⇒ 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)
⇒ 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(Bootstrapping, K-Fold validation etc)
⇒ Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
⇒ 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)
⇒ 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
⇒ 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
⇒ 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
⇒ 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
⇒ 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
⇒ Concept of Conditional Probability
⇒ Bayes Theorem and Its Applications
⇒ Naïve Bayes for classification
⇒ Applications of Naïve Bayes in Classifications
⇒ 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.
⇒ Applying different algorithms to solve the business problems and bench mark the results
Duration
2-3 MonthsAvailable Seats
15Online Training Schedule
8.00 pm to 10.00 pmIndustrial Training Schedule in Chandigarh
8.00 am to 7.00 pm