Machine learning course syllabus - NetTech India

1. 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

2. 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 203/RATNMANI BLDG, DADA PATIL WADI, OPP ICICI ATM, THANE WEST Web: www.nettechindia.com Phone : 9870803004/ 9870803005