Machine Leaning Using Python Training in Chandigarh
Machine Leaning Using Python
Who should do this course?
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.
Prerequisite Skills:
Core Python and Data Science
CORE PYTHON
Module1: Machine Learning
Introduction to Machine Learning & Predictive Modeling
Types of Business problems - Mapping of Techniques - Regression vs. classification vs.
segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building,
Validation)
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, Regression Trees
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 (Supervised)
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, and Hand Calculations
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 with hyper parameters
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 with hyper parameters
Validating SVM models
Module 7: K-Nearest Neighbors Algorithm (KNN)
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:Naïve Bayes
Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications
Module 9: Data Mining
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 K-Means/Hierarchical Clustering
Text Analytics - Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.
Module 10:Project work
Applying different algorithms to solve the business problems and bench mark the results