Data Science & Machine Learning Training Course

  Lecture 1-15 Python Programming
Live Lecture 
·         Introduction of Python

·         The Companies using Python

·         Different Applications where Python is used

·         Discuss Python Scripts on UNIX/Windows

·         Values, Types, Variables

·         Operands and Expressions

·         Conditional Statements

·         Loops

·         Command Line Arguments

·         Writing to the screen

·         Sequences and File Operations
·         Python files I/O Functions

·         Numbers

·         Strings and related operations

·         Tuples and related operations

·         Lists and related operations

·         Dictionaries and related operations

·         Sets and related operations

·         Functions, OOPs, Modules, Errors and Exceptions
·         Functions

·         Function Parameters

·         Global Variables

·         Variable Scope and Returning Values

·         Lambda Functions

·         Object-Oriented Concepts

·         Standard Libraries

·         Modules Used in Python

·         The Import Statements

·         Module Search Path

·         Package Installation Ways

·         Errors and Exception Handling

·         Handling Multiple Exceptions

  Lecture 16-22 Introduction To Statistics
Live Lecture 
·      Introduction To Statistic

·      Distributions and Hypothesis Tests

·      Distributions of One Variable

·      Hypothesis Testing

·      Typical Analysis Procedure

·      Data Screening and Outliers

·      Normality Check

·      Hypothesis Concept, Errors, p-Value, and Sample Size

·      Chi Square

·      ANOVA

·      Practical Exercise

  Lecture 23-24 Pandas
Live Lecture 
·      Pandas Introduction

·      Series

·      Data Frames

·      CSVs

·      Data from URLs

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Manipulating Data

·      Practical Exercise

  Lecture 25-27 NumPy
Live Lecture 
·      Mathematical Computing with Python (NumPy)

·      NumPy Introduction

·      NumPy DataTypes and Attributes

·      Creating NumPy Arrays

·      NumPy Random Seed

·      Viewing Arrays and Matrices

·      Manipulating Arrays

·      Standard Deviation and Variance

·      Reshape and Transpose

·      Dot Product vs Element Wise

·      Store Sales

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into NumPy Arrays

·      Practical Exercise

  Lecture-28 Introduction to Machine Learning
Live Lecture 
·      What Is Machine Learning?

·      AI/Machine Learning/Data Science

·      Practical Exercise

  Lecture 29-30 Machine Learning and Data Science Framework
Live Lecture 
·      Machine Learning Framework

·      Types of Machine Learning

·      Types of Data

·      Types of Evaluation

·      Features In Data

·      Modelling - Splitting Data

·      Modelling - Picking the Model

·      Modelling - Tuning

·      Modelling – Comparison

·      Practical Exercise

  Lecture-31 Data Science Environment Setup
Live Lecture 
·      Introducing Our Tools

·      Windows Environment Setup

·      Linux Environment Setup

·      Jupyter Notebook Walkthrough

·      Practical Exercise

  Lecture 32-35 Matplotlib + Seaborn: Plotting and Data Visualization
Live Lecture 
·      Matplotlib Introduction

·      Importing And Using Matplotlib

·      Anatomy Of A Matplotlib Figure

·      Scatter Plot And Bar Plot

·      Histograms And Subplots

·      Subplots Option 2

·      Plotting From Pandas DataFrames

·      Customizing Your Plots

·      Saving And Sharing Your Plots

·      Practical Exercise

  Lecture 36-40 Scikit-learn: Creating Machine Learning Models
Live Lecture 
·      Scikit-learn Introduction

·      Scikit-learn Cheatsheet

·      Typical scikit-learn Workflow

·      Debugging Warnings In Jupyter

·      Splitting Your Data

·      Clean, Transform, Reduce

·      Convert Data To Numbers

·      Handling Missing Values With Pandas

·      Handling Missing Values With Scikit-learn

·      Choosing The Right Model For Your Data

·      Practical Exercise

  Lecture 41-45 Scikit-learn - Regression Model
Live Lecture 
·      Types of Regression Algorithms

·      Simple Linear Regression

·      Multiple Linear Regression

·      Logistic Regression

·      Polynomial Regression

·      Support Vector Regression

·      Ridge Regression

·      Lasso Regression

·      ElasticNet Regression

·      Bayesian Regression

·      Decision Tree Regression

·      Random Forest Regression

·      Practical Exercise

  Lecture 46-48 Scikit-learn – Classification
Live Lecture 
·      Types of Classification Algorithms

