Overview
Crafted a Dynamic Program Just for You!
Missed a class?
Watch the recording later with teaching assistants available to
solve your doubts
Feeling left behind?
We have created a collaborative learning environment with peer
learning and group projects
Job & class timings clash?
Flexible class timings to choose from based on your availability
Want to practice?
Study materials, quizzes, projects and mock interviews are
provided to ease the process
Have doubts?
Reach out anytime - ours Mentors and teaching assistance will
always be ready to help
Who is this course for?
Applicable to anyone who is interested to learn ML and AI, even to
people with minimal or no coding background.
UG/PG students trying to land a job.
Professionals trying to switch career.
Professors trying to skill up to teach and learn for their
research purposes.
People on a career break, to get acquainted with the latest
technology.
Why yet another AI course?
All the resources required to become a Data scientist are
available online for free. But when students come across a complex
Math Equation or Algorithm, they tend to lose confidence and drop
out.
On the other hand there are very expensive courses (2L to 3L), which are way out of reach to the common people. Those programmes are also based on recorded videos with once a week live Mentoring session.
AI FAB 50 offers you live classroom-like training with 1 to 1 mentorship to follow up daily, at a very nominal cost.
On the other hand there are very expensive courses (2L to 3L), which are way out of reach to the common people. Those programmes are also based on recorded videos with once a week live Mentoring session.
AI FAB 50 offers you live classroom-like training with 1 to 1 mentorship to follow up daily, at a very nominal cost.
Course Duration?
It has been planned in such a way that you get time to learn and
solidify your learning through implementation.
4 months
(16 weeks live classroom training)
+2 months
(Project Mentorship/Internship)
We don't leave you
(Career Guidlance)
We will create a scenario
(Job Assistance)
Who Created this course?
Navaneeth Malingan and Team
Navaneeth is the Founder & Director of Nunnari labs. He leads the
AI Innovation at Nunnari Labs and empowers people through
education via Nunnari Academy. He has impacted over 10,000+
students through different channels and communities. He has
extensive experience in Software Engineering, Data Analysis,
Machine Learning, Deep Learning, Computer Vision, NLP and MLOps.
He is involved in building real world AI Solutions and also does
Corporate Training, Workshops and Faculty Development Programs.
Navaneeth is a Tech Evangelist and Thought Leader, putting to action his plans and vision through Nunnari labs, an emerging startup with aspiring goals to provide Industry 4.0 solutions.
Navaneeth is a Tech Evangelist and Thought Leader, putting to action his plans and vision through Nunnari labs, an emerging startup with aspiring goals to provide Industry 4.0 solutions.
Program Instructors
Key Features
Why AI FAB 50 is your next career move?
Online
100% Live Classroom session
1 to 1
Expert Mentoring in English and தமிழ்
Help
Our expert team will always be available to help you
Hiring
Top performers will be hired under the R&D sector
Tailor Made
For different groups like Students, Academicians and Software
Engineers
Community learning
You will be a part of the learning community, even after
course completion
What will you be Learning?
