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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:

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

Machine learningKnowledge of PythonKnowledge of Artificial Intelligence

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

Tensorflow and Keras

Intro to Keras and Keras Basics
Keras Models
- Sequential Model
- The Functional API
- Model Subclassingii
Sequential model Steps and it's Parameters and Adding Layers
Building a Network in Keras
Loading and Save the Models with Example and Measuring the Performance

Data Engineering

ETL
Dimensional Data Modelling
Big Data Query

Overfitting and Generalization

Memorization and Generalization
Occam’s Razor
Ways to Prevent/Reduce Overfitting

Time Series

Classical Models (AR, MA, ARMA & ARIMA)
Neural Networks for Time Series Forecasting

Ensemble methods and Random Forest

Random Forest
Boosting and Adaboost

Human in the Loop Systems

Different Roles for Human in the Loop
Users in the ML Systems
Relevance Feedback
Active Learning

Projects

Image Classification
Identification of plant disease from leaf images

Appreciating CNNs

Visualizing CNNs
Improving CNNs
Popular CNN Architectures

RNN, LSTM and GRU

Vanishing Gradients
Understanding Recurrent Architectures
RNN, LSTM, GRU

Recommendation Systems

Introduction to Recommendation Systems
Social Recommender Systems
Collaborative Filtering
Hybrid Recommendation Models

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

Projects

Retail sales forecasting
Image processing and Transfer Learning

Instructors

IIIT Hyderabad Frequently Asked Questions (FAQ's)

1: What kind of expertise will the participants receive?

This program helps the learners to reach higher levels of learning as per Bloom's Taxonomy. They will not only understand but also Apply, Assess, and Evaluate the concepts which are far higher levels of expertise.

2: What kind of practical experience will the participants get?

A participant will be given 40+ individual lab experiments and 8 mini-hackathons. They will also have to participate in 4 hackathons, including select kaggle experiments.

3: How is the programme format different from other courses?

This Program is delivered in an interactive digital format that retains effectiveness while maintaining safety. This format combines both in-class and online classroom benefits.

4: What are the features of the Digital learning platform?

Direct-to-device digital learning platform provides features such as interactive live class, multi-device access, group labs, hackathons, interactive forums, personalized learning plan, one to one mentor support, online practice labs, and many more.

5: How will this platform support lecture classes?

The digital platform will enable the participants to attend all classes online from any location on any device, Interact with the faculty, and through chat and audio, during the online classes.

6: How will this platform support lab sessions?

Participants will be able to interact with the mentor during the lab sessions, set up one-on-one mentoring sessions, and work with peers for group projects.

7: How will the doubts be cleared?

In case the participants have any queries, they can post their queries in the discussion forum, can schedule one on one session with mentors or clarify their doubts with the professors during the review session scheduled periodically.

8: What are the requirements for the digital learning platform?

Participants will need a Desktop/Tablet/Laptop/Smartphone with a camera and mic, regular broadband/wifi connection or a 4G mobile connection, and a computer with the necessary configuration for the online lab sessions.

9: Is placement assistance provided?

As this programme has been created for working professionals only, no placement assistance is given to the participants. However, TalentSprint does get requests from companies seeking certified individuals. Such opportunities will be informed about, they can be explored directly.