Machine Learning for Data Science and Analytics

Why Machine Learning

Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range

The Outcomes of Machine Learning Training

This workshop will cover the basic algorithm that helps us to build and apply prediction functions with an emphasis on practical applications. attendees, at the end of this workshop, will be technically competent in the basics and the fundamental concepts of Machine Learning such as:

• Understand components of a machine learning algorithm.
• Apply machine learning tools to build and evaluate predictors.
• How machine learning uses computer algorithms to search for patterns in data
• How to uncover hidden themes in large collections of documents using topic modeling.
• How to use data patterns to make decisions and predictions with real-world examples
• from healthcare involving genomics and preterm birth
• How to prepare data, deal with missing data and create custom data analysis solutions for different industries
• Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming

The Objective of Machine Learning Training

To expose the Faculty/ Research Scholars/ Students in emerging technologies in the areas of Data Science & analytics. This workshop provides practical foundation level training that enables immediate and effective participation in Big data And Data Science and other Analytics projects.

This data science course is an introduction to machine learning and algorithms. Participants will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

Course Content

Introduction to Data Science

• types of Data
• Why Data
• Different types of Data
• Data Quality
• Law of Diminishing Returns
• Design for Scalability

Note: Extraction of Data

Introduction to analytics

Different Types of Analytics

Introduction to Intelligence

• Business Intelligence
• Artificial Intelligence (with respect to the software)
• Defining AI(Artificial Intelligence) and ML(Machine Learning)
• Expert System

Why these to used for (BI and ML) for Analytics

Statistics

• Causality of collinearity
• Types of Categorical predictors
• Contigency tables
• Non Casual Relationship
• Some current offerings of Statstics tools
• MathSoft

Neural Networks

• How predictions are made in neural network
• How Back propagation Learning works
• Data Preparation
• Combating Over fitting
• Applying and training the neural network
• Explaining the network
• Case Study Using  Weka Machine Learning Tool  and Using R Language

Genetic Algorithm

• What are genetic algorithm
• Where to Use Genetic algorithm in Analytics
• The General Idea
• How the Genetic algorithm works
• Mutation
• Epistasis
• Classifier Systems
• Case Study (Real time)

Business Analysis

• Reporting
• Managing
• Olap Tools
• Applications
• Power Builder

Pattern and Models

• Problem 1: Selection
• Problem 2: Acquisation
• Problem 3: Retention
• Problem 4: Extension
• The “Right” model?
• The Perfect model
• Missing Data
• Experimental Design
• Avoiding Bias

R

• Introduction to R
• Why R Language
• Basic Math
• Variable assignment
• Removing Variables with respect to R
• Numeric Data ( WRT to R)
• Character Data
• Dates
• Logical (with respect to R)
• Vectors

Data Visualization

• Principles
• parallel co ordinates
• Visualization of trees
• Advanced visual system
• Alta Analytics
• Silicon Graphics
• Business Objects

Weka Machine Learning – offline
Why Weka
Watson Analytics using Machine Learning !

Machine learning

Introduction to Data Mining

• What is data mining?
• Related technologies – Machine Learning, DBMS, OLAP, Statistics
• Data Mining Goals
• Stages of the Data Mining Process
• Data Mining Techniques
• Knowledge Representation Methods
• Applications
• Example – Weather Data

Data Warehouse and OLAP

• Data Warehouse and DBMS
• Multidimensional data model
• OLAP operations

Data Preprocessing

• Data cleaning
• Data transformation
• Data reduction
• Discretization and generating concept hierarchies
• Installing Weka 3 Data Mining System
• Experiments with Weka – filters, discretization

Data mining implementation for machine learning

• Task relevant data
• Background knowledge
• Interestingness measures
• Representing input data and output knowledge
• Visualization techniques
• Experiments with Weka – visualization

Attribute-oriented analysis

• Attribute generalization
• Attribute relevance
• Class comparison
• Statistical measures
• Experiments with Weka – using filters and statistics

Attribute-oriented analysis

• Attribute generalization
• Attribute relevance
• Class comparison
• Statistical measures
• Experiments with Weka – using filters and statistics

