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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Course Overview

The “Artificial Intelligence + Machine Learning” certification is a 6 months online course provided by the E&ICT Academy, IIT Kanpur. This course is made for professionals who want to understand and learn what exactly is Artificial Intelligence, and its concept of Specialization like Machine Learning.

The “Artificial Intelligence + Machine Learning” syllabus teaches students the basic programming concepts that include Machine Learning, and by using the programming language Python. The E & ICT Academy Specialization is nothing but a series of courses that will help candidates master a skill. This will allow candidates to make their own applications that will be used for data analysis, visualization, processing, and retrieval, and become an expert in various programming languages, and tools.

Once the course is complete, and candidates are able to showcase their “Artificial Intelligence + Machine Learning” training as a certificate to their professional network then it will attract prospective employers. This, in turn, will provide better job opportunities to the candidates. And, make them ready to take on this competitive world.

The Highlights

6 Months of Online Course
5 Courses Taught
5 Certificates of Completion
Hands-on-Projects
Certificate initiation by E&ICT Academy, IIT Kanpur

Programme Offerings

Online Course
Certificate by E&ICT Academy
IIT Kanpur
Programming Tools
programming languages
Hands-on-Project

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Kanpur

The candidates have to pay Rs. 30,000 and along with it 18% extra GST charges for the “Artificial Intelligence + Machine Learning” Certification fee. Sometimes coupon codes are also available for discounts.

Artificial Intelligence + Machine Learning Fee Structure

Description	Amount in INR
Artificial Intelligence + Machine Learning	Rs. 30,000 + 18 % GST Extra

Eligibility Criteria

Certification Qualifying Details

Candidates should have completed learning all the course materials, done the hands-on-project, then he or she will be entitled to receive a certificate of completion.

What you will learn

Machine learningData science knowledgeKnowledge of Artificial IntelligenceKnowledge of deep learning

After the completion of the “Artificial Intelligence + Machine Learning” course candidates will be able to do the following:

Learn, and start the application of Programming Languages, Tools like Excel, Pandas, Python, MySql etc.
The candidates can explore the cases and applications of Artificial intelligence.
Students also will be able to properly demonstrate AIML.
Candidates will also explore skills like Python Supervised Learning, Fundamentals Exploratory Data Analysis, Inferential Statistics, Logistic Regression models, and more,

Who it is for

Anyone who wants to develop a career in Artificial Intelligence + Machine Learning can learn this course. Also, people who want to up their skills in Statistics can study this course.

Admission Details

Candidates can follow the steps below for admission details to the “Artificial Intelligence + Machine Learning” course:

Step 1: Visit the course's official website: https://ict.iitk.ac.in/ai-ml-specialization/.

Step 2: Next Click on ‘Enrol, And Pay Now’ in the center of the page.

Step 3: Add the course to the cart.

Step 4: Checkout, and pay the total fees.

Step 5: Candidates can now start studying.

Application Details

Filling in the application form is not necessary for the “Artificial Intelligence + Machine Learning” certification. The candidates can add the course to the cart, and proceed to checkout to get enrolled in the course.

The Syllabus

Introduction
The Programming Cycle for Python
Interacting with Python Programs
Python programs
Elements of Python
Type Conversion
Expressions
Assignment Statement
Arithmetic Operators
Operator Precedence.
Boolean Expression
Conditionals
Conditionals (Continued)
Expression Evaluation
Float Representation
Loops
Loops (Continued)
For Loop
Nested Loops
Break and Continue
Function
Parts of A Function
Execution of A Function
Keyword and Default Arguments
Scope Rules
Strings
Indexing and Slicing of Strings
More Slicing
Tuples
Unpacking Sequences
Lists
Mutable Sequences
List Comprehension
Sets
Dictionaries
Higher Order Functions
Sieve of Eratosthenes
File I/O
Exceptions
Assertions
Modules
Importing Modules
Abstract Data Types
Classes
Special Methods
Class Example
Inheritance
Inheritance and OOP
Iterators
Recursion
Simple Search
Estimating Search Time
Search in Python Programming.
Estimating Binary Search Time
Recursive Fibonacci
Tower Of Hanoi
Sorting
Selection Sort
Merge List
Merge Sort
Higher Order Sort

Introduction to SQLite database - Day's (1,2)

Overview
Create Database
Create Table
Drop-Table
Insert query
Select query
Delete and Update query
WHERE AND & OR Clause

Regular Expression - Day's (3)

RegEx Module
Match object
RegEx Functions:search, findall,sub
Match at the beginning or end
Metacharacters
Special Sequences

GUI Programming with Tkinter

Introduction
Saying Hello with Labels
Buttons
Message Widget
Entry Widget
Dialogs
Radiobutton and Checkboxes
Creating Menus
Events and Binds

