema theorem
10.Genetic programming
11.Representing programs
12.Illustrative example
13.Remarks on genetic programming
14.Models of evolution learning
15.Lamarckian evolution
16.Baldwin effect
17.Parellelizing genetic algorithms
18.Summary and further reading
19.Exercises
20.References

LEARNING SETS OF RULES:
1.Introduction
2.Sequential covering algorithms
3.General to specific beam search
4.Variations
5.Learning rules sets:summary
6.Learning first-order rules
7.First-order learn clauses
8.Terminology
9.Learning sets of first-order rules:FOIL
10.Generating candidate specialization in foil
11.Guiding the search in foil
12.Learning recursive rule sets
13.Summary of FOIL
14.Introduction as inverted deduction
15.First-order resolution
16.Inverting resolution:first-order case
17.Summary of inverse resolution
18.Generalization ,subsumption ,andentailment
19.Progol
20.Summary and further reading
21.Exercise
22.References

ANALYTICAL LEARNING :
1.Introduction
2.Inductive and analytical learning problems
3.Learning with perfect domain theories :prolog -EBG
4.An illustrative trace
5.Remarks on explanation-based learning
6.Discovering new features
7.Deductive learning
8.Inductive bias in explanation -based learning
9.Knowledge level learning
10.Explanation -based learning of search control knowledge
11.Summary and further reading
12.Exercises
13.References

COMBINING INDUCTIVE AND ANALLYTICAL LEARNING:
1.Motivation
2.Inductive-analytical approaches to learning
3.The learning problem
4.Hyphothesis space search
5.Using prior knowledge to initialize the hypothesis
6.The KBANN algorithm
7.An illustrative example
8.Remarks
9.Using prior knowledge to alter the search objective
10.The TANGENTPROP algorithm
11.An illustrative example
12.The EBNN algorithm
13.Remarks
14.Using prior knowledge ton augment search operators
15. The FOCL algorithm
16.Remarks
17.State of the art
18.Summary and further reading
19.Exercises
20.References

REIGNFORCEMENT LERANING:
1.Introduction
2.the learning task
3.Q learning
4.The Q function
5.An algorithm for learning Q
6.An illustrative example
7.Convergence
8.Experimentation strategies
9.Updating sequence
10.Nondeterministic rewards and actions
11.Temporal difference learning
12.Generalizing from examples
13.Relationship to dynamic programming
14.Summary and further reading
15.Exercise
16.Reference