

3.5
|23+ Reviews
|43+ Course Learners

This course provides a comprehensive introduction to Data Science and Artificial Intelligence (AI), combining theoretical knowledge with practical skills. Learners begin with Python programming, covering data types, control structures, functions, and essential libraries like NumPy, Pandas, and Matplotlib. The curriculum emphasizes data cleaning, preprocessing, exploration, and visualization, helping students extract insights from real-world datasets. Core machine learning algorithms, including supervised, unsupervised, and reinforcement learning, are taught with hands-on practice using Scikit-learn. Advanced topics include deep learning, neural networks, TensorFlow, PyTorch, natural language processing (NLP), and computer vision. With integrated concepts of statistics, probability, and linear algebra, participants learn model optimization techniques. Through projects and case studies, learners gain practical experience to analyze data, build AI models, and implement predictive, data-driven solutions across industries

Career Path Guidance
60hrs of Session
Real-time Use Cases
24/7 Lifetime Support
Certificate Based Curriculum
Flexible Schedule
One-One Doubt Clearing
Job Support
Unlock your team's potential with continuous guidance and measurable outcomes, ensuring sustained development at every step of the learning journey from virtual machines and storage solutions to app development and networking. Delve into hands-on exercises and expert-led sessions to master the essential skills needed to leverage course capabilities effectively.
• Python Fundamentals: Syntax, Data Types, Control Flow
• Functions, Lambda, Decorators & Generators
• OOP Concepts, Modules & Packages
• Data Structures & Algorithmic Problem Solving
• Exception Handling & File Handling
• APIs & Web Data Integration (REST, JSON, Requests)
• Libraries for AI: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• NEW: Py Torch Lightning & TensorFlow 2.x Overview
• Bonus: Best Practices for Clean Code & Performance Optimization
• Cloud Computing Fundamentals (AWS, GCP, Azure)
• Cloud-based ML Services: AWS Sage maker, GCP Vertex AI, Azure ML Studio
• Version Control with Git & GitHub
• OS & Shell Scripting for Data Workflows
• CI/CD for ML Pipelines – GitHub Actions, Jenkins, Azure DevOps
• Containerization using Docker & Kubernetes
• NEW: Deploying Models with Stream lit & Gradi on Cloud
• Descriptive & Inferential Statistics
• Probability Theory & Distributions
• Linear Algebra for ML: Vectors, Matrices, Eigenvalues
• Calculus for Optimization
• Gradient Descent, Loss Functions & Regularization
• Numerical Methods & Convergence
• NEW: Hands-on with Python (NumPy/SciPy) for Math Concepts
• Relational & Non-Relational Databases
• SQL Basics: DDL, DML, DQL, TCL
• Aggregations, Grouping, and Complex Joins
• Subqueries, Nested Queries & Window Functions
• Query Optimization & Performance Tuning
• NEW: Data Pipelines using Airflow & DBT
• NEW: Introduction to NoSQL (MongoDB) for Unstructured Data
• EDA Workflow & Hypothesis Testing
• Data Cleaning, Transformation, and Feature Engineering
• Statistical Summary & Correlation Analysis
• Visualization with Matplotlib, Seaborn, and Polly
• Power BI & Tableau for Business Visualization
• NEW: Automated EDA Tools (Sweet viz, Pandas-Profiling)
• NEW: Data Storytelling Techniques
• Supervised Learning: Regression, Classification
• Unsupervised Learning: Clustering, PCA, Dimensionality Reduction
• Model Evaluation Metrics (Accuracy, Precision, Recall, ROC, AUC)
• Bias-Variance Trade-off & Cross-Validation
• NEW: Explainable AI (SHAP, LIME)
• Model Deployment using Flask & Fast API
• NEW: Feature Store & Data Versioning
• Ensemble Methods: Bagging, Boosting, Stacking
• Boost, Light, Cat Boost
• Hyperparameter Tuning (Grid, Random, Bayesian Search)
• Feature Selection & Dimensionality Reduction
• NEW: Auto ML Tools (H2O.ai, Auto- Sc learn, Py Caret)
• NEW: Model Monitoring & Drift Detection
• Neural Network Fundamentals: Perceptron, MLP
• Activation Functions, Optimizers & Loss Functions
• CNNs for Image Recognition (VGG, Res Net, Efficient Net)
• RNN, LSTM, GRU for Sequential Data
• Transfer Learning & Fine-Tuning
• NEW: Vision Transformers (Vitt), CLIP Models
• NEW: Neural Network Deployment using ONNX & Torch Serve
• Tensor Board for Model Tracking
• NLP Fundamentals & Text Preprocessing
• Word Embeddings: Word2Vec, Glove, TF-IDF
• Sequence Models (RNN, LSTM, GRU)
• Transformers Architecture & BERT Models
• Named Entity Recognition (NER) & Sentiment Analysis
• NEW: LLM Fine-tuning (BERT, GPT) using Hugging Face
• NEW: Lang Chain, Vector Databases (FAISS, Chroma DB)
• Introduction to Generative AI & LLMs
• GPT, Claude, Gemini – Ecosystem Overview
• GANs, VAEs, Diffusion Models
• Text-to-Image Models (Stable Diffusion, DALL·E)
• Prompt Engineering Techniques & Templates
• NEW: Fine-Tuning & Retrieval-Augmented Generation (RAG)
• NEW: Prompt Patching, Chain-of-Thought, and Context Injection
• Ethical AI & Responsible Prompting
• Introduction to Mops
• End-to-End ML Lifecycle
• CI/CD for ML Pipelines
• Model Tracking with Moldflow, DVC
• Model Versioning, Deployment & Monitoring
• Containerization with Docker & Kubernetes
• NEW: Kubeflow & Vertex AI Pipelines
• NEW: Cloud Mops (AWS Sage maker Pipelines, Azure ML)
• Capstone: Full Mops Project from Training to Deployment
• Predictive Analytics (Finance, Retail, Healthcare)
• Computer Vision Project (Face Recognition, Defect Detection)
• NLP Project (Chatbot, Sentiment Analyzer)
• Generative AI Project (LLM-based Assistant or Content Generator)
• Mops Pipeline Deployment (End-to-End)
Learners develop a strong foundation in Python programming, including data types, control structures, functions, and libraries like NumPy, Pandas, Matplotlib, and Seaborn. They gain expertise in data collection, cleaning, preprocessing, and visualization to extract meaningful insights from raw datasets. Core machine learning skills include building and evaluating supervised, unsupervised, and reinforcement learning models using Scikit-learn. Advanced competencies include deep learning, neural networks, TensorFlow, PyTorch, natural language processing (NLP), and computer vision. Students also acquire knowledge of statistics, probability, and linear algebra for model optimization and predictive analytics. Practical skills include data analysis, feature engineering, model deployment, and performance evaluation, making learners capable of solving real-world business problems. By the end of the course, participants are equipped to design, develop, and implement AI-driven solutions across diverse industries.
Python Programming
Data Analysis & Visualization
Data Preprocessing & Cleaning
Machine Learning
Deep Learning
Natural Language Processing (NLP)
Computer Vision
Statistics & Probability
Model Deployment & Evaluation
Upon successful completion of the Artificial intelligence course, participants will be awarded an Artificial intelligence Expert Certificate. This certificate acknowledges the acquisition of in-depth knowledge and proficiency in utilizing Artificial intelligence tools and services effectively.
Certificate Benefits
Industry Recognition
Demonstrated Skills
Proof of Achievement
Career Advancement
Artificial intelligence
AI Engineer / Research Scientist
NLP or Computer Vision Specialist

