AI & Machine Learning

Beginner to Mastery

Applied Data Science with AI

Comprehensive Applied Data Science & AI curriculum

Hands-on projects with real-world applications

Taught by industry experts

Focus on industry-relevant tools and technologies

Group Enrollment with Friends or Colleagues

Applied Data Science with AI

Course Duration

300 Hours

Next Batch

14 September 2025

Course Material

Live. Online. Interactive.

Flexible learning paths

Globally recognized certification

Career support and networking opportunities

Strong emphasis on practical skills and innovation

Highlight Applied Data Science with AI

KEY HIGHLIGHTS OF APPLIED DATA SCIENCE WITH AI PROGRAM

  • Weekly sessions with industry professionals
  • Dedicated Learning Management Team
  • 300 hours of hands-on learning experience
  • Over 105 hours live sessions spread across 05 months
  • 105 hours of self-paced Learning
  • Learn from IIT Faculty & Industry experts
  • More than 40+ industry-related projects and case studies
  • Personalised mentorship sessions with industry experts
  • 24*7 Support
  • Dedicated Learning Management Team
  • 1:1 Mock Interview
  • No-Cost EMI Option
  • Designed for both working professionals and fresh graduates
  • High Demand and Career Opportunities
  • Competitive Edge and Innovation
  • Problem-Solving and Critical Thinking

WHY JOIN APPLIED DATA SCIENCE WITH AI PROGRAM?

High Demand

Learn to lead high-demand careers in data analysis, Machine Learning and AI not limited by industry inclination.

Comprehensive Data Science with AI Training

Gain expertise across all major aspects of artificial intelligence and machine learning.

Hands-On Learning

Work on projects which develop real-world skills and experience.

Industry-Relevant Curriculum

Get trained about the latest tools and tricks that are currently trending in Industry.

UPCOMING BATCH:

14 September 2025

SkillzRevo

SkillzRevo Solutions

30 MINUTE MEETING

Web conferencing details provided upon confirmation.

Corporate Training, Enterprise training for teams

Batch schedule

BatchBatch Type
Online Live Instructor Led SessionFull-Time
Online Live Instructor Led SessionPart-Time

Regional Timings

BatchBatch Type
IST (India Standard Time)09:00 PM–12:00 AM
Bahrain, Qatar, Kuwait, Saudi Arabia06:30 PM–09:30 PM
UAE / Oman07:30 PM–09:00 PM

Applied Data Science with AI OVERVIEW

This Program provides a structured path through the world of artificial intelligence, beginning with Python programming skills and basic statistics. With this bundle, you’ll delve into machine learning, deep learning, computer vision and natural language processing to acquire the tools of this trade. The program also covers reinforcement learning, equipping you to develop AI solutions that learn from interactions. By the end, you’ll have a strong foundation to apply AI technologies in real-world scenarios.

ENROLL NOW & BOOK YOUR SEAT AT FLAT 50% WAIVER ON FEE

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Applied Data Science with AI Objectives

This course aims to provide a comprehensive understanding of key AI and ML principles. You will start from Python Programming then go on to statistical methods for data analysis. It enables you to have hands-on experience of machine learning algorithms and go deep for computer vision, natural language processing etc. You will also learn about reinforcement learning which is a subcategory of machine learning that examines how AI agents may improve by just carrying out some actions. It sounds like a lot, but by bringing all of these elements together the course is designed to help you solve hard problems and do serious innovation in AI

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Why Learn Applied Data Science with AI ?

MASTER ESSENTIAL TOOLS

Gain proficiency in Python programming, statistical analysis, and data visualisation to effectively explore and understand data.

BUILD PREDICTIVE MODELS

Learn machine learning and deep learning techniques to create intelligent systems capable of making accurate predictions and decisions.

UNLOCK DATA POTENTIAL

Discover hidden patterns and insights within your data through data analysis and feature engineering, driving informed decision-making.

EXPLORE DEEP LEARNING

Design and optimise neural networks for advanced tasks like computer vision and natural language processing.

TAKE REINFORCEMENT LEARNING

Discover how AI agents learn at an intuitive level by interacting with their environment and improving over time.

REAL-WORLD IMPACT

Apply your know-how to craft solutions for critical problems at the heart of AI.

BE READY FOR INDUSTRY REQUIREMENTS

Learn the most popular skills required to get a job in one of the fastest growing fields, AI.

ENHANCE COMMUNICATION

Effectively convey complex information and findings using data visualisation techniques.

Program Advantages

Comprehensive skill set in Python programming, statistical analysis, machine learning, deep learning, computer vision, NLP and Reinforcement learning.

Industry-relevant knowledge with a focus on current tools and approaches.

Practical experience through projects and assignments.

Expert instruction from knowledgeable educators.

Familiarity with the latest libraries and frameworks.

Development of critical thinking and problem-solving abilities.

