EXPLORATORY DATA ANALYSIS

Learn how to find and analyze the patterns in the data to draw actionable insights.

• Data Sourcing
• Data Cleaning
• Univariate Analysis
• Segmented Univariate
• Bivariate Analysis
• Derived Metrics

INVESTMENT ASSIGNMENT

Learners will fill in the shoes of an analyst at an investment bank and determine where the firm should invest. They will then have to explain their recommendations in lieu of the analysis conducted

• Problem Statement
• Evaluation Rubric
• Final Submission
• Solution

INFERENTIAL STATISTICS

Build a strong statistical foundation and learn how to ‘infer’ insights from a huge population using a small sample.

• Basics of Probability
• Discrete Probability Distributions
• Continuous Probability Distributions
• Central Limit Theorem

HYPOTHESIS TESTING

Understand how to formulate and validate hypotheses for a population to solve real-life business problems.

• Concepts of Hypothesis Testing - I: Null and Alternate Hypothesis, Making a Decision, and Critical Value Method.
• Concepts of Hypothesis Testing - II: p-Value Method and Types of Errors
• Industry Demonstration of Hypothesis Testing: Two-Sample Mean and Proportion Test, A/B Testing.

LINEAR REGRESSION

Venture into the machine learning community by learning how one variable can be predicted using several other variables through a housing dataset where you will predict the prices of houses based on various factors.

• Introduction to Simple Linear Regression
• Simple Linear Regression in Python
• Multiple Linear Regression
• Multiple Linear Regression in Python
• Industry Relevance of Linear Regression

LOGISTIC REGRESSION

Learn your first binary classification technique by determining which customers of a telecom operator are likely to churn versus who are not to help the business retain customers.

• Univariate Logistic Regression
• Multivariate Logistic Regression - Model Building
• Multivariate Logistic Regression - Model Evaluation
• Logistic Regression - Industry Applications

NAIVE BAYES

Understand the basic building blocks of Naive Bayes and learn how to build an SMS Spam Ham Classifier using Naive Bayes technique.

• Bayes Theorem and Its Building Blocks
• Naive Bayes For Categorical Data
• Naive Bayes for Text Classification

MODEL SELECTION

Learn the pros and cons of simple and complex models and the different methods for quantifying model complexity, along with regularisation and cross validation.

• Principles of Model Selection
• Model Evaluation

SUPPORT VECTOR MACHINE (OPTIONAL)

Learn how to find a maximal marginal classifier using SVM, and use them to detect spam emails, recognize alphabets and more!

• SVM - Maximal Margin Classifier
• SVM - Soft Margin Classifier
• Kernels

TREE MODELS

Learn how the human decision-making process can be replicated using a decision tree and other powerful ensemble algorithms.

• Introduction to Decision Trees
• Algorithms for Decision Tree
• Construction Truncation and Pruning
• Random Forests

BOOSTING

Learn how weak learners can be ‘boosted’ with the help of each other and become strong learners using different boosting algorithms such as Adaboost, GBM, and XGBoost.

• Introduction to Boosting and AdaBoost
• Gradient Boosting

UNSUPERVISED LEARNING: CLUSTERING

Learn how to group elements into different clusters when you don’t have any pre-defined labels to segregate them through K-means clustering, hierarchical clustering, and more.

• Introduction to Clustering
• K Means Clustering
• Executing K Means in Python
• Hierarchical Clustering
• Other Forms of Clustering

TELECOM CHURN CASE STUDY

Solve the most crucial business problem for a leading telecom operator in India and southeast Asia - predicting customer churn

• Problem Statement
• Evaluation
• Rubric
• Final Submission
• Solution

LEXICAL PROCESSING

Do you get annoyed by the constant spams in your mail box? Wouldn’t it be nice if we had a program to check your spellings? In this module learn how to build a spell checker & spam detector using techniques like phonetic hashing, bag-of-words, TF-IDF, etc.

• Introduction to NLP
• Basic Lexical Processing
• Advanced Lexical Processing

SYNTACTIC PROCESSING

Learn how to analyze the syntax or the grammatical structure of sentences with the help of algorithms & techniques like HMMs, Viterbi Algorithm, Named Entity Recognition (NER), etc.

• Introduction to Syntactic Processing
• Parsing
• Information Extraction
• Conditional Random Fields

SYNTACTIC PROCESSING -ASSIGNMENT

Build a POS tagger for tagging unknown words using HMMs and modified Viterbi algorithm

• Problem Statement
• Evaluation
• Rubric
• Final Submission
• Solution

SEMANTIC PROCESSING

Learn the most interesting area in the field of NLP and understand different techniques like word-embeddings, LSA, topic modeling to build an application that extracts opinions about socially relevant issues (such as demonetization) on social media platforms.

• Introduction to Semantic Processing
• Distributional Semantics Topic Modelling
• Social Media Opinion Mining
• Semantic Processing
• Case Study

BUILDING CHATBOTS WITH RASA

Imagine if you could make restaurant booking without opening Zomato. Build your own restaurant-search chatbot with the help of RASA - an open source framework and deploy it on Slack.

• Problem Statement
• Evaluation
• Rubric
• Final Submission
• Solution

INTRODUCTION TO NEURAL NETWORKS

Learn the most sophisticated and cutting-edge technique in machine learning - Artificial Neural Networks or ANNs.

