Data Science with Machine Learning Training in Chennai

machine-learning-training-in-Chennai

Leading Data Science with Machine Learning Training in Chennai

Learning data science will assist you comprehend how to take the raw data, evaluate it, and make some interesting discoveries. When you learn machine learning along with it, you will become an expert in artificial intelligence. Want to become a data scientist and machine learning expert? Then it’s time to enroll in the Top Data Science with Machine Learning Training Institute in Chennai.

Course Overview

Data science has evolved as an extension of statistics that is capable of handling the huge sums of data through computer science technologies. Generally, Machine Learning is confused with Data Science which is wrong. Even though Machine Learning is a prominent area of Data Science it is not the only one. Data science encompasses a huge spectrum of data technologies comprising Python, SQL, Spark, Hadoop etc. Through the Data Science with Machine Learning Certification training in Chennai you will learn the subtle differences between the two and what is their importance.

Perhaps the most famous data science methodologies arise from machine learning. The difference between machine learning and other computer guided decision processes is that the former develops prediction applying data. Some of the most famous products that utilize machine learning comprise the handwriting readers deployed by the postal service, movie recommendation systems, and speech recognition and spam detectors. In the Data Science with Machine Learning classes in Chennai you will get knowledge of popular machine learning algorithm and other factors.

To be precise, machine learning is valuable part of data science. In one way, we can tell that machine learning never would take place without big data.

Objective of Data Science with Machine Learning Certification Training in Chennai

Softlogic’s Machine Learning and Data Science course will assist you ace the data science and analytics applying various machine learning techniques.

Who can attend Data Science with ML Course

  • Data Analyst
  • Data Scientist who is willing to deploy Predictive Modeling
  • Teams beginning Data Science and ML project

Prerequisites for Data Science with Machine Learning Course

  • A background in Java and statistics is an added advantage. It will make the learning curve easy.
  • Knowledge of Python is beneficial

Machine Learning Course Fee and Duration

Duration
Hours
Training Mode

Regular Track

45 – 60 Days

2 hours a day

Live Classroom

Weekend Track

8 Weekends

3 hours a day

Live Classroom

Fast Track

5 Days

6+ hours a day

Live Classroom

The course fee for Data Science with Machine Learning Training in Chennai is reasonable. You have the flexibility to pay it in two installments. Are you having any issues with regard to time, place and duration of the training session? Then call our educational counselors and clear your queries.

Roles and Responsibilities of Machine Learning Engineer and Data Scientist

Machine Learning Engineer Roles:

  • Understand and transform Data science prototypes
  • Frame Machine Learning Systems
  • Research and execute relevant ML algorithms and tools
  • Develop machine learning applications as per requirements
  • Choose relevant Datasets and Data Representation Methods
  • Initiate Machine Learning Tests and Experiments
  • Carry out Statistical analysis and Fine-Tuning applying Test Results
  • Train Systems
  • Retrain systems when needed
  • Extend existing ML Frameworks and Libraries
  • Be updated with Developments in the Field

Data Scientist Roles:

  • Choosing features, Developing and Optimizing Classifiers applying Machine Learning Techniques
  • Comprehend the customer’s business requirement and lead them to a solution
  • Data mining applying cutting-edge methods
  • Processing, cleansing, and checking the integrity of data used for evaluation
  • Carry out Market Research
  • Gather data and Recognize Strength
  • Apply Deep Learning frameworks like MXNet, Tensorflow, Theano and Keras to develop Deep Learning models
  • Identify Trends, Patterns and Correlations in complex data sets
  • Find out new opportunities for process enhancement
  • Collaborate with Professional Services DevOps consultants to assist customers work with models after they are created

Machine Learning Training Course Syllabus

The syllabus is crafted as per industry standards. It is seen that the content of the syllabus is fresh and excellent. Since both data science and machine learning are equally important terms, the syllabus covers all the relevant topics with regard to both of them.