Career advancement opportunities include various roles such as Data Analyst, ML Engineer, Deep Learning Engineer, Computer Vision Engineer, NLP Engineer, Data Scientist, AI Engineer and AI specialist.

A collaborative classroom environment that encourages teamwork and relationship-building.

Create a portfolio for employers to see skills.

Foundation for continued learning in an evolving world of Data Science and AI.

Description

Applied Data Science with AI program Certifications

NASSCOM

NASSCOM

Course Completion

Course Completion

Project Completion

Project Completion

Applied Data Science with AI Curriculum

Lecture 01: Introduction to Python, Why Python, Variables, Data Types, Type Casting, Strings, Indexing
Lecture 02: Operators and Conditional Statements, Looping Statements and its Control Statement
Lecture 03: Lambda Functions, *args, **kwargs, Functions
Lecture 04: Data Structures - List, Tuple and List Comprehensions
Lecture 05: Data Structures - Set and Dictionaries
Lecture 06: Classes, Objects and Constructors, Inheritance
Lecture 07: Polymorphism, Abstraction and Encapsulation
Lecture 08: Connecting to Databases, Establishing connections to databases, Executing SQL Queries, ORM (Object-Relational Mapping), Working with NoSQL Databases
Lecture 09: Introduction to Numpy and Pandas
Lecture 10: Introduction to Seaborn and Matplotlib
Lecture 11: Introduction to Statistics, Descriptive Statistics, Sample, Population, Measures of Central Tendency, Standard Deviation
Lecture 12: Variance, Range, IQR, Outliers, Correlation, Covariance, Skewness, Kurtosis, Probability
Lecture 13: Probability, Probability Distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 14: Normal Distribution, Type I & Type II Error
Lecture 15: T-test, Z-test, Hypothesis Testing Interview Questions
Lecture 16: Introduction to ML, Types of Variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression
Lecture 17: Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest
Lecture 18: Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R² Score, Adjusted R² Score, F1 Score
Lecture 19: Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, XGBoost
Lecture 20: Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical Clustering
Lecture 21: Introduction to Neural Network, Forward Propagation, Activation Function
Lecture 22: Activation Functions (Linear, Sigmoid, ReLU, Leaky ReLU), Optimizers, Gradient Descent, Stochastic Gradient Descent
Lecture 23: Mini Batch Gradient Descent, Adagrad, Padding, Pooling, Convolution, Checkpoints and Neural Networks Implementation
Lecture 24: Introduction to Time Series Analysis, Various Components of the TSA, Decomposition Method (Additive Method and Multiplicative)
Lecture 25: ARMA and ARIMA
Lecture 26: Introduction to Image Processing, Feature Detection, OpenCV
Lecture 27: Convolution, Padding, Pooling & its Mechanisms
Lecture 28: Forward Propagation & Backward Propagation for CNN
Lecture 29: CNN Architectures like AlexNet, VGGNet, InceptionNet, ResNet, Transfer Learning
Lecture 30: Introduction to Text Mining, Text Processing using Python and Introduction to NLTK
Lecture 31: Sentiment Analysis, Topic Modeling (LDA) and Named-Entity Recognition
Lecture 32: BERT (Bidirectional Encoder Representations from Transformers), Text Segmentation, Text Mining, Text Classification
Lecture 33: Automatic Speech Recognition, Introduction to Web Scraping
Lecture 34: RL Framework, Components of RL Framework, Examples of Systems
Lecture 35: Types of RL Systems, Q-Learning
Lecture 36: Project Session

Applied Data Science with AI Skills Covered

Data Analysis
Data Manipulation
Statistical Analysis
Data Visualization
Machine Learning
Model Evaluation
Feature Engineering
Neural Networks
Transfer Learning
Deep Learning
Computer Vision Techniques
Image Processing
Text Processing
Sentiment Analysis
Language Modelling
Reinforcement Learning Algorithms

Applied Data Science with AI Tools Covered

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Applied Data Science with AI Program Benefits

Applied Data Science with AI Program Benefits Illustration

CAREER OPPORTUNITIES AFTER THIS COURSE

Research Scientist Salary Range

Min

$85,000

Average

$110,000

Max

$140,000

Projects

Learn Practical Data Science with Real-World AI Projects

Practice Essential Tools

Designed By Industry Experts

Get Real-world Experience

Machine Learning
NO. OF PROJECTS: 10
Deep Learning
NO. OF PROJECTS: 10

Capstone Projects of this Program

Multimodal Text-to-Image Generator

A system that creates high-quality, realistic images from descriptive text inputs, blending multiple data types for creative visual output.

AI Music Composer with Genre Adaptation

An AI tool that generates original musical compositions while adapting to specific genre styles, tempo, and mood preferences.

Realistic Deepfake Video Synthesizer

A tool that realistically manipulates and synthesizes video content to create authentic-looking deepfake footage for various applications.

