Indian Cyber Security Solutions

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Diploma in Machine Learning and AI in India

D|ML & AI Course

Diploma in Machine Learning and AI in India from Indian Cyber Security Solutions is the most demanded training in India as well as across globe. Machine Learning is a very important and inevitable part of knowledge in today’s World. Diploma in Machine Learning by ICSS will help you to lead the AI-driven technological revolution.

Objective of Machine learning training in India course is to impart in depth knowledge in Machine Learning & Artificial Intelligence and its applications using tools. Get trained by industry experts of data science. More than 200+ students got placed in a different place from ICSS. By leveraging Diploma in Machine Learning and AI for Industry you will be exposed to numerous high-paying job opportunities. Indian Cyber Security Solutions as a renowned industry in the country is thus providing the best Diploma in machine learning online Training.

PG Diploma in Machine Learning and AI
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1,09,233 Students Enrolled

D|ML & AI - Diploma in Machine Learning and AI

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. As of 2019, many sources continue to assert that machine learning remains a subfield of Artificial Intelligence.

Machine learning is closely related to computational statistics. Its applications across business problems, machine learning is also referred to as predictive analytics. As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. The job market for machine learning specialists is expected to grow 56% over the next few years, with a significant concentration of the jobs found in the Greater Toronto Area. This is the right time to grow your knowledge in the field of Machine Learning & Artificial Intelligence. Join Indian Cyber Security Solutions for Diploma in Machine Learning and AI in India course and get deep knowledge from professional Data Scientist. You can work with both structured and unstructured data with various supervised, unsupervised and reinforcement learning algorithms after completion of the course. Our Diploma in Machine Learning and AI in India course will help to reach your career goals.


Class Room Training on PG Diploma in Machine Learning and AI

PG Diploma in Machine Learning and AI with hands-on training in the lab from the professional Data Scientist. Data Science & Machine Learning training is in huge demand as organizations are going online with more than 170 Billion Doller investment in the Machine Learning & Artificial Intelligence domain worldwide. At Indian Cyber Security Solutions, you will learn in-depth on Machine Learning & Data Science & learn to create AI tool by using machine learning from our experts. Machine Learning training is designed is such a way that you get the maximum practical knowledge. At our training center, we bring in guest faculties from the industry so they can share their practical experience with you. At the end of the course, we aim to make your placement ready.

Online LIVE Training on PG Diploma in Machine Learning and AI in India

Indian Cyber Security Solutions have 130+ trainers who are professional Data Scientist working in different MNCs like Infosys, Cognizant, Wipro, ATOS, Intel and are also members of our research & development team. All the trainers are geographically located in different areas provide online training on Machine Learning. All the machine learning training classes are held through an application where the faculty and the students are LIVE interacting with each other over the internet. We provide online Live Training on Machine Learning. Most importantly, all the classes are recorded and uploaded in our online portal where all students have lifetime access. We guarantee your satisfaction or we pay back your course fee. There will be 3 instructors dedicated for individual batch which will carry on for 3 months.


Eligibility Criteria to become a Professional Data Scientist & Machine Learning Engineer

The prime objective of this course is to make you ready for the industry where you can use your skills. After completion of this course, you will be able to successfully create AI tools by using machine learning. Any graduate with knowledge of basic programming languages can apply for this course.

Technical Educational Background

You need to be from engineering / science/ Maths / Stats background to understand the theory and the techniques used in machine learning. Prior programming experience would be very useful. Machine Learning is all about Stats and Probability. So, having a prior knowledge in that area would come very handy. it will be necessary for you to learn how to program in a programming language such as Python. You will get python programming course in this course bundle.


Non-Technical Educational Background

You can get into data science & machine learning from a non-technical background. Machine learning makes use of a lot of programming and mathematics. However, even if you are not a technical person, it will be possible for you to learn machine learning if you follow the right path.



Why you should choose ICSS ?