·      Logistic Regression/Classification

·      K-Nearest Neighbours

·      Support Vector Machines

·      Kernel Support Vector Machines

·      Naive Bayes

·      Decision Tree Classification

·      Random Forest Classification

·      Practical Exercise

  Lecture 49-50 K Means Clustering
Live Lecture 
·      K-Means Clustering

·      A Simple Example of Clustering

·      Clustering Categorical Data

·      How to Choose the Number of Clusters

·      Pros and Cons of K-Means Clustering

·      To Standardize or not to Standardize

·      Relationship between Clustering and Regression

·      Market Segmentation with Cluster Analysis

·      Species Segmentation with Cluster Analysis

·      Advanced Statistical Methods - Other Types of Clustering

·      Types of Clustering

·      Dendrogram

·      Heatmaps

·      Practical Exercise

  Lecture 51-53 Text Mining
Live Lecture 
·      The concepts of text-mining

·      Use cases

·      Text Mining Algorithms

·      Quantifying text

·      TF-IDF

·      Beyond TF-IDF

·      Practical Exercise

  Lecture 54-58 Basic Time Series Forecasting
Live Lecture 
·      What is time series?

·      Techniques and applications

·      Time series components

·      Moving average

·      Smoothing techniques

·      Exponential smoothing

·      Univariate time series models

·      Multivariate time series analysis

·      Sentiment analysis in Python (Twitter sentiment analysis)

·      Text analysis

·      Rolling Mean For Detecting Temporal Variation

·      Simple Exponential Smoothing (SES)

·      Holt extended simple exponential smoothing

·      Holt Winters

·      Auto Regression Model (AR): Consider Previous Time Steps

·      Implement a Basic ARIMA Model

·      Automated ARIMA & Account for Seasonality (SARIMA)

·      Practical Exercise

  Lecture 59-60 Principal Component Analysis (PCA)
Live Lecture 
·      Primer

·      Measurement

·      Assumptions

·      Applied PCA work flow

·      Analysis of performance

·      Dimensionality reduction with (PCA)

·      Practical Exercise

  Case Studies
Live 
·      Case Study -1 Creating Application- oriented Analyses Using Tax Data

·      Preparing for the analysis of top incomes

·      Importing and exploring the world's top incomes dataset

·      Analyzing and visualizing the top income data of the India

·      Furthering the analysis of the top income groups of the India

·      Reporting with Jinja2                    

·      Case Study -2 Driving Visual Analyses with Automobile Data

·      Preparing to analyze automobile fuel efficiencies

·      Exploring and describing fuel efficiency data with Python

·      Analyzing automobile fuel efficiency over time with Python

·      Investigating the makes and models of automobiles with Python                       

·      Case Study -3 Working with Social Graphs

·      Preparing to work with social networks in Python

·      Importing networks

·      Exploring subgraphs within a heroic network

·      Finding strong ties

·      Finding key players

·      Exploring characteristics of entire networks

·      Clustering and community detection in social networks

·      Visualizing graphs             

·      Case Study -4 Recommending Movies at Scale

·      Modeling preference expressions

·      Understanding the data

·      Ingesting the movie review data

·      Finding the highest-scoring movies

·      Improving the movie-rating system

·      Measuring the distance between users in the preference space

·      Computing the correlation between users

·      Finding the best critic for a user

·      Predicting movie ratings for users

·      Collaboratively filtering item by item

·      Building a nonnegative matrix factorization model

·      Loading the entire dataset into the memory

·      Dumping the SVD-based model to the disk

·      Training the SVD-based model                

·      Case Study -5 Har vesting and Geo locating Twitter Data

·      Creating a Twitter application

·      Understanding the Twitter API v1.1

·      Determining your Twitter followers and friends

·      Pulling Twitter user profiles

·      Making requests without running afoul of Twitter's rate limits

·      Storing JSON data to the disk

·      Setting up MongoDB for storing the Twitter data

·      Storing user profiles in MongoDB using PyMongo

·      Exploring the geographic information available in profiles

·      Plotting geospatial data in Python            

·      Case Study -6 Optimizing Numerical Code with NumPy and Scipy

·      Understanding the optimization process

·      Identifying common performance bottlenecks in code

·      Reading through the code

·      Profiling Python code with the Unix time function

·      Profiling Python code using built-in Python functions

·      Profiling Python code using IPython's %timeit function

·      Profiling Python code using line_profiler

·      Plucking the low-hanging (optimization) fruit

·      Testing the performance benefits of NumPy

·      Rewriting simple functions with NumPy

·      Optimizing the innermost loop with NumPy