BASIC AND ADVANCED STATISTICS

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• PURPOSE OF STATISTICS

• DESCRIPTIVE STATISTICS

• INFERENTIAL STATISTICS

• WHAT IS DATA?

• DIFFERENT TYPES OF DATA

• PROBABILITY DISTRIBUTION

• NORMAL DISTRIBUTION

• SKEWNESS & KURTOSIS

• HYPOTHESIS TESTING

• NULL HYPOTHESIS TESTING, ALTERNATE HYPOTHESIS

• STATISTICAL TESTING

1. ANOVA TEST 2. 2-SAMPLE-T TEST 3. 2 - PROPORTION TEST 4. CHI-SQUARE TEST

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PYTHON PROGRAMMING

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1. Installation of Anaconda and Setting up Google Colabs
2. Basics of Python Programming
3. Introduction to Data Structures
4. Values, Variables, Functions and Librarys
5. Various libraries in python and purpose of each library
6. Deep Dive into List, Tuple, Set & Dictionary
7. Selection/Conditional Branching Statements
8. User Defined Functions
9. Lamda Functions
10. Iterators
11. Loop Structures/Iterative Statements

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MACHINE LEARNING ALGORITHMS PART -1

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• WHAT IS SUPERVISED LEARNING ?

• WHAT IS UNSUPERVISED LEARNING ?

• WHAT IS SEMI - SUPERVISED LEARNING ?

• WHAT IS REINFORCEMENT LEARNING

• DIFFERENCE BETWEEN MACHINE LEARNING & DEEP LEARNING.

• DIFFERENCE BETWEEN A.I. & C.I

• REGRESSION vs CLASSIFICATION

• INTRODUCTION TO LINEAR REGRESSION

1. SIMPLE LINEAR REGRESSION 2. MULTIPLE LINEAR REGRESSION 3. MATHS BEHIND L.R 4. OLS 5. BASED ON SLOPE AND INTERCEPT EQUATIONS 6. GRADIENT DESCENTS

• BUILD LINEAR REGRESSION MANUALLY

• CODING LINEAR REGRESSION ON PYTHON

• VALIDATION TECHNIQUES LIKE

• R - SQUARED , MSE, RMSE, AIC, BIC E.T.C.

• ASSUMPTIONS OF L.R

• REAL TIME BUSINESS CASE EXPLANATION ON L.R

• REAL TIME PROJECT - HEALTH CARE DOMAIN ON L.R

• MATHS BEHIND LOGISTIC REGRESSION

• BUILDING LOGISTIC REGRESSION MANUALLY

• CODING LOGISTIC REGRESSION ON PYTHON

• VALIDATION TECHNIQUES LIKE CONFUSION MATRIX

• ROC, AIC E.T.C.

• ASSUMPTIONS ON LOGISTIC REGRESSION

• BUSINESS CASE EXPLANATION ON LOGISTIC

• REGRESSION

• BUSINESS CASE ON LOGISTIC REGRESSION FROM BANKING

• K - MEANS CLUSTERING

• H. CLUSTERING

• MARKET SEGMENTATION USING K - MEANS CLUSTERING

• CLUSTERING FOR DATA IMPUTATION

• CLUSTERING IN CYBER SECURITY

• H . CLUSTERING TO UNDERSTAND THE PURCHASE PATTERN

• WORKING ON A REAL TIME PROJECT BASED ON ABOVE 3

• ALGORITHMS

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MACHINE LEARNING DEPLOYMENT & ARCHITECTURE

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1. What are the skillset required from companies for machine learning

2. Skills evaluations for individual participants

3. Life cycle of a machine learning developer

3.1 Problem Understanding

3.2 Data collection

3.3 Data Wrangling

3.4 Choosing right algorithm

3.5 Building Model

3.6 Model Evaluation

3.7 Model Performance Improvement

3.8 Model Finalisation

3.9 Model Deployment

3.10. Model Documentation

4. Best Practices on Machine Learning

4.1 Scrum Methodology Implementation on Machine Learning

4.2 Agile CRISP Implementation on Machine Learning

5. Building Machine Learning Solution Architecture for Banking Domain Project

5.1 Data Flow Design

5.2 Data Capturing

5.3 Running Machine Learning Engine in AWS using Docker

5.4 Implementations of Jenkins for Machine Learning Deployment

6. Live Project on Machine Learning Practical

6.1 Design Business Case for Machine Learning

6.2 Designing Machine Learning Architecture

6.3 Work allocation

6.4 Data Validation

6.5 Data Understanding Phase

6.6 Domain Understanding Phase

6.7 Feature Extraction Phase

6.8 Environment Study and Algorithm Finalisation

6.9 Model Building

6.10 Model Evaluation & Deployment

6.11 Client Feedback or Stakeholder Feedback on Deployed ML Engine

6.12. Performance Improvement

6.13. Change in Tools for better deployment and better features on improvement

6.14. Machine Learning Model Release

7. How to learn different Machine Learning Tools Faster

7.1 List of tools

7.2 Google Collabs, Google ML Kit

7.3 Apache Mahout, Apache Spark

7.4 ML Deployment Eco-System Docker, Jenkins, Django, Flask

7.5. Documentation and Github Release

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ADVANCED MACHINE LEARNING ALGORITHMS

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• DECISION TREE - INTERNAL MECHANISM

• ENTROPY & GINI

• HYPER PARAMETER TUNING

• CONTROLLING THE OVER FITTING

• BAGGING & BOOSTING FOR DECISION TREE AND MANUALLY BUILDING A

• BASIC DECISION TREE FOR A SAMPLE DATA

• HOW TO BUILD A POC ?

• EXAMPLE POC BUILDING.

• BUILD A POC ON DECISION TREE/RANDOM FOREST/ GRADIENT BOOSTING

• FROM (AIRLINES INDUSTRY)

• SUPPORT VECTOR MACHINES

• MATHS BEHIND SUPPORT VECTOR MACHINES

• SUPPORT VECTOR REGRESSION & SVC

• HINGE LOSS

• CHOOSING DECISION BOUNDARIES

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ADVANCED NATURAL LANGUAGE PROCESSING

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• POS TAGGING

• SEMANTIC ANALYSIS

• ENTITY RECOGNITION

• WORD CLOUD

• LEXICAL ANALYSIS

• SENTIMENTAL ANALYSIS

• NAVIES BAYES FOR NLP MODEL BUILDING

• BUILDING A PROJECTS ON NLP USING NLTK & SPACY

• LIMITATIONS OF NAIVE BAYES OVER RNN & LSTM

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PICK ONE ELECTIVE TOPIC

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• CONVOLUTION NEURAL NETWORK(IMAGE RECOGNITION)

• DEPLOYMENT OF MACHINE LEARNING MODELS

• BUILDING REINFORCEMENT LEARNING MODELS

• HIDDEN MARKOV CHAINS

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