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EIEIO / ARTIFICIAL INTELLIGENCE

Data Science Machine Learning

Course outline

5 days
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Duration

5 days

Outline

Data Science Overview

  • Data Science
  • Data Scientists
  • AI vs ML vs DL vs Data Science
  • Data Science Scope, Applications
  • Data Science Introduction
  • Predictive v/s Descriptive Data Analysis
  • Data Science v/s Data Analytics
  • Regression & Classification Problems
  • What makes a Data Science Expert?
  • The art of making stories from Data
  • Examples of Data Science
  • Python for Data Science

Data Analytics Overview

  • Introduction to Data Visualization
  • Processes in Data Science
  • Data Wrangling, Data Exploration, and Model Selection
  • Exploratory Data Analysis or EDA
  • Data Visualization
  • Plotting
  • Hypothesis Building and Testing

(This part will be taken for 1 hour to refresh python basics considering participants have basic idea of atleast one programming language)

Introduction to Python Programming

  • What is Python?
  • Installing Anaconda
  • Understanding the Spyder Integrated Development Environment (IDE)
  • Python basics and string manipulation
  • lists, tuples, dictionaries, variables
  • Control Structure – If loop, For loop and while Loop
  • Single line loops
  • Writing user defined functions
  • Object oriented programming
  • Working with Class & Inheritance

Statistic for Data

  • Measure of Central Tendency – Mean, Mode and Median
  • Grouped and Ungrouped Data
  • Measure of Spread – IQR, Variance and Standard Deviation
  • Covariance
  • Correlation
  • Kurtosis, Skewness

Analyzing the categorical Data

  • Proportional Test
  • Chi Square Test
  • Fisher’s Exact Test
  • Mantel Henszel test

Analyzing the Continuous Data

  • One Sample T-Test
  • Two Independent Samples Tests
  • Paired T-test
  • Wilcoxon Test
  • Anova
  • Kruskal Wallis Test

Probabilistic Theory

  • Events and their Probabilities
  • Rules of Probability
  • Conditional Probability and Independence
  • Distribution of a Random Variable
  • Bayes Theorem
  • Moment Generating functions Central
  • Limit Theorem
  • Expectation & Variance
  • Standard Distributions – Bernoulli, Binomial & Multinomial

Data Structure & Data Manipulation in Python

  • Intro to Numpy Arrays
  • Creating ndarrays
  • Indexing
  • Data Processing using Arrays
  • Mathematical computing basics
  • Basic statistics
  • File Input and Output
  • Getting Started with Pandas
  • Data Acquisition (Import & Export)
  • Indexing
  • Selection and Filtering
  • Sorting & Summarizing
  • Descriptive Statistics
  • Combining and Merging Data Frames
  • Removing Duplicates
  • Discretization and Binning
  • String Manipulation

Visualization in python, case studies

  • Introduction to Visualization
  • Visualization Importance
  • Visualization Rules
  • Working with Python visualization libraries
  • Matplotlib
  • Creating Line Plots, Bar Charts, Pie Charts, Histograms, Scatter Plots

Working with Seaborn

  • Data Visualization using Seaborn
  • Basic Plots, color palettes
  • Plotting categorical data
  • Visualizing linear relationship
  • Plotting on data-aware grids
  • HeatMap, Histogram, Barplot, Factor plot
  • Density Plot, Joint Distribution Plot

Linear Regression

  • Regression Problem Analysis
  • Mathematical modelling of Regression Model
  • Gradient Descent Algorithm
  • Programming Process Flow
  • Use cases
  • Regression Table
  • Heteroscedasticity
  • Model Specification
  • L1 & L2 Regularization

Linear Regression – Case Study & Project

  • Programming Using python
  • Building simple Univariate Linear Regression Model
  • Multivariate Regression Model
  • Apply Data Transformations
  • Identify Multicollinearity in Data Treatment on Data
  • Identify Heteroscedasticity
  • Modelling of Data
  • Variable Significance Identification
  • Model Significance Test
  • Bifurcate Data into Training / Testing Data set
  • Build Model on Training Data Set
  • Predict using Testing Data Set
  • Validate the Model Performance
  • Project: Boston Housing Prizes Prediction
  • Project: Cancer Detection Predictive Analysis
  • Best Fit Line and Linear Regression

Logistic Regression

  • Variable and Model Significance
  • Maximum Likelihood Concept
  • Log Odds and Interpretation
  • Regression Table
  • Null Vs Residual Deviance
  • Problem Analysis
  • Cost Function Formation
  • Mathematical Modelling
  • Use Cases

Case Study & Project

  • Model Parameter Significance Evaluation
  • Drawing the ROC Curve
  • Estimating the Classification Model Hit Ratio
  • Isolating the Classifier for Optimum Results
  • Project: Digit Recognition using Logistic Regression

