Machine Learning

Machine Learning

Machine Learning Introduction

  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
  • Clustering
  • 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
  • Case study
  • Implementing Association rule mining
  • Case study
  • Understanding Process flow of Supervised Learning Techniques
  • Decision Tree Classifier
  • How to build Decision trees
  • Case study
  • Random Forest Classifier
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Case study
  • Naive Bayes Classifier
  • Case study
  • Project Discussion
  • Problem Statement and Analysis
  • Various approaches to solving a Data Science Problem
  • Pros and Cons of different approaches and algorithms
  • Linear Regression
  • Case study
  • Logistic Regression
  • Case study
  • Text Mining
  • Case study
  • Sentimental Analysis
  • Case study

Python

Getting Started with Python

  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc
  • Starting Python
  • Interpreter PATH
  • Using the Interpreter
  • Running a Python Script
  • Python Scripts on UNIX/Windows, Editors and IDEs
  • Using Variables
  • Keywords
  • Built-in Functions
  • StringsDifferent Literals
  • Math Operators and Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line Parameters and Flow Control

Sequences and File Operations

  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a Sequence
  • Functions for all Sequences
  • Using Enumerate()
  • Operators and Keywords for Sequences
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets

Deep Dive – Functions Sorting Errors and Exception Handling

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections, Dictionaries and Lists in Place
  • Errors and Exception Handling
  • Handling Multiple Exceptions
  • The Standard Exception Hierarchy
  • Using Modules
  • The Import Statement
  • Module Search Path
  • Package Installation Ways

Regular Expressionist’s Packages and Object – Oriented Programming in Python

  • The Sys Module
  • Interpreter Information
  • STDIO
  • Launching External Programs
  • path directories and Filenames
  • Walking Directory Trees
  • Math Function
  • Random Numbers
  • Dates and Times
  • Zipped Archives
  • Introduction to Python Classes
  • Defining Classes
  • Initializers
  • Instance Methods
  • Properties
  • Class Methods and Data Static Methods
  • Private Methods and Inheritance
  • Module Aliases and Regular Expressions

Debugging, Databases and Project Skeletons

  • Debugging
  • Dealing with Errors
  • Using Unit Tests
  • Project Skeleton
  • Required Packages
  • Creating the Skeleton
  • Project Directory
  • Final Directory Structure
  • Testing your Setup
  • Using the Skeleton
  • Creating a Database with SQLite 3
  • CRUD Operations
  • Creating a Database Object.

Machine Learning Using Python

  • Introduction to Machine Learning
  • Areas of Implementation of Machine Learning
  • Why Python
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning
  • Learning NumPy
  • Learning Scipy
  • Basic plotting using Matplotlib
  • Machine Learning application

Supervised and Unsupervised learning

  • Classification Problem
  • Classifying with k-Nearest Neighbours (kNN)

Algorithm

  • General Approach to kNN
  • Building the Classifier from Scratch
  • Testing the Classifier
  • Measuring the Performance of the Classifier
  • Clustering Problem
  • What is K-Means Clustering
  • Clustering with k-Means in Python and an

Application Example

  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions
  • Converting Different Formats
  • Combining Data from Various Formats
  • Slicing/Dicing Operations.

Scikit and Introduction to Hadoop

  • Introduction to Scikit-Learn
  • Inbuilt Algorithms for Use
  • What is Hadoop and why it is popular
  • Distributed Computation and Functional Programming
  • Understanding MapReduce Framework Sample MapReduce Job Run

Hadoop and Python

  • PIG and HIVE Basics
  • Streaming Feature in Hadoop
  • Map Reduce Job Run using Python
  • Writing a PIG UDF in Python
  • Writing a HIVE UDF in Python
  • Pydoop and MRjob Basics

Python Project Work

  • Real world project

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