Industry professions from Amazon, Cognizant & Intel will share their practical experience in the class

100% practical and lab-based classes (available online & offline)


25% Scholarship program for merited students with a minimum of 90% marks in their board exams


LIFETIME access to video tutorials, case studies


EMI option is available if you go for 2 or more courses.



Get educational loan @  O% interest



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PGD|ML & AI - Course Module


  • Introduction to Data Science / R Programming
  • R Programming: Basics 
  • Data Wrangling and Manipulation
  • Inferential Stats: Hypothesis testing: z-test, t-test, ANOVA & Chi-square
  • Predictive Modelling
  • Project 
Introduction to Data Science / R Programming
R Programming: Basics 
  • Syntax
  • Data Structures
  • Loops & Conditional Statements and User Defined functions

Data Wrangling and Manipulation
  • Import the Data in R
  • Export the Data in CSV / Excel
  • Data Exploration: Filtering / Sorting Merging
  • Structural Description of the Data
  • Data Manipulation
  • Statistical Description of the Data
  • Visualization

Inferential Stats: Hypothesis testing: z-test, t-test, ANOVA & Chi-square
Predictive Modelling
  • Regression: Linear & Logistic
  • Classification: DT, KNN NB SVM & RF
  • Dimentionality Reduction: PCA
  • Clustering: Hierarchical & K Means
  • Association Mining
Project 
  • Introduction of Python 
  • Installation of Python 
  • Basics of Python
  • Python Strings
  • Python Lists 
  • Python Tuples
  • Python Dictionary
  • Python Set
  • Python Control Statement 
  • Python Functions 
  • File Handling 
  • Python OOPS Concept  
  • Python Modules  
  • Python Exceptions   
  • Python Date   
  • Python Network Programming     
  • Database Connectivity using MqSQL DB   
Introduction of Python 
  • What is Python
  • Python History
  • Python 2.x vs 3.x
  • Features of Python
  • Applications of Python

Installation of Python 
  • How to install python
  • Python Script Mode
  • Python GUI Mode
  • Python Interactive Mode
  • Python in Linux
  • Linux Script Mode
  • Linux GUI Mode
  • How to install IDLE in Linux
  • How to set path
Basics of Python
  • Python “Hello World”
  • How to Execute Python
  • Variables in python
  • Keywords in python
  • Identifiers in python
  • Literals in python
  • Operators in python
  • Comments in python
Python Strings
  • Accessing Strings
  • Strings Indexing
  • Slice Notation
  • String Built-in Functions
  • Slice Notation

Python Lists 
  • How to define list
  • Accessing list
  • Elements in a Lists
  • List Operations
  • Adding Lists
  • List slicing
  • Updating elements in a List
  • Appending elements to a List
  • Deleting Elements from a List
  • Functions and Methods of Lists

Python Tuples
  • How to define a tuple
  • Accessing tuple
  • Elements in a tuple
  • Tuple Operations
  • Tuple slicing
  • Deleting tuple
  • Functions and Methods of tuple
Python Dictionary
  • How to define dictionary
  • Accessing Dictionary
  • Updation
  • Deletion
  • Functions and Methods
Python Set
  • How to define Set
  • Accessing Set
  • Set Built-in Functions
  • Set Operations