Mining algorithms: Association rules

• Motivation and terminology
• Example: mining weather data
• Basic idea: item sets
• Generating item sets and rules efficiently
• Correlation analysis
• Experiments with Weka – mining association rules

Mining algorithms: Classification

• Basic learning/mining tasks
• Inferring rudimentary rules: 1R algorithm
• Decision trees
• Covering rules
• Experiments with Weka – decision trees, rules

Mining algorithms: Prediction

• The prediction task
• Statistical (Bayesian) classification
• Bayesian networks
• Instance-based methods (nearest neighbor)
• Linear models
• Experiments with Weka – Prediction

Mining algorithms: Prediction

• The prediction task
• Statistical (Bayesian) classification
• Bayesian networks
• Instance-based methods (nearest neighbor)
• Linear models
• Experiments with Weka – Prediction

Mining real data

Applying various data mining techniques to create a comprehensive and accurate model of the data. which could be analyzed and implemented for Machine learning Using   R

Documentation

• ID – Parent Database, with M-n relationship
• that is one to many
• Partitioning methods: k-means, expectation maximization (EM)
• Hierarchical methods: distance-based agglomerative and divisible clustering
• Conceptual clustering: Cobweb
• Experiments with Weka – k-means, EM, Cobweb

Advanced techniques, Clustering, Machine Learning software and applications

Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).Bayesian approach to classifying text

Web mining: classifying web pages, extracting knowledge from the web
Machine Learning software and applications.

Why Deep Learning and Artificial Intelligence

Artificial intelligence is the future of computer science, and the future of technology. Its impact will be almost immeasurable. The field is wide open today, with so much to learn, and so many ways to contribute.

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.

Some representations are loosely based on interpretation of information processing and communication patterns in a biological nervous system, such as neural coding that attempts to define a relationship between various stimuli and associated neuronal responses in the brain.Research attempts to create efficient systems to learn these representations from large-scale, unlabeled data sets.

What you Will Learn

Learn how to use Anaconda and Jupyter for data analysis. Also, go through some key math concepts behind deep learning. Vincent Vanhoucke, Principal Scientist at Google Brain, introduces you to deep learning and Tensorflow, Google’s open source library for machine intelligence. Learn how to create your first neural network.

 Course Content

1. Introduction Of Machine Learning and Artificial Intelligence

• Introduction of Machine Learning
• Introduction of Artificial Intelligence
• Why machine learning
• Classification of Machine Learning
• Difference between Machine Learning and Artificial Intelligence
• Machine Learning Techniques
• Types of Learning
• Machine Learning System Design
• Supervised Learning- Regression Classification
• Future scope, Machine Learning And Artificial Intelligence

2. Introduction Neural Network

• BASIC introduction Neuron
• Activation function
• The Neuron Diagram
• Neuron Models
• step function
• ramp function
• sigmoid function
• Gaussian function
• single-layer feed-forward
• multi-layer feed-forward,Recurrent,
• Perceptron, multilayer network,backpropagation,
• introduction to deep neural network

3. Python/Anaconda

• Introduction to python and anaconda
• Conditional Statements
• Looping, Control Statements
• Lists, Tuple ,Dictionaries
• String Manipulation
• Functions
• Installing Packages
• Introduction of Various Tool
• Introduction of Anaconda
• Working on spyder ,Jupyter notebook

4. Working on Various Python Library

• Installing library and packages for machine learning and data science
• Matplotlib
• Scipy and Numpy
• Pandas
• IPython toolkit
• scikit-learn

5. TensorFlow

• Introduction of Tensorflow
• Basics of TensorFlow
• Graph in TensorFlow
• TensorFlow Session
• Placeholders,Constants,Variables
• Common Data Stored in Tensors
• Linear and Logistic Regression in TensorFlow
• image classifier using convolutional neural network

6. Project Machine Learning for recognizing hand written digits (MNIST dataset)

• Simple Spam-Detecting Machine Learning Classifier
• Modern Face Recognition with Deep Learning
• Case Study: Cancer Detection
• Case Study: Character Recognition
• Case Study: Iris Clustering
• Image Segmentation using deconvolution layer in Tensorflow
• Develop a predictive analytics model for a complex data set

