Unsupervised Learning : Segmentation

• 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)

Supervised Learning :- Decision Trees

• 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

Supervised Learning :- 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

Supervised Learning :- Artificial Neural Network – ANN

• 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

Supervised Learning :- 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

Supervised Learning :-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

Supervised Learning :- Naive Bayes

• Concept of Conditional Probability
• Bayes Theorem and Its Applications
• Naïve Bayes for classification
• Applications of Naïve Bayes in Classifications

Text Mining And Analytics

• 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.

You should be able to demonstrate these skills

This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Course Contains

Introduction to Data Science with Python

• What is analytics & Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project

Outline for this course

Introduction to Data Science with Python

Python Essentials

Accessing/Importing And Exporting Data Using Python Modules

Data Manipulation – Cleansing – Munging using python modules

Data Analysis – Visualization Using Python

Introduction to Statistics

Introduction to Predictive Modelling

Data Exploration For Modelling

Data Preparation

Segmentation: Solving Segmentation Problems

Linear Regression: Solving Regression Problems

Logistic Regression : Solving Classification Problems

Unsupervised Learning : Segmentation

Supervised Learning :- Decision Trees

Time Series Forecasting : Solving Forecasting Problems

Machine Learning : Predictive Modelling

Supervised Learning :- Ensemble Learning

Supervised Learning :- Artificial Neural Network – ANN

Supervised Learning :- Support Vector Machines

Supervised Learning :-KNN

Supervised Learning :- Naive Bayes

Text Mining And Analytics

Audience for this course

• Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in the Data Science Industry or want to be future Data Scientists.
• Engineers who want to use a distributed computing engine for batch or stream processing or both
• Analysts who want to leverage Spark for analyzing interesting datasets
• Data Scientists who want a single engine for analyzing and modelling data
• MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
• Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
• Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being data scientists
• Professionals who’ve worked mostly with tools like Excel and want to learn how to use Python for statistical analysis.