Python Control Statement 
  • “If” in python
  • “If else” in python
  • “else if” in python
  • “nested if” in python
  • “for loop” in python
  • “while loop” in python
  • “break” in python
  • “continue” in python
  • “pass” in python
Python Functions 
  • Defining a Function
  • Invoking a Function
  • return Statement
  • Argument and Parameter
  • Passing Parameters
  • Default Arguments
  • Keyword Arguments
  • Anonymous Function
  • Difference between Normal Functions and Anonymous Function
  • Scope of Variable
File Handling 
  • Operations on Files
  • Opening file
  • closing file
  • reading file
  • writing file
  • Modes of files
  • Methods in files
Python OOPS Concept  
  • Python OOPs Concepts
  • Python Object Class
  • Python Constructors
  • Python Inheritance
  • Multilevel Inheritance
  • Multiple Inheritance
Python Modules  
  • Importing a Module
  • Example of importing multiple modules
  • How to use “from” import statement
  • import whole module
  • Built-in Modules in Python
  • Package
Python Exceptions   
  • What is Exception handling
  • Declaring Multiple Exception
  • Finally Block
  • Raise an Exception
  • Custom Exception
Python Date   
  • Import datetime
  • 100 Current datetime
  • 101 Use of strftime method
Python Network Programming     
  • Basics of networking
  • What is the socket?
  • creating server
  • creating client
Database Connectivity using MqSQL DB   
  • What is the database?
  • How to connect with the database?
  • Select data from the database?
  • insert data into the database?
  • delete data from the database?

  • Module 1: Features of Python
  • Module 2: Data Structures
  • Module 3: Concept of Modules 
  • Module 4: OOPS Concept
  • Module 5 
  • Exam on Python 
  • Module 6: Numpy 
  • Module 7: Basic of Pandas
  • Module 8: Data Visualization with Matplotlib & Seaborn  
  • Module 9: Supervised Learning 1  
  • Module 10: Machine Learning vs Statistical Modelling   
  • Module 11: Unsupervised Learning 
  • Module 12: Logistic Regression Algorithm    
  • Module 13: Practice Linear & Logistic Regression with Different Dataset    
  • Module 14: K Means & Hierarchical Clustering Algorithm    
  • Module 15: PCA & Decision Tree Algorithm    
  • Module 16: Random Forest Algorithm    
  • Module 17: K-Nearest Neighbor Algorithm   
  • Module 18: Support Vector Machine & Natural Language Processing with Naive Bayes   
  • Doubt Clear Session
  • Final Project with Documentation
Module 1: Features of Python

Using the Python Interpreter

  • Invoking the Interpreter
  • The Interpreter and Its Environment

Introduction to Anaconda

  • Anaconda Navigator
  • Anaconda Prompt
  • IPython Console
  • Jupyter qt-console
  • Jupyter Notebook
  • Spyder

An Informal Introduction to Python

  • Using Python as a Calculator
  • First Steps Towards Programming

Installation to Anaconda and Control Flow Tools

  • if Statements
  • for Statements
  • The range() Function
  • break and continue Statements, and else Clauses on Loops
  • pass Statements
  • Defining Functions
  • More on Defining Function
  • Function special attributes
  • Coding Style

Module 2: Data Structures
  • Lists
  • The del statement
  • Tuples and Sequences
  • Sets
  • Dictionaries
  • Looping Techniques
  • More on Conditions
Module 3: Concept of Modules 
  • Standard Modules
  • The dir() Function
  • Packages  Input and Output Reading and Writing Files
Module 4: OOPS Concept
  • Object: properties and operations
  • Class as a blueprint for objects
  • Fields: Python convention for defining private fields
  • Constructors: overloading and chaining
  • Designing static properties, operations, and blocks in Python
  • Special method names in a class and their uses (e.g. __new__, __del__, __str__ etc.)
  • Single and multiple inheritances
  • Method overriding and polymorphism
  • Iterators
  • Errors and Exceptions
  • Syntax Errors
  • Exceptions
  • Handling Exceptions
  • Raising Exceptions

Input & Output

  • Fancier Output Formatting
  • Reading and Writing Files
Module 5 
  • Single and multiple inheritances
  • Method overriding and polymorphism
  • Iterators
  • Generators
  • Generator Expressions
  • Errors and Exceptions
  • Syntax Errors
  • Exceptions
  • Handling Exceptions
  • Raising Exceptions
  • User-defined Exceptions
  • Defining Clean-up Actions
  • Predefined Clean-up Actions


