Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom, Campus Based/Physical Classroom | Video and Text Based | Weekends |
Course Overview
TalentSprint provides a high-end and comprehensive education to aspiring professionals. The Advanced Certification Program in AI/ML by TalentSprint has been designed to give the learners an in-depth knowledge of Artificial Intelligence and Machine learning.
The course will be collaboratively led by faculty from industry, academia, and global blue-chip institutions.
Participants will learn about the various concepts of Artificial Intelligence and Machine Learning.
They will be trained to build and deploy Artificial Intelligence and Machine Learning and Deep Learning Applications. They will be equipped to apply standard Artificial Intelligence and Machine learning algorithms to create Artificial Intelligence and Machine learning applications, build a phone unlocking app, domain agnostic chatbot, voice-based application for ordering food, and Implement practical solutions using Deep Learning Techniques and Toolchains. Their unique 5-step learning process will ensure fast-track learning.
The 6-months course includes online learning sessions and candidates get Advanced Certification from IIT Hyderabad ML Lab after successful completion of the course.
The Highlights
- Online learning
- 6 months course
- Interactive Live Online Program
- Advanced Certification from IIIT Hyderabad ML Lab
- Alumni Status of IIIT Hyderabad Executive Education
Programme Offerings
- masterclass lecture
- guided mentorship
- hands on lab
- Industry Workshops.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIIT Hyderabad |
Fees payable for the Advanced Certification Program in AI/ML by TalentSprint are:
The application fees is Rs. 2,000/-
Advanced Certificate Program | ₹2,00,000 |
PG Certificate Program | ₹3,00,000 |
Eligibility Criteria
Work experience
Candidates who wish to pursue the Advanced Certification Program in AI/ML by TalentSprint should be Tech Professionals with at least 1 year of work experience and a background in coding.
Certification Qualifying Details
The completion certificate will be given to candidates only after they successfully complete it.
What you will learn
While pursuing the Advanced Certification Program in AI/ML by TalentSprint participants will learn varied things that are mentioned below:
- The fundamental concepts of Artificial Intelligence
- Understanding the concepts of Machine Learning
- Learning about Neural Networks
- Understanding Python programming
- Strategizing the Representation of real-world data
Application Details
Participants will follow the below-given steps to take admission in the Advanced Certification Program in AI/ML by TalentSprint.
Step1: Click on the official link of the course.
Step2: To enrol for the course, candidates have to fill in their education and professional details in the box given on the right side of the website.
Step3: Selection of the candidates shall be done by the programme selection committee.
Step4: Selected candidates shall be informed by the committee. Candidates have to pay the registration fee in order to reserve their seats.
Step5: Later, they can enrol in the programme by paying the programme fees.
The Syllabus
Proficiency in Python
- Basics of Python
- Data Types and Data Structures
- Defining and Calling Functions
- Python Library Packages
Calculus and Linear Algebra
- Vectors
- Norms
- Matrices
- Matrix Operations
- Linear Dependence and Independence
- Linear Transformations
- Least Square Error
- Derivatives
- Chain Rule
- Integration
- Gradients
Probability and Statistics
- Continuous and Discrete Random Variables
- Mutually Exclusive/Non-exclusive Events
- Independent and Dependent Events
- Probability Distribution Functions
- Cumulative Distribution Functions
- Measures of Centre and Spread
- Normalization and Standardization
Basic CS Theory
- Basic understanding of Algorithm Design Paradigms, Python Classes, Objects, Methods
Key Python Libraries (NumPy, SciPy, Pandas, Matplotlib)
- Numpy
- Scipy
- Pandas
- Matplotlib
Overview of AI and ML concepts
- Demystifying ML
- Modern AI
Principles of ML and Deeper Look at ML
- Supervised vs Unsupervised Learning
- Classification vs Regression
- Training and Testing Methods
- ML Pipeline
Data Processing and Visualization
- Data Munging
- Outlier Detection
- Hands-on Data Visualization
Features Selection and Extraction
- Feature Selection Methods
- Feature Extraction Methods
Classification 1.0
- k-NN Classification
- Decision Tree
- Linear Classifier
Regression 1.0
- Linear Regression
- Polynomial Regression
- Logistic Regression
Dimensionality Reduction
- Basic Mathematics and Hands-on Analysis
Representing Text and Language
- Understanding standard NLP Libraries
- Exploring Different Representations Methods
Sampling Methods (Bootstrap and Bagging)
- Bootstrapping
- Bagging
Projects
- Literacy Rate Prediction
- Aptitude Questions Classification
Intro to Natural Language Processing
- NLP Problem Formulation and Fundamental Solutions
Representation of Real-world Data
- Exploring Word Embeddings
Performance Metrics
- Methods to Measure the Efficacy of the ML models
Gradient Descent and Back Propagation
- Single Layer Perceptron
- Basic Mathematics
- Practical Tricks and Tips
Non-Linear Solutions and Multi Layer Perceptron
- Activation Functions
- MLP Architecture
PCA with EigenFaces
- PCA Algorithm
- Underlying Mathematics
- Applications of PCA
Support Vector Machines and Kernels
- SVM Formulation
- Non-linear Mapping
- Popular Kernels
Manifold Learning and Unsupervised Learning
- Non-Linear Dimensionality Reduction for visualization
- Hierarchical Agglomerative Clustering
- K-means clustering
Features for Perception (Image and Speech)
- Classical and Modern Feature Representation for Visual Data
- Classical and Modern Feature Representation for Audio/Speech
Projects
- Author Identification
- Customer Segmentation
Introduction to Deep Learning
- Deep Learning Framework
- Neural Network Module
- Training a Neural Network
Pytorch
- Basics of Pytorch
- Pytorch Packages
- Computation on GPU
- Model Building
- Model Training and Evaluation
Convolutional Neural Networks
- Convolutional Layers and Backpropagation
- Building Blocks for Convolutional Networks
- Applications of CNN - Audio, Text and Image
Autoencoders
- Intro to Autoencoders
- Deep Autoencoder
- De-noising Autoencoder
Recurrent Neural Networks
- Recurrent Neural Network Architecture
- Machine Learning with Sequential Data
- Neural Network Language Models
Time Series
- Classical Models (AR, MA, ARMA & ARIMA)
- Neural Networks for Time Series Forecasting
Beyond AlexNet and Transfer Learning
- Adapting Pre-trained Models for Desired Applications
Fast API
- Machine Learning Deployment using
- Docker
- HTML
- Java Script
- Joblib and Pickle
Deployment and Practical Issues
- Technical and Non-technical Pratical Issues in ML
Model Compression
- Model Compression
- Pruning
- Quantization
- Student-teacher Network
Reinforcement Learning and Applications
- Reinforcement Learning Framework
- Single and Multi agent RL
- Q-Learning
- Deep RL
- RL Applications
Siamese Networks and GANs
- Siamese Architecture
- Generative Adversarial Networks
Computer Vision
- Introduction to Computer Vision
- Camera Model and Geometry
- Problems in Computer Vision
- Applications and Use Cases
ML in Speech and NLP
- ML in NLP
- Sequence Generation and Information
- Extraction
- Latest Text Embedding Models
- Neural Machine Translation
- Attention Mechanism for DL
- ML in Speech
- DNN based Speech Applications
- DNN with Attention
- Sequential Network
- Encoder Decoder Models for Speech
- Recognition
- Automatic Speech Recognition
Industry Lectures and talks
- Talks from different domain experienced people