Machine Learning Course in Mumbai and Thane

1. MACHINE LEARNING Section 1 : Introduction to Machine Learning Introduction to Machine Learning Types of Machine learning Data understanding : real life example Why Machine learning is future Which skills are required for Machine learning Discussion on different packages used for ML Related concepts: Splitting the dataset into train set and test set Practical knowledge of the algorithm on Python and R Section 2: Data prepressing & Regression Techniques Linear Regression Technique Dataset with problem description Non- Linear Regression Techniques Logistic Regression Technique Section 3 : K- Nearest Neighbors K-Nearest Neighbors Concept and theory Distance functions: Euclidean, Minkowski Why should we use KNN? Mathematical approach Dataset with problem description Practical application on Python and R Section 4 : Support Vector Machine Support Vector machine Introduction to Support Vector machine Mathematical Approach Theory on hyperplane Dataset with problem description Practical application on R and Python Section 5 : Random Forest Random Forest Theory and mathematical concepts Entropy and Decision Tree Dataset with problem description Classification using random forest on Python and R

2. Section 6 : Naïve Bayes Introduction of Naïve Bayes Theory of classification Concept of probability: prior and posterior Bayes Theorem Mathematical concepts Limitation of Naïve Bayes Dataset with problem description Practical application on Python and R Section 7: 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 8: Clustering Introduction of clustering K-mean clustering Dataset with problem description Practical application on Python and R Section 9: Gradient descent Gradient descent Stochastic Gradient descent Gradient boosting Types of boosting Bootstrapping Practical application on Python and R Section 10: Natural Language Processing Introduction of Natural Language Processing Information Retrieval Concepts and how to deal with humungous information Related concepts and theory Section 11: Deep Learning Introduction of Deep Learning Scope of Deep Learning Understanding Artificial Neural Network(ANN) Activation function & Neuron Feature learning and feature engineering Introduction to python advance packages for Machine Learning: TensorFlow Real world Machine Learning projects 203, Ratnamani Bldg, Near Platform No.1. Opp. Rajdarshan Society, Thane-West Web: www.nettechindia.com Mob : 9870803004/ 5