Exam on Python 
Module 6: Numpy 
  • An example
  • Array Creation
  • Printing Arrays
  • Basic Operations
  • Universal Functions
  • Indexing, Slicing, and Iterating

Shape Manipulation

  • Changing the shape of an array
  • Stacking together different arrays
  • Splitting one array into several smaller ones

Copies and Views

  • No Copy at All
  • View or Shallow Copy
  • Deep Copy
  • Functions and Methods Overview

Broadcasting rules

Fancy indexing and index tricks

  • Indexing with Arrays of Indices
  • Indexing with Boolean Arrays
  • The ix_() function
  • Indexing with strings

Linear Algebra

  • Simple Array Operations

Tricks and Tips

  • “Automatic” Reshaping
  • Vector Stacking
  • Histograms
Module 7: Basic of Pandas

Introduction to Pandas Data Structures

  • Series
  • DataFrame
  • Index Objects Reindexing
  • Dropping entries from an axis
  • Indexing, selection, and filtering
  • Arithmetic and data alignment
  • Function application and mapping
  • Sorting and ranking
  • Axis indexes and duplicate values Filtering out missing Data
  • Filling out missing Data


Essential Functionality

  • Reindexing
  • Dropping entries from an axis
  • Indexing, selection and filtering
  • Arithmetic and data alignment
  • Function application and mapping
  • Sorting and ranking
  • Axis indexes and duplicate values

Summarizing and Computing descriptive statistics

  • Correlation and Covariance
  • Unique values, Value Counts and  Membership

Handling missing data

  • Filtering out missing Data
  • Filling out missing Data

Hierarchical Indexing

  • Reordering and sorting levels
  • Summary statistics by level
  • Using a data frame's column

Other Pandas topics

  • Integer indexing
  • Panel Data
Module 8: Data Visualization with Matplotlib & Seaborn  
  • What is SQL-Injection ?
  • Types of SQL-Injection
  • Live Demo on SQL-Injection
  • Business Logic bypass technique
Module 9: Supervised Learning 1  
  • K-Nearest Neighbours
  • Decision Trees 
  • Random Forests
  • Reliability of Random Forests 
  • Advantages & Disadvantages of Decision Trees
  • Project-1 on data analysis with documentation
Module 10: Machine Learning vs Statistical Modelling   


  • Machine Learning Languages, Types, and Examples 
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning 
  • Supervised Learning Classification 
  • Unsupervised Learning 

Supervised Learning II

  • Regression Algorithm (Linear, Multiple, Polynomial)
  • Model Evaluation 
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models 

Module 11: Unsupervised Learning 
  • K-Means Clustering plus Advantages & Disadvantages
Module 12: Logistic Regression Algorithm    
Module 13: Practice Linear & Logistic Regression with Different Dataset    
Module 14: K Means & Hierarchical Clustering Algorithm    
Module 15: PCA & Decision Tree Algorithm    
Module 16: Random Forest Algorithm    
Module 17: K-Nearest Neighbor Algorithm   
Module 18: Support Vector Machine & Natural Language Processing with Naive Bayes   
Doubt Clear Session
Final Project with Documentation

Class Room Training

Students Enrolled73%

Course Fee

INR 40,000 / - + 18% GST

Installment Division:

1st - INR 10,000/-

2nd - INR 10,000/-

3rd - INR 20,000/-

Course Duration:

9 Months

2 Classes Per Week X 2 Hours Each Day

Batch Timing

Week End Classes | Week Days Classes

Online Self Paced LIVE Training

Students Enrolled92%

Course Fee

INR 40,000 / - + 18% GST

Installment Division:

1st - INR 10,000/-

2nd - INR 10,000/-

3rd - INR 20,000/-

Course Duration:

9 Months

2 Classes Per Week X 2 Hours Each Day

Batch Timing

Week End Classes | Week Days Classes


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Diploma in Machine Learning

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Machine Learning Professional | Machine Learning Engineer | Data Scientist

Artificial Intelligence Developer


Machine learning training in India by ICSS - Get trained from Data Scientist

Machine learning training in India course by ICSS will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand, on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. Machine learning training in India and PG Diploma in Machine Learning and AI in India course can turn out to be more effective to help the machine develop its own algorithm. The Machine Learning market is expected to reach USD $8.81 Billion by 2022, So it’s a great opportunity to grow your career in this field.

