Machine Learning , Artificial Intellegence

What is Machine Learning?
Analytics vs Data Science

  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle
  • Advantage of Deep Learning over Machine learning
  • Reasons for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning


  • Basis of Data Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data & Sources
  • Data Quality & Changes
  • Data Quality Issues
  • Data Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs OLAP
  • How is Data Stored?

Data Science Deep Dive

  • What Data Science is
  • Why Data Scientists are in demand
  • What is a Data Product
  • The growing need for Data Science
  • Large Scale Analysis Cost vs Storage
  • Data Science Skills
  • Data Science Use Cases
  • Data Science Project Life Cycle & Stages
  • Data Acuqisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats
  • Data Quantity
  • Data Quality
  • Resolution Techniques
  • Data Transformation
  • File format Conversions
  • Annonymization

Numpy & Pandas

  • Learning NumPy
  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions
  • Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Classes & OOPs


  • What is Statistics
  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions
  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics
  • What is Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • what is pValue
  • Chi-Square test
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression

Machine Learning, Deep Learning & AI using Python


  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques


  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model

Implementing Association rule mining

  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier

  • How to build Decision trees
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Decision Tree
  • Confusion Matrix

Random Forest Classifier

  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance

Naive Bayes Classifier.

Problem Statement and Analysis

  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.

Linear Regression

  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear Regression

Logistic Regression

  • Logistic Regression Overview
  • Data Partitioning
  • Univariate Analysis
  • Bivariate Analysis
  • Multicollinearity Analysis
  • Model Building
  • Model Validation
  • Model Performance Assessment AUC & ROC curves
  • Scorecard

Support Vector Machines

  • Introduction to SVMs
  • SVM History
  • Vectors Overview
  • Decision Surfaces
  • Linear SVMs
  • The Kernel Trick
  • Non-Linear SVMs
  • The Kernel SVM

Time Series Analysis

  • Describe Time Series data
  • Format your Time Series data
  • List the different components of Time Series data
  • Discuss different kind of Time Series scenarios
  • Choose the model according to the Time series scenario
  • Implement the model for forecasting
  • Explain working and implementation of ARIMA model
  • Illustrate the working and implementation of different ETS models
  • Forecast the data using the respective model
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective model for forecasting
  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series forecasting
  • Forecasting for given Time period

Machine Learning Project

Machine learning algorithms Python

  • Various machine learning algorithms in Python
  • Apply machine learning algorithms in Python

Feature Selection and Pre-processing

  • How to select the right data
  • Which are the best features to use
  • Additional feature selection techniques
  • A feature selection case study
  • Preprocessing
  • Preprocessing Scaling Techniques
  • How to preprocess your data
  • How to scale your data
  • Feature Scaling Final Project

Which Algorithms perform best

  • Highly efficient machine learning algorithms
  • Bagging Decision Trees
  • The power of ensembles
  • Random Forest Ensemble technique
  • Boosting – Adaboost
  • Boosting ensemble stochastic gradient boosting
  • A final ensemble technique

Model selection cross validation score

  • Introduction Model Tuning
  • Parameter Tuning GridSearchCV
  • A second method to tune your algorithm
  • How to automate machine learning
  • Which ML algo should you choose
  • How to compare machine learning algorithms in practice

Text Mining& NLP

  • Sentimental Analysis

PySpark and MLLib

  • Introduction to Spark Core
  • Spark Architecture
  • Working with RDDs
  • Introduction to PySpark
  • Machine learning with PySpark – Mllib

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