Django: A web framework

Introduction
What is Django..?
Installing Django
The MVC framework
Why use Django..?
Creating a Django Project
Starting a project with Django
Creating an application
Configuring the application
Hello World with Django
Creating our first URL
Creating our first view
Testing our application
Working with Templates
Template Basic
Injecting the data from view to the template
Built-in Tags & filters
Integrating variables in templates
Extending the templates
Using static files in templates
Models
Define models
Creating a simple model
Save a model into a Database
Inserting & Editing Data
Getting a Model data with Queryset
Deleting Objects
Implementing Foreign key
Extending a model
Django Admin Interface
Enabling admin interface
Creating an admin user
Django Forms
Forms in Django
Searching Query
GET & POST methods
Form fields in Django
Building a form in Django
Model-based form
Custom validation
The Authentication Module
How to use the authentication module
Adding a user
Login and logout
Built-in Decorators
Restricting access to unknown users

Project

Calculator with Tkinter
A Web App using Django

Calculating mean and median values
Measuring maximums, minimums, and other data characteristics
Analyzing data using variance and standard deviation
Introducing the central limit theorem
Analyzing a population using data samples
Identifying and minimizing sources of error
Grouping data using histograms
Identifying relationships using XY scatter charts
Visualizing data using logarithmic scales
Adding trendlines to charts
Forecasting future results
Calculating running averages
Formulating a hypothesis
Interpreting the results of your analysis
Considering the limits of hypothesis testing
Using the normal distribution
Using the exponential distribution
Using a uniform distribution
Using the binomial distribution
Using the Poisson distribution
Visualizing what covariance means
Calculating covariance between two columns of data
Calculating covariance among multiple pairs of columns
Visualizing what correlation means
Calculating the correlation between two columns of data
Calculating correlation among multiple pairs of columns
Introducing Bayesian analysis
Analyzing a sample problem - Kahneman's Cabs
Creating a classification matrix
Calculating Bayesian probabilities in Excel
Updating your Bayesian analysis

Numpy

Overview
Creating ndims arrays
Why do we need arrays ?
Numeric operations using numpy
Indexing and slicing
Some Mathematical functions

Pandas

Pandas Overview
Data Structures
Series
DataFrame
Series and DataFrame operations
Missing Data
Categorical Data
Time Series data
Read data from the different file format
Merging and Grouping Data
Many other data operations using Pandas

Matplotlib and Seaborn

Overview
Scatter plot, line plot, bar plot
Histogram
Xlabel, Ylabel, Xticks, Yticks, title
Marker style,type, size
Figure and Subplot
Saving a Figure
HeatMap,BoxPlot

Welcome to Machine Learning

Introduction To Machine Learning
History and Evolution
Artificial Intelligence Evolution
Find out where Machine Learning is applied in Technology and Science

Machine Learning Categories

Supervised Learning
Unsupervised Learning

Machine Learning Python Packages

Data Analysis Packages
NumPy
SciPy
Matplotlib
Pandas
Slkearn

Supervised Learning

Regression
Classification
Generalization, Overfitting, and Underfitting

Classification

Classification

Regression

Understand how continuous supervised learning is different from discrete learning
Code a Linear Regression in Python with scikit-learn
Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions

Supervised Machine Learning Algorithms

k-Nearest Neighbor
Linear models
Naive Bayes Classifiers
Decision trees
Support Vector Machines

Unsupervised Learning and Preprocessing

Challenges in unsupervised learning
Preprocessing and Scaling
Applying data transformations
Scaling training and test data the same way

Dimensionality Reduction and Feature Extraction

Principal Component Analysis (PCA)

Introduction to Deep Learning

A revolution in Artificial Intelligence
Limitations of Machine Learning
What is Deep Learning?
Advantage of Deep Learning over Machine learning

Introduction To Neural Networks with TensorFlow

How Deep Learning Works?
Activation Functions
Training a Perceptron
TensorFlow code-basics
Tensorflow data types
Tensorflow methods
Introduction to Neural Networks
Neural Network Architecture
Linear Regression example revisited
The Neuron
Neural Network Layers
The MNIST Dataset
Coding MNIST NN

Introduction to Convolutional Neural Networks (CNN) with TensorFlow

Understand the limitations of a Single Perceptron
Deepening the network
Convolutional Neural Networks
ConvNet Architecture
Overfitting and Regularization
Max Pooling and ReLU activations
Dropout
Strides and Zero Padding
Coding Deep ConvNets demo
Visualizing NN using Tensorfl

Keras API

How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers

Instructors

IIT Kanpur Frequently Asked Questions (FAQ's)

1: What kind of learning can a candidate expect?

The content is made up of asynchronous lectures from leaders in the industry, and live hangout sessions which is mainly for problem-solving and academic queries.

2: What can candidates expect out of this programme?

This programme is prepared for anyone looking to learn skills in advanced concepts along with a proper foundation of Statistics.

3: What should candidates not expect from this programme?

This programme will not lead to advanced roles but in turn make a transition to the AI and Machine Learning field of the professionals/students.

4: Who is the programme made by?

The program has been designed by industry leaders, and teachers of IIT, Kanpur who are experienced, and the best when it comes to this programme.