Machine learning training in India course is easily approachable by keen students those who have craving for Machine Learning and Artificial Intelligence. This course will equip you with the basic machine learning and AI tools for mining and analyzing datasets, and extracting insights for decision making. You will learn from our data scientist how to identify correlations and patterns in datasets, build predictive models using machine learning and AI software. You will emerge with the hands-on skills you need to apply AI in your enterprise, and enhance your career in the field.

What You Will Learn from ICSS:

Explore all modern branches of Machine Learning & AI

Understand how to use ML & AI to address urgent business and social issues

Develop innovative industrial, commercial and government applications & Tools

Introduce AI to your workplace or further its development there

Diploma in Machine Learning and AI for Industry

and workshops conducted by ICSS Educational Division

Diploma in Machine Learning and AI for Industry will expand your knowledge of neural networks-based Artificial Intelligence & Machine Learning in this course. ICSS had been fortunate enough to have been associated with some of the renounced educational institutions like IIT Kharagpur, NIT Durgapur, Jadavpur University, Lovely Professional University and JIS collage to name a few. Our campus representatives are highly active and conduct interactive sessions. Considering the major approaches in Diploma in Machine Learning and AI for Industry, the country is developing rapidly. Our training module is delicately balanced between practical lab based training and theoretical content. Learn the newest AI & Machine Learning applications and opportunities from top industry experts in the course of PG Diploma in Machine Learning and AI in India. Increasingly challenging exercises will help you to build AI tool that can learn to classify images, perform rudimentary language translation etc.

Learn how to apply technology to real world data science problems and gain an in depth understanding of emerging technologies, statistical analysis and computational techniques from Diploma in Machine Learning and AI for Industry course. This course brings out the growth in the business by molding key strategies, and decision-making capabilities through technology that have the capability of thinking like a human being.

By studying this degree you will:


  • Have the option to study one of the specialist pathways in Artificial Intelligence or Financial Technology
  • Address skills required by data scientists to drive improvements in organisational performance
  • Have the opportunity to create your own data analysis projects

Do you want to be a Campus Representative?

If you want to be a CR there is a long list of benefits that you will be entitled to.

Campus Representative of ICSS

Diploma in machine learning online

Why Choose online course from ICSS?

Diploma in machine learning online course by ICSS is a unique program fostering innovation with outstanding expertise of Machine Learning in the next generation leaders. Get online live classes from anywhere & get all the class records after completion of the each class. It is a one to one interaction. Learn PG Diploma in Machine Learning and AI in India course from India's leading Data Science experts and industry leaders. As the growing adoption of the technology across businesses and the need for trained professionals to do the jobs increase, AI and ML have become a sound path to a comprehensive career. Machine Learning have already impacted the quality of lives for humans in very positive manners. Get Hands on experience in in-demand technologies of AI ML. This course is designed for working professionals. Get guidance on your learning journey, and access dedicated career support.

AI-assisted applications are being equally used by both modern and traditional industries. Currently, there are several AI based job opportunities and the demand is expected to rise in the future. If you are interested in a career in this field, the first step is to join Diploma in machine learning online course. There is an increasing need for intelligent and accurate decision making across industries. This has led to an exponential growth in the adoption of AI and ML technologies. Our Diploma in machine learning online course stresses experiential learning to help you learn the tools and techniques in artificial intelligence and machine learning. We are offering language & Python programming along with machine in this course so that you will be learning artificial intelligence and machine learning easily. Anyone taking up ML & AI Courses from ICSS gets a diverse choice of courses that come in varying levels of specialization.

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