AI FAB 50 covers
Artificial Intelligence Technologies
Programming with Python
Mathematics for Machine Learning
Machine Learning
Deep Learning
Computer Vision
Natural Language Processing
Large Language Models
AI Agents
Tools Covered
Industry relevent Tools and Frameworks
Python Libraries
Visualisation Libraries
Deep Learning
Natural Language Processing
Demo Videos
Checkout our previous classes
Training Curriculum
Checkout the full syllabus here
Module 01 : Introduction to AI, ML & DS
- Introduction to AI
- History, Present, Future Trends
- Types of AI
- Applications, Ethics & Challenges
- Overview of Machine Learning and Deep Learning
- Data Science Lifecycle
Module 02 : Python Programming
- Introduction
- Python Data Types
- Flow Control
- Functions
- OOPS Concept
- File Handling
- Installation & Environment Setup
Module 03 : Python for Data Science
- NumPy Arrays
- Matrix Operation using NumPy
- Pandas Data Structure
- Data Manipulation using Pandas
Module 04 : EDA & Feature Engineering
- Data Preparation/Cleaning
- Data Exploration
- Data Visualization
- Feature Extraction
- Feature Selection
Module 05 : Maths for ML
- Probability & Distributions
- Statistics
- Linear Algebra
- Multivariate Calculus
Module 06 : Supervised Learning
- Linear Regression
- Logistic Regression
- Polynomial Regression
- k-NN Classifiers
- Naive Bayes Classifiers
- Support Vector Machine
- Decision Trees
- Random Forest
Module 07 : Ensemble Techniques
- Bagging, Boosting
- AdaBoost
- Gradient Boost
- XG Boost
- LightGBM, CatBoost
Module 08 : Unsupervised Learning
- K-Means Clustering
- DBSCAN
- Hierarchical Clustering
- Dimensionality Reduction Techniques - PCA, LDA
Module 09 : Deep Learning
- Introduction to Neural Networks
- The Perceptron, Multilayer Perceptron
- Neural Network Equation
- Activation Function
- Forward and Back Propagation
- Hyperparameter Tuning
- Loss Functions
- Optimization
- Gradient Descent
- Regularisation
- Types of Neural Networks
Module 10 : Introduction to CNN
- Introduction to Convolutional Neural Networks
- Convolution, Pooling, Padding & its mechanisms
- CNN Architectures - LeNet, AlexNet, VGGNet, ResNet
- Image Processing and Data Augmentation
- Transfer Learning
- Deploying Image Classification Model - Server & Lite
Module 11 : Computer Vision
- Introduction to OpenCV
- Object Detection - YOLO, SSD, Faster R-CNN
- Image Segmentation - UNet
Module 12 : Advanced Deep Learning Techniques
- Autoencoders
- Variational Autoencoders
- Introduction to GANs
- Types of GANs - DCGAN, Pix2Pix, CycleGAN
Module 13 : Natural Language Processing
- Fundamentals of NLP
- Text Processing
- Tokenization
- Introduction to N-Grams
- Word Embeddings
- Named Entity Recognition
- Regular Expressions
- Sequence Models - RNN, LSTM, GRU
- Attention Mechanism
Module 14 : Transformers
- Introduction to Transformers
- Text Generation Models
- BERT
- Text-To-Text Transfer Transformer T5
- Pre-trained vs Fine-tuned Models
- Using Hugging Face Models
Module 15 : LLMs & Vector Databases
- Introduction to Large Language Models
- LLM Architecture & Applications
- Open Source LLMs
- Fine-tuning LLMs
- Introduction to Vector Databases
- Pinecone, Weviate
Module 16 : Prompt Engineering
- Introduction to Prompt Engineering
- Crafting Effective Prompts
- Advanced Prompting Techniques
Module 17 : RAGs and AI Agents
- Introduction to RAG
- Components of RAG
- Similarity Search, Faiss
- Introduction to AI Agents
- Applications and Implementation
Module 18 : ML and LLM Ops
- Data Version Control
- Git and GitHub Foundation
- Docker Foundation
- ML Models in Production
- ML Pipelines
- Model Deployment
- Deployment and Serving of LLMs
- Monitoring and Performance Tuning
Earn a verified
Certificate of Accomplishment
Certificate of Accomplishment
On course completion you will be issued with a certificate that
you can showcase on your Résumé and LinkedIn profile.
Our Alumni
Our students have excelled and are now employed in top MNCs.
Testimonials from our Learners
Hear from our previous batch students
Swathi Durai
Student
Coimbatore Institute of Technology
Rajasimman
Professional | Android Developer
TVS
N.Nithya
Associate Professor
Sona School of Management
Deepika Gurung
Student
Sona college of technology
Cathrine Jeeva
Sri Krishna College of Arts and Science
TCS
Harini Sivakumar
Student
SNS college of technology
Prabhhav Sharma
Fresher
Karunya University
Apply Now!
Unlock your potential with AI FAB 50.
Join us and become part of the next generation of AI innovators!
Join us and become part of the next generation of AI innovators!
