Artificial Intelligence: Take your career to the next level with our advanced training program

1st January, 2024

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Empower Your Career with Our Complete, Industry-Focused Program

Designed for college students, Professionals, and Innovators Across All Fields.

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Create a standout job-ready profile with an impressive project portfolio.

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100K +
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$1.8 trillion
Global Market Size
₹ 10 lakh
Average Annual Salary

  • Overview of AI
    • The simulation of human intelligence processes by machines, especially computer systems
  • Applications of AI
    • Healthcare
    • Finance
    • Autonomous Vehicles
    • Robotics
    • Education
    • Gaming
  • AI Foundation and History
    • Mathematics
    • Linguistics
    • Control Theory & Cybernetics
    • Computer Engineering
  • Types of AI
    • Capabilities - Weak AI, General AI, Super AI
    • Functionalities - Relative Machines, Limited Memory, Theory of Mind
  • Introduction to Python
  • Python Functions, Packages, and Routines.
    • Functions: Fuctions are blocks of reusable code that perform a specific task.They are defined using the def keyword, allow parameters, and can return results, making code more modular and organised.
    • Python Packages: Packages are collections of modules that group related functions, classes, and routines together.
    • Routines: Refers to a series of programmed instructions or functions that can be reused to perform common tasks. They help automate processes, improve efficiency, and minimise code duplication.
  • Working with Data structure, Arrays, Vectors & Data Frames.
    • Data structures in Python (e.g., lists, tuples, dictionaries, and sets) are ways to store and organise data efficiently. They allow for easy access, modification, and management of data depending on the structure's properties.
    • Arrays (using libraries like numpy) and vectors are ordered collections of elements, typically of the same data type. Arrays support fast mathematical operations, while vectors are 1D arrays often used in linear algebra and machine learning.
    • It is a two-dimensional, table-like data structure (from libraries like pandas) where data is stored in rows and columns. It’s ideal for handling and manipulating structured data, similar to spreadsheets or SQL tables.
  • Pandas, NumPy, Matplotib packages.
    • Powerful library for data manipulation and analysis, Pandas provides data structures like DataFrames, allowing for easy handling, cleaning, and transformation of structured data.
    • A fundamental package for numerical computations, NumPy offers support for multi-dimensional arrays and a wide range of mathematical functions for operations on arrays and matrices.
    • A popular plotting library used for creating static, interactive, and animated visualisations in Python, Matplotlib allows users to generate a wide variety of charts, including line plots, histograms, and scatter plots.
  • Intelligent Agents
    • Autonomous systems that perceive their environment, make decisions, and take actions to achieve specific goals, often improving performance through learning and adaptation.
  • Rational Agents
    • These agents act to achieve the best possible outcome based on the information they have, making decisions using logic, knowledge, reasoning and aiming to act optimally in any given situation that maximises expected utility and aligns with their goals.
  • PEAS Representation
    • Performance
    • Environment
    • Actuators
    • Sensors
  • Types of AI Agents
    • Simple reflex agents
    • Model-based agent
    • Goal-based agents
    • Utility-based agents
    • Learning agents
  • Uninformed Search Examples
    • ​​Explore a problem space without using additional information beyond the structure of the problem.
  • Search Algorithms
    • Terminologies
    • Transition Model
    • Optimal Solution
  • Uninformed Search Algorithm
    • Breadth First Search
    • Depth First Search
    • Depth Limited Search
    • Uniform Cost Search
    • Iterative Deepening Depth First Search
    • Bidirectional Search
  • Informed (Heuristic) Search Algorithm
    • Best First Search
    • A* Search
  • Hill Climbing Algorithm
    • No Backtracking
    • State Space Diagram
    • Simple Hill Climb
    • Steepest Ascent Hill Climb
    • Stochastic Hill Climb
  • Adversarial Search and Games
    • Purpose: Game-playing
    • Components: Players, States
    • Deterministic Games
    • Non Deterministic Games
    • Zero Sum Game
    • Tic Tac Toe Game
  • Minimax Algorithm
    • Goal: Optimal decision-making
    • Type: Adversarial search
    • Process: Evaluate moves, Minimise loss
    • Components: Max player, Min player
    • Use: Two-player games
  • Alpha-Beta Pruning
    • Purpose: Optimise Minimax
    • Function: Reduce search space
    • Technique: Prune branches
    • Efficiency: Faster evaluation
    • Use: Game AI strategies
  • Introduction to ML
  • Types of ML
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • Life Cycle of ML
    • Gathering Data
    • Data Preparation
    • Data Wrangling
    • Data Analysis
    • Train Model
    • Test Model
    • Deployment
  • Supervised Learning
    • Classification - Logistic Regression, Decision Trees, SVM, KNN, Naive Bayes,
    • Regression - Linear Regression, Polynomial Regression,Ridge Regression,SVR
  • Unsupervised Learning
    • Types: Clustering, Dimensionality Reduction, Association
    • Techniques: K-Means, Hierarchical Clustering, PCA
  • Clustering Methods
    • Partitioning Clustering
    • Density Based Clustering
    • Distribution Model Based Clustering
    • Hierarchical Clustering
  • Association Rules
    • Metrics of Association Rule Learning - Support, Lift, Confidence
    • Types - Apriori Algorithm, Eclat Algorithm, F-P Growth Algorithm
  • Introduction to Deep Learning
  • Architecture and Application
    • Architecture - Deep Learning Network, Deep Belief Network
    • Types - FFNN,CNN,Restricted Boltzmann Machine, Autoencoders
  • Deep Learning Algorithms
    • Convolutional Neural Networks
    • Long Short Term Memory Networks
    • Recurrent Neural Networks
    • Generative Adversarial Networks
    • Radial Basis Function Networks

