Machine Learning free videos and free material uploaded by Nagaraju Thatha .
Syllabus / What will i learn?
OBJECTIVES:
• Familiarity with a set of well-known supervised, unsupervised and semi-supervised
• learning algorithms.
• The ability to implement some basic machine learning algorithms
• Understanding of how machine learning algorithms are evaluated
UNIT -I:
The ingredients of machine learning, Tasks: the problems that can be solved with machine learning, Models: the output of machine learning, Features, the workhorses of machine learning. Binary classification and related tasks: Classification, Scoring and ranking, Class probability estimation
UNIT- II:
Beyond binary classification:Handling more than two classes, Regression,
Unsupervised and descriptive learning. Concept learning: The hypothesis space, Paths through the hypothesis space, Beyond conjunctive concepts
UNIT- III:
Tree models: Decision trees, Ranking and probability estimation trees, Tree learning as variance reduction. Rule models:Learning ordered rule lists, Learning unordered rule sets, Descriptive rule learning, First-order rule learning
UNIT -IV:
Linear models: The least-squares method, The perceptron: a heuristic learning algorithm for linear classifiers, Support vector machines, obtaining probabilities from linear classifiers, Going beyond linearity with kernel methods.Distance Based Models: Introduction, Neighbours and exemplars, Nearest Neighbours classification, Distance Based Clustering,
Hierarchical Clustering.
UNIT- V:
Probabilistic models: The normal distribution and its geometric interpretations, Probabilistic models for categorical data, Discriminative learning by optimising conditional likelihoodProbabilistic models with hidden variables.Features: Kinds of feature, Feature transformations, Feature construction and selection. Model ensembles: Bagging and random forests, Boosting
UNIT- VI:
Dimensionality Reduction: Principal Component Analysis (PCA), Implementation and demonstration. Artificial Neural Networks:Introduction, Neural network representation,
appropriate problems for neural network learning, Multilayer networks and the back propagation algorithm.
OUTCOMES:
• Recognize the characteristics of machine learning that make it useful to real-world
• Problems.
• Characterize machine learning algorithms as supervised, semi-supervised, and
• Unsupervised.
• Have heard of a few machine learning toolboxes.
• Be able to use support vector machines.
• Be able to use regularized regression algorithms.
• Understand the concept behind neural networks for learning non-linear functions.
Curriculum for this course
29 Lessons
08:45:02 Hours
Introduction to Machine Learning
1 Lessons
- Introduction to Machine Learning Video 00:07:53
Accessing and Visualizing performance - Part-2 Machine learning
1 Lessons
- Accessing and Visualizing performance - Part-2 Machine learning Video 00:10:05
Regression -Machine Learning
1 Lessons
- Regression -Machine Learning Video 00:23:21
Multi class Classification- Handling More than Two classes
1 Lessons
- Multi class Classification- Handling More than Two classes Video 00:24:27
Introduction to Tree models -Machine Learning
1 Lessons
- Introduction to Tree models -Machine Learning Video 00:12:21
Decision Trees -Part-1 Machine learning
1 Lessons
- Decision Trees -Part-1 Machine learning Video 00:23:32
Decision Trees -Part-2 Machine learning
1 Lessons
- Decision Trees -Part-2 Machine learning Video 00:24:45
Ranking And Probability Estimation -Decision trees -Machine Learning
1 Lessons
- Ranking And Probability Estimation -Decision trees -Machine Learning Video 00:33:37
Tree Learning as variance reduction - Regression Tree -Machine Learning
1 Lessons
- Tree Learning as variance reduction - Regression Tree -Machine Learning Video 00:32:44
Tree Learning as Variance Reduction -clustering Tree -Machine Learning
1 Lessons
- Tree Learning as Variance Reduction -clustering Tree -Machine Learning Video 00:15:37
Rule models- Learning ordered Rule list- Machine Learning
1 Lessons
- Rule models- Learning ordered Rule list- Machine Learning Video 00:15:30
Example of Learning ordered rule list -Machine Learning
1 Lessons
- Example of Learning ordered rule list -Machine Learning Video 00:16:02
SCORING AND RANKING Part-1 Machine Learning
1 Lessons
- SCORING AND RANKING Part-1 Machine Learning Video 00:11:43
Scoring and ranking-Machine learning
1 Lessons
- Scoring and ranking-Machine learning Video 00:27:47
Supervised Learning- Machine Learning
1 Lessons
- Supervised Learning- Machine Learning Video 00:09:32
Unsupervised Learning
1 Lessons
- Unsupervised Learning Video 00:11:41
Reinforcement Learning -machine learninig
1 Lessons
- Reinforcement Learning -machine learninig Video 00:08:16
Ingredients of Machine learning -Tasks(the problems that can be solved with machine learning )
1 Lessons
- Ingredients of Machine learning -Tasks(the problems that can be solved with machine learning ) Video 00:25:39
Ingredients of Machine Learning- Models(the output of machine learning)- geometric model
1 Lessons
- Video 00:25:44
Ingredeints of Machine learning -models(probabilistic model)
1 Lessons
- Ingredeints of Machine learning -models(probabilistic model) Video 00:14:27
Ingredients of Machine learning- Models( Logical models)
1 Lessons
- Ingredients of Machine learning- Models( Logical models) Video 00:14:14
Ingredients of Machine learning - features
1 Lessons
- Ingredients of Machine learning - features Video 00:12:33
Binary Classification - Machine learning
1 Lessons
- Binary Classification - Machine learning Video 00:12:30
Accuracy calculation in binary classification- Machine Learning
1 Lessons
- Accuracy calculation in binary classification- Machine Learning Video 00:18:05
Visualizing classification performance - Machine learning
1 Lessons
- Visualizing classification performance - Machine learning Video 00:11:45
Learning Unordered rule list- Rule models- Machine Learning
1 Lessons
- Learning Unordered rule list- Rule models- Machine Learning Video 00:15:39
Unsupervised and Descriptive Learning
1 Lessons
- Unsupervised and Descriptive Learning 00:35:01
Concept Learning -- The hypothesis space
1 Lessons
- Concept Learning -- The hypothesis space 00:22:16
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