• Structure of Neural Networks
• Feed Forward in Neural Networks
• Backpropagation in Neural Networks
• Modifications to Neural Networks
• Hyperparameter Tuning in Neural Networks

NEURAL NETWORKS - ASSIGNMENT

Build a neural network from scratch in Numpy to identify the type of skin cancer from images.

• Problem Statement
• Evaluation
• Rubric
• Final Submission
• Solution

CONVOLUTIONAL NEURAL NETWORKS -INDUSTRY APPLICATIONS

Learn the basics of CNN and OpenCV and apply it to Computer Vision tasks like detecting anomalies in chest X-Ray scans, vehicle detection to count and categorise them to help the government ascertain the width and strength of the road.

• Building CNNs with Python and Keras
• CNN Architectures and Transfer Learing
• Style Transfer and Object Detection Industry
• Demo:Using CNNs with Flowers Images Industry
• Demo:Using CNNs with X-ray Images

CREATE MACHINE LEARNING MODELS

CREATE MACHINE LEARNING MODELS

• Explore and analyze data with Python
• Train and evaluate machine learning models
• Train and evaluate regression models
• Train and evaluate classification models
• Train and evaluate clustering models
• Train and evaluate deep learning models

CREATE NO-CODE PREDICTIVE MODELS WITH AZURE MACHINE LEARNING

• Use Automated machine learning in Azure Machine Learning
• Creating a regression model with Azure Machine Learning designer
• Creating a classification model with Azure Machine Learning designer
• Creating a clustering model with Azure Machine Learning designer

BUILD AI SOLUTIONS WITH AZURE MACHINE LEARNING

• Introduction to Azure machine learning SDK
• Train a machine learning model with Azure Machine Learning
• Work with Data in Azure Machine Learning
• Work with Compute in Azure Machine Learning
• Orchestrate machine learning with pipelines
• Deploy real-time machine learning services with Azure Machine Learning
• Deploy batch inference pipelines with Azure Machine Learning
• Tune hyper parameters with Azure Machine Learning
• Automate machine learning model selection with Azure Machine Learning
• Explore differential privacy
• Explain machine learning models with azure machine learning
• Detect and mitigate unfairness in models with azure machine learning
• Monitor models with azure machine learning
• Monitor data drift with azure machine learning.

Introduction

Big Data - Beyond The Hype, Big Data Skills And Sources Of Big Data, Big Data Adoption, Research And Changing Nature Of Data Repositories, Data Sharing And Reuse Practices And Their Implications For Repository Data Curation, Overlooked And Overrated Data Sharing, Data Curation Services In Action, Open Exit: Reaching The End Of The Data Life Cycle, The Current State Of Meta-Repositories For Data, Curation Of Scientific Data At Risk Of Loss: Data Rescue And Dissemination

Hadoop:

Introduction of Big data programming-Hadoop, The ecosystem and stack, The Hadoop Distributed File System (HDFS), Components of Hadoop, Design of HDFS, Java interfaces to HDFS, Architecture overview, Development Environment, Hadoop distribution and basic commands, Eclipse development, The HDFS command line and web interfaces, The HDFS Java API (lab), Analyzing the Data with Hadoop, Scaling Out, Hadoop event stream processing, complex event processing, MapReduce Introduction, Developing a Map Reduce Application, How Map Reduce Works, The MapReduce Anatomy of a Map Reduce Job run, Failures, Job Scheduling, Shuffle and Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features, Real-World MapReduce,

Hadoop Environment:

Setting up a Hadoop Cluster, Cluster specification, Cluster Setup and Installation, Hadoop Configuration, Security in Hadoop, Administering Hadoop, HDFS – Monitoring & Maintenance, Hadoop benchmarks,

Introduction to HIVE,

Programming with Hive: Data warehouse system for Hadoop, Optimizing with Combiners and Practitioners (lab), Bucketing, more common algorithms: sorting, indexing and searching (lab), Relational manipulation: map-side and reduce-side joins (lab), evolution, purpose and use, Case Studies on Ingestion and warehousing

HBase:

Overview, comparison and architecture, java client API, CRUD operations and security

Apache Spark APIs for large-scale data processing:

Overview, Linking with Spark, Initializing Spark, Resilient Distributed Datasets (RDDs), External Datasets, RDD Operations, Passing Functions to Spark, Job optimization, Working with Key-Value Pairs, Shuffle operations, RDD Persistence, Removing Data, Shared Variables, EDA using PySpark, Deploying to a Cluster Spark Streaming, Spark MLlib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka Connect API, Mapreduce, Connecting DB’s with Spark

Data warehousing Concepts

Understand how to formulate and validate hypotheses for a population to solve real-life business problems.

• What is DWH?
• Characteristics of Datawarehouse
• Difference between OLTP and DWH
• Architecture of DWH
• Various BI tools
• Types of DWH
• Types of Dimensional Data Modeling
• Surrogate key
• Types of Dimension

Tableau Desktop (Introduction)

• Introduction Tableau
• Connecting to Excel, CSV Text Files
• Getting Started
• Product Overview
• Connecting to Databases
• Working with Data
• Analyzing
• Formatting
• Introduction to Calculations
• Dashboard Development
• Sharing
• Data Calculations
• Aggregate Calculations
• User Calculations
• Table Calculations
• Logical Calculations
• String Calculations
• Number Calculations
• Type Conversion
• Parameters
• Filtering Conditions
• Filtering Measures
• Histograms
• Sorting
• Grouping
• Sets
• Tree maps, word clouds and bubble charts
• Pareto Charts
• Waterfall Charts
• Bump Charts
• Funnel Charts
• Bollinger Bands