Text Classification With Tensorflow

Mastery of TensorFlow Ecosystem: Gained practical experience in working with TensorFlow, TensorFlow Hub, and TensorFlow Datasets for loading, splitting, and processing data, especially for tasks like sentiment analysis.

Transfer Learning for NLP: Learned to leverage pretrained models from TensorFlow Hub to efficiently implement transfer learning in natural language processing tasks, enhancing model performance with minimal effort.

Model Building and Evaluation: Developed skills in building, training, and evaluating neural networks, focusing on performance metrics like accuracy and loss to fine-tune the model for better predictions.

Classification of Pet's Faces

Image Data Handling and Label Encoding: The project effectively extracts pet breed names from image filenames, encodes them into numerical labels, and resizes images to a standard size (224x224), ensuring consistency for model training.

Dataset Exploration and Visualization: The project uses Matplotlib to visualise the pet images and their distribution, helping to identify class imbalances and ensuring that the dataset is suitable for building a robust classifier.

TensorFlow Integration for Feature Extraction: It leverages TensorFlow’s image processing capabilities to load, resize, and convert images into arrays, preparing the dataset for deep learning model development focused on pet breed classification.

Object Detection using Tensorflow

Deep Learning Fundamentals: Gain a solid understanding of object detection algorithms, enhancing knowledge of convolutional neural networks (CNNs).

TensorFlow Proficiency: Improve TensorFlow skills, learning to train and fine- tune models for accurate detection of multiple object classes.

Data Annotation and Preprocessing: Learn the importance of data annotation and preprocessing to ensure reliable model training and enhance detection accuracy.

Landmark Detection

Image Preprocessing and Data Management: Gained experience in loading, managing, and preprocessing large-scale image datasets using libraries like OpenCV, PIL, and pandas, essential for preparing data for deep learning models.

Using VGG-19 for Transfer Learning: Applied the VGG-19 model, a powerful pre trained convolutional neural network, for transfer learning. This involved adapting the model to classify landmark images, showcasing the efficiency of using pre-trained architectures for specialised tasks with limited labelled data.

End-to-End Workflow Integration: Developed skills in building an end-to- end workflow, integrating TensorFlow for model training, OpenCV for image handling, and Matplotlib for visualisation. This included fine-tuning the VGG- 19 model for improved performance on the landmark classification task.

Get Certified, Get Ahead! Your success starts here!

Meet your mentors!

Our expert instructors guide you through the fundamentals in an engaging and supportive learning environment.
4+ years work experience
4+ years mentoring experience
3k+ students mentored
Sankal Tomar

Sr. Data and Applied Scientist

Senior Data Scientist | Senior System Engineer at Infosys
12+ years work experience
3+ years mentoring experience
5k+ students mentored
Gaurav Kumar Verma

AI Consultant @Google

Software Architect | Data Scientist | Associate Engineer
4+ years work experience
5+ years mentoring experience
3k+ students mentored
Rajendra Mehta

Head of Analytics

Head of Analytics | Chief Data Scientist at LTIMindtree
4+ years work experience
2+ years mentoring experience
5k+ students mentored
Sathya Prakash Soma

Senior Technical Trainer

Senior Technical Trainer | Senior Java Consultant
5+ years work experience
4+ years mentoring experience
6k+ students mentored
Sahil Handoo

AI Engineer @Google

AI Engineer | Data Engineer at Yara International
10+ years work experience
6+ years mentoring experience
4k+ students mentored
Suddhasatwa Bhaumik

Strategic Cloud Engineer

Google Cloud Consulting | Senior Manager Data Products at VOIS
6+ years work experience
4+ years mentoring experience
4k+ students mentored
Swati Thakur

Cloud Consultant @Google

Cloud Consultant|Senior Data Analyst
4+ years work experience
3+ years mentoring experience
5k+ students mentored
Aishwarya K