Highlights of Big Data & Hadoop

• Implement a Hadoop Project
• Learn to write Complex MapReduce programs
• Perform Data Analytics using Pig and Hive
• Understand Data Loading Techniques using SQOOP and Flume
• Master the concepts of Hadoop Distributed File System and MapReduceFramework
• Work on a Real Life Project on Big Data Analytics and gain Hands on Project Experience

Big Data &Hadooop Projects details

• Word Count for a large amount of data With Using (MapReduce , Pig , Hive )
• Temperature Sensor Conversation With Using Pig or HQL
• DATA Sorting With Large data Set With Pig and Sqoop
• Write Complex MapReduce programs
• Master the concepts of Hadoop Distributed File System and MapReduce Framework
• Work on a Real Life Project on Big Data Analytics and gain Hands on Project Experience

Big Data & Hadoop Course Content

A.Introduction to Big Data and Hadoop

• What is a Data?
• Type of Data
• Need of Big Data
• Characteristics of Big Data

B.Different Components of Hadoop

C.Big Data Technology

• Traditional IT approach
• Big Data Capabilities
• Milestones of Hadoop

A. Software Introduction

• VMware Player
• VMware installed with BIOS system
• Horton Works Sand Box Introduction

B. Hadoop Architecture

• Hadoop cluster
• Hadoop Core Services
• Hadoop Core Components
• Map reduce Introduction
• HDFS

A. Starting With Hadoop

• Map reduce Analogy
• Map reduce Example
• Map Execution
• Real time Using With Hadoop

B. Pig

• Introduction to Apache Pig
• Components of pig
• How to Works pig, or Data model
• Pig vs. SQL
• Pig Execution Modes

A.Pig

• Map Reduce vs. Apache Pig
• Different Data Types in Pig
• Modes of Execution in Pig
• Local Mode
• Execution Mechanism
• Grunt Shell
• Scrip
• Pig Commands
• Examples Of pig
• Word Count
• Batting Examples

A.SQOOP

• Introduction to SQOOP
• MySQL Client and Server Installation
• How to Connect to Relational Database
• Using SQOOP
• Different SQOOP Commands
• Different Flavors of Import
• Export
• HIVE Imports

B.Introduction Of Zookeeper

• Features of Zookeeper
• Use of ZooKeeper
• Zookeeper Data Model

Who should go for this Course?

Predictions say 2017 will be the year Hadoop finally becomes a cornerstone of your business technology agenda. To stay ahead in the game, Hadoop has become a must-know technology for the Graduates aiming to build a career in Big Data.

What are the other requirement for this training program?

• Seminar hall/classroom having the enough capacity to conduct hands-on-session for all participants.
• Good Quality public address system ideally two cordless mikes will be required.
• Projector/ Screen along with black/white board for teaching and presentation purposes.
• This training center can only be arranged for a minimum of 50 students
• Accommodation to our Hadoop Expert.

Objectives of IOT using Arduino Training

Internet of Things,or IOT in short, is the idea of making devices and objects smarter by linking them to the internet. This workshop introduces you to the amazing world of IOT and its fascinating applications. Using Arduino development kit, you will develop an electronic device that streams temperature and humidity data over the internet. You can program the system in such a way that say whenever the temperature exceeds a certain limit, the device will automatically send an email notification!

Knowledge and Understanding after completion of this course

• knowledge and understanding of fundamental IOT paradigms, architectures, possibilities and challenges, both with respect to software and hardware,
• A wide competence from different areas of technology, especially from computer engineering, robotics, electronics, intelligent systems .
• Learn the basics of Internet of Things and its applications.
• What “the Internet of Things” means and how it relates to Cloud computing concepts.
• How open platforms allow you to store your sensor data in the Cloud.
• The basic usage of the Arduino environment for creating your own embedded projects at low cost.
• How to connect your Arduino with your Android phone.
• How to send data to the Internet and talk to the Cloud.
• How to update sensor readings on Twitter (Social Networking Sites).
• Control a Relay Switch by texting from your Phone.