Personality-Driven Conversational AI Assistant

A chatbot designed to interact with a distinct and consistent personality, providing a more natural and engaging user experience.

Artistic Style Transfer Engine for Videos

A program that applies the artistic style of a source image to every frame of a video, creating a new, stylized visual experience.

Context-Aware Text Summarization System

An advanced system that generates concise summaries of long texts by understanding the document's broader context and key information.

Voice Cloning and Speech Style Transfer Tool

A tool that replicates a speaker's voice and transfers a specific speech style or emotion to new synthesized audio content.

Interactive AI Storyteller with Image Generation

An AI that collaboratively creates stories with users, dynamically generating scenes and characters based on a developing narrative.

2D Sketch to 3D Object Generation Pipeline

A seamless workflow that converts simple two-dimensional sketches into detailed, three-dimensional models for creative and design purposes.

Synthetic Data Generator for Model Training

A system that produces large and diverse datasets for machine learning models, overcoming data scarcity for effective training.

AI-Powered Video Subtitle Generator and Translator

An automated solution that transcribes spoken language in videos and translates it into accurate subtitles for global accessibility.

Generative Design Tool for Product Prototyping

An AI application that rapidly generates numerous product design prototypes based on a set of constraints and functional requirements.

RAG-based Research Assistant (Retrieval-Augmented Generation)

An intelligent assistant that answers complex queries by retrieving relevant information from large documents and generating coherent responses.

Multilingual Chatbot with Memory and Personality

A conversational agent that can communicate in multiple languages while maintaining a consistent personality and recalling past interactions.

AI-Powered Comic Strip Generator from Text Prompts

A tool that creates unique comic strips by converting user text prompts into sequential images with a consistent artistic style.

Explainable AI Engine for Text Classification

An AI system that classifies text and provides clear, understandable reasons for its decisions, ensuring transparency and trust.

Autonomous Slide Generator from Research Papers

An AI that automatically extracts key findings and data from research papers to create well-structured, presentable slide decks.

AI Assistant for Code Generation and Explanation

A helpful tool that writes code snippets and explains complex programming logic in simple terms, assisting developers with their tasks.

AI-Driven Marketing Content Generator Across Formats (text, image, video)

A unified platform that produces marketing materials in various formats, including text, images, and videos, from a single campaign brief.

Real-Time Emotion Detection and Response AI System

A system that identifies human emotions from facial expressions or speech and generates appropriate, empathetic responses in real time.

Job Obligation After This Course

WE CAN APPLY FOR JOBS IN

Create and Deploy AI Models, & Solutions for Real World Problems

Collect, clean and preprocess data to prepare high level inputs of your ML algorithm.

Monitor, tune and enhance AI model performance.

Work with cross-functional teams (data scientists, ML/AI engineers) to integrate AI solutions.

Stay updated with the latest advancements in AI and machine learning technologies.

Document development processes and communicate results to technical and non-technical stakeholders.

Ensure ethical AI practices, focusing on fairness, transparency, and responsible use of AI technologies.

Identify and solve technical challenges, driving innovation in AI projects.

Ensure scalability and effective deployment of AI models in production environments.

Engage in continuous learning to keep pace with emerging tools and methodologies in AI.

Companies Hiring for this Course

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Admission Process

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Course Fees & Financing

Course Fees

Enroll Now & Save Up To

60%

In USD

$599

In INR

49,999

Inclusive of All Taxes

Enroll Now →
Payment Partners

We partnered with financing companies to provide competitive finance options at 0% interest rate with no hidden costs.

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UPCOMING BATCHES/PROGRAM COHORTS

BatchDateTime (IST)Batch Type
Online Live Instructor Led Session30 August 20259 PM to 12 AMBatch 1
Online Live Instructor Led Session14th Sept 20259 PM to 12 AMBatch 2

COMPARISON WITH OTHERS

FeatureOur CourseCOMPETITOR A
Curriculum DepthComprehensive, covering Python, ML, DL, NLP, CV, and RLOften focused on basic concepts or specific areas like ML or DL
Hands-On ExperienceEmphasises practical projects and real-world applicationsVaries; may include limited practical exposure
Instructor ExpertiseTaught by industry professionals and expertsOften led by academic instructors or general trainers
Industry-Relevant SkillsFocused on current tools and technologies used in the industryMay include outdated or less industry-relevant content
CertificationProvides globally recognized certificationCertification may not be widely recognized
Career SupportIncludes career services and networking opportunitiesLimited or no career support offered
FlexibilityStructured yet adaptable learning pathsMay have rigid schedules or limited flexibility
Learning ResourcesAccess to extensive resources, including updated materialsOften limited to basic learning materials
Project-Based LearningStrong focus on project-based learningMay include fewer or less challenging projects

Frequently Asked Questions