Module 1 – Core Java Fundamentals

  • Java Programming Language Keywords
  • Literals and Ranges of All Primitive
  • Data Types
  • Array Declaration, Construction, and Initialization

Module 2 – Declarations and Access Control

  • Declarations and Modifiers
  • Declaration Rules
  • Interface Implementation

Module 3 – Object Orientation, Overloading and Overriding, Constructors

  • Benefits of Encapsulation
  • Overridden and Overloaded Methods
  • Constructors and Instantiation
  • Legal Return Types

Module 4 – Flow Control, Exceptions, and Assertions

  • Writing Code Using if and switch statements
  • Writing Code Using Loops
  • Handling Exceptions
  • Working with the Assertion Mechanism
  • Write Java Programs

Module 5 – TestNG

  • Setting up TestNG
  • Testing with TestNG
  • Composing test and test suites
  • Generating and analyzing HTML test reports
  • Troubleshooting

Module 6 – Machine Learning 

  • Introducing Machine Learning
  • To Automate or Not to Automate?
  • Test Automation for Web Applications
  • Machine Learning Components
  • Supported Browsers
  • Flexibility and Extensibility

Module 7 – Machine Learning -IDE

  • Introduction
  • Installing the IDE
  • Opening the IDE
  • IDE Features
  • Building Test Cases
  • Running Test Cases
  • Debugging
  • Writing a Test Suite
  • Executing Machine Learning -IDE Tests on Different Browsers

Module 8 – XPATH

  • Understanding of Source files and Target
  • XPATH and different techniques
  • Using attribute
  • Text ()
  • Following

Module 9 – Machine Learning 

  • Introduction
  • How Machine Learning Works
  • Installation
  • Configuring Machine Learning With Eclipse
  • Machine Learning RC Vs Machine Learning
  • Programming your tests in WebDriver
  • Debugging WebDriver test cases
  • Troubleshooting
  • Handling HTTPS and Security Pop-ups
  • Running tests in different browsers
  • Handle Alerts / Pop-ups and Multiple Windows using WebDriver

Module 10 – Automation Test Design Considerations

  • Introducing Test Design
  • What to Test
  • Verifying Results
  • Choosing a Location Strategy
  • UI Mapping
  • Handling Errors
  • Testing Ajax Applications
  • How to debug the test scripts

Module 11 – Handling Test Data

  • Reading test data from excel file
  • Writing data to excel file
  • Reading test configuration data from text file
  • Test logging
  • Machine Learning Grid Overview

Module 12 – Building Automation Frameworks Using Machine Learning 

  • What is a Framework
  • Types of Frameworks
  • Modular framework
  • Data Driven framework
  • Keyword driven framework
  • Hybrid framework
  • Use of Framework
  • Develop a framework using TestNG/WebDriver

Fundamental Python Syllabus

Python – Overview
  • A brief history of python
  • Application and trends in python
  • Available python versions
Python – Environment Setup
  • Getting and installing python
  • Environmental variables and idle
  • Executing python from command line
Fundamentals
  • I/o
  • Naming conventions
  • Datatypes:
  • Numbers
  • String
  • List
  • Tuple
  • Dictionary
  • Set
Python Operators
  • List, Tuple, Dictionary, Set Methods
  • Statements: If, elif, Break, Continue
  • Loops: For loop, while loop
  • Functions

Python Operators

List, Tuple, Dictionary, Set Methods

Statements: If, elif, Break, Continue

Loops: For Loop, While Loop

Functions

Oops Concepts:

  • Class and objects
  • Getters and setters
  • Properties
  • Inheritance
  • Polymorphism
  • Special Functions of Python: Lambda, Map, Reduce, Filter

Modules in Python:

  • Math
  • Arrow
  • Geopy
  • Beautiful soup
  • Numpy
  • Sys
  • Os

Multithreading:

  • Introducing threads and life cycles
  • Priorities
  • Dead Locks

Exceptional Handling

  • Errors
  • Runtime errors
  • Exceptional model
  • Exceptional hierarchy
  • Handling multiple exception
  • Raise exceptions

