Machine learning course syllabus - NetTech India

MACHINE LEARNING Section 1 : Introduction to Machine Learning  Introduction to Machine Learning  Types of Machine learning: Supervised, Unsupervised and Reinforcement Learning  Discussion on different packages used for ML  Working on Linear regression: Understanding the regression technique  Related concepts: Splitting the dataset into training and validation  Case study based practical application of the technique on R and Python Section 2 : Supervised Machine Learning  Linear Regression Technique  Logistic Regression Technique  Hierarchical Cluster Analysis Section 3 :Decision Tree  Decision Tree  Introduction to Decision tree  Significance of using Decision Tree  Different kinds of Decision Tree  Procedure and technique of Decision Tree  Practical application of Decision Tree on R and Python Section 4 : Support Vector Machine  Support Vector machine  Introduction to Support Vector machine  Mathematical Approach  Theory on hyperplane and kernels  Kernel function  Different kinds of kernels  Practical application on R and Python Section 5 : Random Forest  Random Forest  Theory and mathematical concepts  Entropy and Decision Tree Classification using random forest on Python and R Section 6 : Naïve Bayes  Naïve Bayes  Theory of classification  Concept of probability: prior and posterior  Bayes Theorem  Mathematical concepts  Limitation of Naïve Bayes  Practical application on Python and R Section 7 : K- Nearest Neighbours  K-Nearest Neighbours  Concept and theory

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Distance functions: Euclidean, Hamming, Minkowski Why should we use KNN? Mathematical approach Practical application on Python and R

Section 8 : Gradient Boosting  Gradient boosting  Bootstrapping  Types of boosting  Gradient descent  Practical application on Python and R Section 9 : Information Retrieval  Information Retrieval  Concepts and how to deal with humungous information  Natural Language Processing  Related concepts and theory Section 10 : Deep Learning  Deep Learning  Understanding Artificial Neural network(ANN)  Scope of Deep Learning  Feature learning and feature engineering  Introduction to python packages for Machine Learning: TensorFlow and Theano  Using TensorFlow on Mnist dataset containing black and white images  Creating a chatbot with TensorFlow on Python

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