Machine Learning Using Python Training in Chandigarh
Machine Learning Training

Machine Learning Using Python

Candidates from various quantitative backgrounds, like Engineering, Finance, Math, Statistics, Economics, Business Management and have some knowledge on the data analysis, understanding on business problems etc.

Machine Learning Fundamentals

MODULE 1: MACHINE LEARNING INTRODUCTION
  • Introduction to Machine Learning & Predictive Modeling
  • Types of Business problems - Mapping of Techniques
  • Major Classes of Learning Algorithms
  • Different Phases of Predictive Modeling
  • Overfitting (Bias-Variance Trade off) & Performance Metrics
  • Feature engineering & dimension reduction
  • Concept of optimization & cost function
  • Overview of gradient descent algorithm
  • Overview of Cross validation
  • Model performance metrics
MODULE 2: UNSUPERVISED LEARNING
  • What is segmentation & Role of ML in Segmentation?
  • Concept of Distance and related math background
  • K-Means Clustering
  • Expectation Maximization
  • Hierarchical Clustering
  • Spectral Clustering (DBSCAN)
  • Principle component Analysis (PCA)
MODULE 3: DECISION TREE
  • Decision Trees - Introduction - Applications
  • Types of Decision Tree Algorithms
  • Construction of Decision Trees through Simplified Examples
  • Choosing the "Best" attribute at each Non-Leaf node
  • Entropy; Information Gain, Gini Index, Chi Square
  • Generalizing Decision Trees; Information Content and Gain Ratio
  • Dealing with Numerical Variables; other Measures of Randomness
  • Pruning a Decision Tree; Cost as a consideration
  • Unwrapping Trees as Rules
  • Decision Trees - Validation
  • Overfitting - Best Practices to avoid
MODULE 4: ENSEMBLE LEARNING
  • Concept of Ensembling
  • Manual Ensembling Vs. Automated Ensembling
  • Methods of Ensembling (Stacking, Mixture of Experts)
  • Bagging (Logic, Practical Applications)
  • Random forest (Logic, Practical Applications)
  • Boosting (Logic, Practical Applications)
  • Ada Boost
  • Gradient Boosting Machines (GBM)
  • XGBoost
MODULE 5: ARTIFICIAL NEURAL NETWORKS
  • Motivation for Neural Networks and Its Applications
  • Perceptron and Single Layer Neural Network
  • Learning In a Multi Layered Neural Net
  • Back Propagation and Conjugant Gradient Techniques
  • Neural Networks for Regression
  • Neural Networks for Classification
  • Interpretation of Outputs and Fine tune the models
  • Validating ANN models
MODULE 6: SUPPORT VECTOR MACHINES
  • Motivation for Support Vector Machine & Applications
  • Support Vector Regression
  • Support vector classifier (Linear & Non-Linear)
  • Mathematical Intuition (Kernel Methods Revisited)
  • Quadratic Optimization and Soft Constraints
  • Interpretation of Outputs and Fine tune the models
  • Validating SVM models
MODULE 7: K-NEAREST NEIGHBORS
  • What is KNN & Applications?
  • KNN for missing treatment
  • KNN For solving regression problems
  • KNN for solving classification problems
  • Validating KNN model
  • Model fine tuning with hyper parameters
MODULE 8: NAIVE BAYES
  • Concept of Conditional Probability
  • Bayes Theorem and Its Applications
  • Naive Bayes for classification
  • Applications of Naive Bayes in Classifications
MODULE 9: DATA MINING & NLP
  • Taming big text, Unstructured vs. Semi-structured Data
  • Fundamentals of information retrieval, Properties of words
  • Creating Term-Document (TxD) Matrices; Similarity measures
  • Low-level processes (Sentence Splitting; Tokenization)
  • Part-of-Speech Tagging, Stemming, Chunking
  • Finding patterns in text: text mining, text as a graph
  • Natural Language processing (NLP)
  • Text Analytics - Sentiment Analysis using Python
  • Text Analytics - Word cloud analysis using Python
  • Text Analytics - Segmentation using Clustering
  • Text Analytics - Classification (Spam/Not spam)
  • Applications of Social Media Analytics
  • Metrics(Measures Actions) in social media analytics
  • Important python modules for Machine Learning
  • Fine tuning the models using Hyper parameters
MODULE 10: PROJECT WORK
  • Applying different algorithms to solve business problems
  • Benchmark the results