Get updates on Eligibility, Admission, Placements Fees Structure
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
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)
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.
This programme is prepared for anyone looking to learn skills in advanced concepts along with a proper foundation of Statistics.
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.
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.