File Handling

  • Text files
  • Csv files

Regular Expressions

  • Simple character matches
  • Flags, quantifers, greedy matches
  • Grouping and matching objects
  • Matching at beginning or end
  • Substituting and splitting a string
  • Compiling regular expressions
Generators
Iterators
Decorators
Closures
Gui Interfacing: Tkinter
  • Widgets
  • Integrated application
  • Mysql/with application
  • Converting .exe

Project 1:  Loops, oops concepts, threading

Analytical Python Syllabus

Introduction

Datascience Modules:

  • pandas
  • numpy
  • scipy
  • matplotlib

Python Data Processing

  • Python Data Operations
  • Python Data cleansing
  • Python Processing CSV Data
  • Python Processing JSON Data
  • Python Processing XLS Data
  • Python Relational databases
  • Python NoSQL Databases
  • Python Date and Time
  • Python Data Wrangling
  • Python Data Aggregation
  • Python Reading HTML Pages
  • Python Processing Unstructured Data
  • Python word tokenization
  • Python Stemming and Lemmatization

Python Data Visualization

  • Python Chart Properties
  • Python Chart Styling
  • Python Box Plots
  • Python Heat Maps
  • Python Scatter Plots
  • Python Bubble Charts
  • Python 3D Charts
  • Python Time Series
  • Python Geographical Data
  • Python Graph Data

Statistical Data Analysis

  • Python Measuring Central Tendency
  • Python Measuring Variance
  • Python Normal Distribution
  • Python Binomial Distribution
  • Python Poisson Distribution
  • Python Bernoulli Distribution
  • Python P-Value
  • Python Correlation
  • Python Chi-square Test
  • Python Linear Regression

Project 2: Tkinter-Gui

Skills Requirements for Data Scientist and Machine Learning Engineer

The skill requirements for these two professionals are very similar. Let’s see the common skillsets:

  • The foremost requirement is to have sound understanding on a programming language. Python is preferred as it has a simple learning curve and its application are exhaustive than any other language.
  • Though Python is an excellent language it alone cannot help you. You may perhaps have to learn C++, R, Python and Java and also operate with Map Reduce at some point.
  • Data Scientists and Machine Learning Engineers should know statistics. Acquaintance with Matrices, Matrix Multiplication and Vectors is required.
  • Data cleansing is a significant process that can assist organizations save time and raise the efficiency. The ability to tell compelling story with data is important to make sense of your point. If your finding can’t be rapidly identified, then you cannot get it through to others easily. Data visualization can have a great effect as far as the impact of your data is concerned.
  • Machine Learning and predictive modeling are turning out to be the two happening topics. Knowledge of Machine Learning techniques including supervised machine learning, logic regression, decision trees etc., is essential. These skills will assist you to solve various data analytical issues that are dependent on predictions of prominent organizational results.
  • Deep Learning has taken conventional Machine Learning approaches to an entirely different level. It has got inspiration from biological Neurons. The key lies in mimicking the human brain. A huge network of such artificial neurons is used. This is called as Deep Neural Networks.
  • A great amount of data is needed to train Machine Learning/Deep Learning Models. Deep Learning models were not possible earlier due to the lack of data and computational power. At present, a huge amount of data is produced at good speed. Hence we need frameworks including Spark and Hadoop to handle Big Data. At present, several companies are using Big Data analytics to obtain hidden business insights. It is hence an essential skill for a data scientist and Machine Learning Engineers.
  • The most successful projects should address the exact pain points. You should know the functioning of the industry and what will be advantageous for the business. Suppose a Machine Learning Engineer or a Data Scientist does not have business insight and the knowledge of aspects that lead to a successful business model, all those technical skills cannot be used in a productive manner.
  • Computer vision and machine learning are two important branches of computer science that can operate and drive very sophisticated systems. When you join the two, you can accomplish lot more.