Internet of Things Projects details

• Project 1: Simple LED Program for Arduino
• Project 2: Integrating Sensors & Reading Environmental Physical Values.
• Project 3: Reading Environmental Values on Android Smartphone.
• Project 4: Voice Controlled Mini Home Automation using Android Smartphone
• Project 5: Control Devices using Localhost Web Server for Home Automation.
• Project 6: Creating own Android App using MIT App Inventor & controlling Arduino connected devices.
• Project 7: Being Social on Twitter & update status on Twitter through Arduino
• Project 9: Use Arduino to Upload free data from Environmental Sensors to Cloud Server.
• Project 10: Automatically Tweet Sensor Data on Twitter.
• Project 11: Receive Automatic Call Notification on Mobile Phone for Burglar Alarm using IoT Platform.
• Project 12: Control Electronic Devices from anywhere across the world using Internet & Mobile App.

Why Internet of Things (IOT) Training from FDP ?

• 12 Major Projects will be covered in this Training.
• Our syllabus is professionally designed to cover Basic as well as Advance aspects of IOT using Arduino
• Each day of our training is well planned to provide you with Theoretical as well as Practical Knowledge of the module
• Each day will come up with New Practical & Projects which makes the training interesting and exciting.
• Time to time Practical Assignments will be provided to the students, which will help them in doing practice at home.
• Revision Time & Query Sessions are provided to the students which help them in clearing previous doubts.
• Exam will be conducted at the end of basic as well as Advance module to test the knowledge level of the students.

Kit For Internet of Things Development

• Arduino Uno Rev 3 (Made in Italy)
• USB Cable
• ESP8266 (ESP01) Serial WiFi Module
• Breadboard (Regular)
• Assorted Jumper Wires (20)
• DHT 11
• Assorted LEDs (10)
• Switches
• Registers
• Software tools and firmware

Therory & Hands on Topics of IOT with Arduino Training

Introduction to the Internet of Things

• The Internet of Things
• Introduction to Cloud Computing
• The Basics of Sensors & Actuators

The Arduino Platform

• The Arduino Open-Microcontroller Platform
• Arduino Basics
• Arduino Board Layout & Architecture
• Arduino Programming & Interface of Sensors
• Interfacing sensors with Arduino Programming Arduino
• Reading from Sensors

Project 1: Simple LED Program for Arduino
Project 2: Integrating Sensors & Reading Environmental Physical Values.

Interfacing sensors with Arduino Programming Arduino

• Talking to your Android Phone with Arduino
• Connecting Arduino with Mobile Device.
• The Android Mobile OS.
• Using the Bluetooth Module

Project 3: Home Automation using Android Smartphone
Project 4: Reading Environmental Values on Android Smartphone

Project 5: Tweet Sensor Reading through your phone on Twitter.

• Creating App on Twitter
• Integrating Arduino to talk to Twitter via Internet.
• Cloud Computing.
• Communicating with the Cloud using Web Services.
• Cloud Computing & IoT.
• Popular Cloud Computing Services for Sensor Management.
• IT SERVICES & SOLUTIONS
• TECHNICAL EDUCATION
• AUTOMATION SERVICES AND SOLUTIONS

Introduction to Interrupts

• What is interrupts
• Application of Interrupts
• Registers of Interrupts Different Modes
• Explaining ARDUINO Serial communication

Project 6: Sending Arduino Data to your Cloud Application.
Project 7: Use Arduino to Upload free data from Environmental Sensors.

Who should go for this Course?

Faculty/Students from B.E/B.Tech/M.Tech/Diploma (ECE/EEE/CSE/IT/MECH) can join this training. Anyone who have interest in this field and have pre-requisite knowledge.

What are the other requirement for this training program?

• Seminar hall/classroom having the enough capacity to conduct hands-on-session for all participants.
• Good Quality public address system ideally two cordless mikes will be required.
• Projector/ Screen along with black/white board for teaching and presentation purposes.
• This training center can only be arranged for a minimum of 50 students
• 1-week accommodation to our IOT Expert.

Internet Of Things using Raspberry Pi

Objective:

Internet of Things, or IoT in short, is the idea of making devices and objects smarter by linking them to the internet.
This workshop introduces you to the amazing world of IoT and its fascinating applications. Using a Raspberry Pi computer and a DHT sensor, you will develop an electronic device that streams temperature and humidity data over the internet. You can program the system in such a way that say whenever the temperature exceeds a certain limit, the device will automatically send an email notification!

