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Course Overview
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Introduction to Machine Learning
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Learning with Regression and Trees
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Learning with Classification and clustering
- Bayes Theorem and Naive Bayes Classifier
- Bayesian Belief Network
- Markov Models
- Hidden Markov Model
- Support Vector Machine
- K mean Clustering Algorithm
- Apriori Algorithm with solved Example
- Agglomerative Algorithm with solved Example Part #1
- Agglomerative Algorithm with solved Example Part #2
- FP Tree Algorithm with Solved Example
- Hidden Markov model Basic Part 1
- Hidden Markov model Basic Part 2
- Hidden Markov model Basic Part 3
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Dimensionality Reduction
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Introduction to Neural Network
- Introduction to ANN and structure of ANN
- Activation functions in ANN (Discrete and Continuous)
- Neural Network Architecture
- Linear Separability
- Mc-Culloch-Pitts Neural Model
- Hebbs Network/Hebbian Learning (with solved example)
- Winners-Takes-All
- Self Organizing Maps and KSOMs
- Linear Vector Quantization (LVQs)
- Derivation of Unipolar Continuous Function.
- Derivation of Bipolar Continuous Function
- Perceptron Learning (with solved example)
- Backpropagation Network (with solved example)
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Introduction to Optimization Techniques
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Notes
Key point in Machine Learning ( Basics of ML )
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5 Comments
Can you advise if this is all in English please?
no sir its in hindi
I am registered in machine learning course.I tried to open your pdf notes but error shows.Please send me all pdf files on my mail id .
It shows click on the blue text below to download the notes as soon as I click on it 404 error is displayed. Please help me out here.
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