Cyber Security:Manager Program Delivery

Cyber Security Mentor | Analyst | Cyber Security Trainer
5+ years work experience
3+ years mentoring experience
8k+ students mentored
Akash Nagineni

Software Developer

Frontend Trainer|Building Dynamic Web App
5+ years work experience
4+ years mentoring experience
9k+ students mentored
Megha Singh

Senior Web Developer

Head of Technical | 10+ Highly Scaled Applications Developed
13+ years work experience
6+ years mentoring experience
5k+ students mentored
Sudhakar G

Principal Architect

Technical Lead | Sr. Delivery Manager
11+ years work experience
10+ years mentoring experience
15k+ students mentored
Raj Ranjan

Sr. Software Developer @Amazon

Software Dev Engineer|Back-end Developer
7+ years work experience
4+ years mentoring experience
10k+ students mentored
Jacob Dennis P

Senior Software Engineer

Web Technologies | Database Technologies | Frameworks | Architecture and Server Management, AWS, Google.

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₹ 4500

+18% GST

Buy Now
  • Program Duration : 4 months
  • 35 Hours of Content
  • 5 Projects
  • Live Sessions During Project Execution & Training
  • Life time access for content and Customised Dashboard
  • Project Completion Certificate from Partnered Companies
  • Internship Offer Letter
  • Internship Completion Certificate
  • Customised Resume Builder

₹ 2999

+18% GST

Buy Now
  • Program Duration : 2 months
  • 30 Hours of Content
  • 3 Major Projects
  • 1 year Access for Dashboard and Content
  • Project Completion Certificate
  • Internship Offer Letter
  • Internship Completion Certificate

₹ 4500

+18% GST

Buy Now
  • Program Duration : 4 months
  • 40+ Hours of Content
  • 5 Projects
  • Live Sessions During Project Execution & Training
  • Life time access for content and Customised Dashboard
  • Project Completion Certificate from Partnered Companies
  • Internship Offer Letter
  • Internship Completion Certificate
  • Customised Resume Builder

₹ 2999

+18% GST

Buy Now
  • Program Duration : 2 months
  • 30+ Hours of Content
  • 3 Major Projects
  • 1 year Access for Dashboard and Content
  • Project Completion Certificate
  • Internship Offer Letter
  • Internship Completion Certificate

FAQs

General
Mentors are Industry Experts as well as from the Company whose project you are selected for.
The students will need to dedicate 6 hours in a week towards the project. Consistency is the key, hence we recommend investing 30 minutes per day towards the project.
The last date for registration depends on the number of seats available for the project you are opting for. Since there are students applying for projects from all over hence we will recommend blocking your seat for the project.
The prereq will vary from project to project. You shall be provided all the details as shared by the respective company on your Dashboard.
Since all sessions are recorded and uploaded on the dashboard, you can access them anytime.
The Capstone and Live Project shall be explained by the Mentor in detail via Live classes During this session you can also clear all your doubts. In addition, if required,you will be given a15 days extension to complete the project and submit it. Once the Project is reviewed and approved by the respective mentor and company, certificate shall be issued
Yes, you may change your domain within 24 hours of your registration.
This is a hybrid program. You will need to complete the prerequisites before starting the live class for the project.
Yes, post projects get reviewed by a company, you will get the Guaranteed internship in the form of Live Project

Internship
The Duration of the internship is 2 months and you will be working on a Real Life Capstone Project.
No, there is no exam before the internship. Instead you need to learn all the prerequisites and submit the live project to get the internship.
The Live sessions are typically hosted in the evenings to accommodate the student’s availability. We shall be notifying you in advance via mail and messages through Telegram. In case if you miss any live classes, the recording of the live session will be uploaded in your dashboard which you can access anytime
As some companies still follow a work-from-home model for their employees, and we have also requested our partner companies to schedule online internships. This approach helps avoid challenges like traveling and finding accommodation, making the process more convenient for students.
Companies offer you a stipend ranging from 5000/- to 15,000/-.The stipend is directly proportional to your performance and completion of project.
The Respective Company SPOC along with the Mentor shall be reviewing your performance throughout the project duration. Based on your performance, if the companies find your performance up to the mark, they may offer you a PPO post interview. However all the interview and hiring rounds will need to be cleared by the student to be considered for the opportunity.
Yes, you may modify and submit your project as your Minor/Major project.
Post successful completion of your internship, you shall be getting an Internship Completion Certificate from the respective company.
You will be getting access to your student dashboard through which you can rasie any doubt with the respective company/ mentor. In addition all your learning modules Projects, Certificates, will be uploaded there. You shall be having life time access to it.
After completing the registration, our partner companies do not charge any additional fees.

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