Workshop Outcomes

• Learn the basics of Internet of Things and its applications
• Build your computer using Raspberry Pi platform
• Work with DHT sensors to detect humidity and temperature
• Setup IoT connectivity using a remote desktop
• Understand Raspbian OS, Python programming, SMTP and API
• Develop and test an IoT weather monitoring station

Kit Content

• Raspberry Pi
• DHT Sensor
• Resistor
• Breadboard
• Connecting wires
• Ethernet cable(LAN) and MicroUSB cable*
• microSD memory card and SD memory card adapter

All the above components would be provided during the program to participants in groups of 2 but would be taken back at the end. If participant want to buy then cost will be Rs5000/-per kit

 IOT Training Outcomes

What is IOT?

Learn: IoT – an Introduction?
Learn: IoT for Weather Monitoring?
Review: What is IoT?

Build your computer using Raspberry Pi

Learn: What is Raspberry Pi?
Do: Raspberry Pi Board
Do: Installing OS on your Raspberry Pi?

Sense Temperature and Humidity

Learn: Sensors
Learn: Temperature and Humidity Sensors
Do: How to work with DHT 11 Sensor
Review: How to sense Temperature and Humidity

Establish Remote Desktop Connection

Do: Internet Sharing from PC to Raspberry Pi
Do: Setup SSH connection using Putty
Do:Remote server access using VNC server

Raspbian OS

Do: Tools and Applications – An Introduction

Python Programming

Learn: An Introduction

Simple Mail Transfer Protocol

Learn: An Introduction
Do: Installation in Raspberry Pi
Do: Send simple Gmail – Python Programming

Application Programming Interface

Learn: An Introduction

Building IOT Weather Station

Do: Connecting DHT 11 sensor with Raspberry Pi
Do: Programming the Raspberry Pi for IOT

Testing

Do: Testing the IoT device

 IOT using Raspberry Pi Course Contents

Introduction

• Introduction to Raspberry Pi
• Different Models of Raspberry Pi
• Why Raspberry Pi.
• Peripherals of Raspberry Pi.
• Applications of Raspberry Pi.
• Future of Micro Computing.
• Preparing Your Raspberry Pi for First Use
• Different Operating Systems for Raspberry pi.
• Getting Started With NOOBS
• Getting things ready for first use
• NOOBS OS inside out
• Booting for the First time.

Introduction to Microcontroller

• Diff B/w Microcontroller and Microprocessor
• Introduction to Raspberry Pi
• Architecture and Hardware specifications
• Introduction to ARM
• GPIOs

Introduction to Programming:

• Introduction to Scratch Programming
• History and basic of Scratch
• My first program on scratch
• Led Blinking using Scratch

Introduction to Python programming Language

• History and Basic of Python
• My first program on scratch
• Led Blinking using Scratch

Interrupts

• Interrupt concept
• NVIC on Arm Cortex M4.
• Edge Trigger and SysTick Interrupt.
• Writing Interrupt Service Routine

Setting Up for a Perfect Pi Experience

• Operation Procedures.
• Updating Pi to Latest software’s.
• Setting various Options and Personalizing.
• First introduction to the LINUX terminal.
• Introduction to the Open Source Software Library.

Introduction to LINUX Environment

• The Linux Organization Structure.
• LINUX Shell.
• SHELL Scripting.
• Getting Familiar with the GPIO Pins of your Pi
• Pin numbering Formats
• The Voltage hazard Information.
• The LED Interfacing.
• The First Button Interface with Raspberry Pi.
• General information on other pins and their functionality

Hands-on session will include

• Setting up Raspberry PI
• Flashing the loading the SD card with the OS
• Booting the OS
• Intro of items on the desktop (Debian Linux/ Wheezy)
• Enabling GPIO pins
• LED interfacing using the GPIO

What are the other requirements for this training program?

• Seminar hall/classroom having the enough capacity to conduct hands-on-session for all participants.
• Good Quality public address system ideally two cordless mikes will be required.
• Projector/ Screen along with black/white board for teaching and presentation purposes. .
• This training center can only be arranged for a minimum of 50 students
• Accommodation to our IOT Expert