Decision Trees with Case Study

  • Forming a Decision Tree
  • Components of Decision Tree
  • Mathematics of Decision Tree
  • Decision Tree Evaluation
  • Practical Examples & Case Study
  • Project: Intrusion Detection

Random Forests

  • Random Forest Mathematics
  • Examples & use cases using Random Forests

Case Study:

  • Bank Marketing Analysis
  • Customer Churn Analysis

K-NN Algorithm – Applications & Case Studies

  • Understanding the KNN
  • Distance metrics
  • Case Study on KNN

Support Vector Machine

  • Concept and Working Principle
  • Mathematical Modelling
  • Optimization Function Formation
  • The Kernel Method and Nonlinear Hyperplanes
  • Use Cases
  • Programming SVM using Python
  • Project - Character recognition using SVM
  • Project - Regression problem using SVM
  • Project - Wisconsin Cancer Detection using SVM

Clustering

  • Application of clustering
  • DBSCAN
  • Self Organizing Maps
  • Hierarchical Clustering
  • K Means Clustering
  • Use Cases for K Means Clustering
  • Programming for K Means using Python
  • Image Color Quantization using K Means Clustering Technique
  • Customer segmentation using KMeans
  • Cluster Size Optimization vs Definition Optimization
  • Projects & Case Studies

Principle Component Analysis

  • Dimensionality Reduction, Data Compression
  • Curse of dimensionality
  • Multicollinearity
  • Factor Analysis
  • Concept and Mathematical modelling
  • Use Cases
  • Programming using Python

NLP – Text Data Analysis

  • Natural Language Understanding
  • Natural Language Generation
  • Word & Sentence Tokenization
  • POS Tagging
  • Morphological Analysis – stemming and lemmatization
  • Count vector
  • TF-IDF
  • Co occurrence Matrix
  • Word2Vec
  • Continuous Bag of Words
  • Text Classification
  • Sentiment Analysis
  • Chatbot
  • Topic Modelling
  • Project: Email Classification
  • Project: Live streaming tweets and Brand Analysis
  • Project: Sentiment Analysis & Text Summarization

Artificial Neural Networks

  • Neurons, ANN & Working
  • Single Layer Perceptron Model
  • Multilayer Neural Network
  • Feed Forward Neural Network
  • What is Activation Function?
  • How to decide number of hidden neurons?
  • Cost Function Formation
  • Applying Gradient Descent Algorithm
  • Backpropagation Algorithm & Mathematical Modelling
  • Programming Flow for backpropagation algorithm
  • Use Cases of ANN
  • Programming SLNN using Python
  • Programming MLNN using Python
  • Digit Recognition using MLNN
  • XOR Logic using MLNN & Backpropagation
  • Diabetes Data Predictive Analysis using ANN
  • Project – Banking Problem Analysis – When the customer will leave?
  • Project – Medical Problem Analysis
  • Project – Miscellaneous (Industry relevant Project)

Time Series

  • Definition of Time Series
  • Time Series Decomposition
  • Simple Moving Average Method
  • Weighted Moving Average Method
  • Single Exponential Smoothing Method
  • Double Exponential Smoothing Method
  • Triple Exponential Smoothing Method
  • Stationarity of Data
  • ARIMA Models

Case on Automobile Sales Data

  • Importing Data in Python
  • Converting into Time Series Data
  • Decomposition of Data
  • Identifying Stationarity
  • Transforming to Stationery Data
  • Modelling
  • Prediction
  • Evaluating Model Performance

Forecasting case study and assessment, Other projects

Learning Objective of this week – Image Processing with python and opencv, working with unsupervised learning techniques, Clustering techniques, Case Studies and Examples

Image Processing with Opencv

  • Image Acquisition and manipulation using opencv
  • Video Processing
  • Edge Detection
  • Corner Detection
  • Face Detection
  • Image Scaling for ANN
  • Face Detection in an image frame
  • Object detection
  • Training ANN with Images
  • Character Recognition

Introduction to TensorFlow & Keras

  • The Programming Model
  • Working with constants, variables and placeholders
  • Linear Regression using Tensorflow
  • Logistic Regression using Tensorflow
  • Tensorflow low level APIs
  • Data manipulation using Tensorflow
  • Data Manipulation using Keras
  • Data Model
  • Tensor Board
  • Introducing Feed Forward Neural Nets
  • Softmax Classifier
  • ReLU Classifier
  • Dropout Optimization
  • MNIST Classification using Deep Neural Network

Convolutional Neural Networks

  • CNN Architecture
  • Convolution and Relu
  • Pooling
  • Variants of the Basic Convolution Function
  • Efficient Convolution Algorithms
  • The Neuroscientific Basis for Convolutional Networks
  • MNIST Data set Analysis
  • CIFAR dataset Analysis
  • Do’s and Don’t while using CNNs
  • Evaluation of Deep Learning Model
  • Tuning Deep Learning Model
  • Dropout Optimization

Programme scope, delivery arrangements and suitability are confirmed during enquiry.