Basic understanding of Python and mathematics .
Foundational knowledge of data analysis and algorithms.



Artificial intelligence


Advanced AI & Deep Learning with TensorFlow
AI for Business Automation & Robotics

Deep Learning, Neural Networks, and Natural Language Processing
Model Optimization and AI Deployment Techniques
Testimonials across all the courses
Meet our Artificial intelligence mentors: experts in Artificial intelligence ready to guide you. Gain insights, hands-on support, and personalized guidance tailored to your Artificial intelligence learning goals. Elevate your skills with our esteemed mentor network.
Trainers
Learners
Placements
Awards Recieved
Excellence
Growth
Success
Leadership
Opportunity
Mastery
Excellence
Growth
Success
Leadership
Opportunity
Mastery
Achieved Companies
Donations Received
Clarity in the Last Year
Donations Received
We have been working with some Fortune 500 clients



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Our course fees vary depending on the program you choose. We do offer periodic discounts and flexible installment options to make learning more accessible. For specific details, feel free to contact our support team.
Yes, we have a refund policy in place to ensure customer satisfaction. For complete details, please review our refund terms on our website or contact us directly.
Yes, we offer certifications upon successful completion of the course and meeting all the required assessments.
While we do not guarantee job placements, we provide robust placement assistance to help you secure opportunities, including resume building, interview preparation, and access to our partner networks.
Yes, we provide ongoing job support, including career guidance, networking opportunities, and updates on relevant job openings, to help you apply your skills effectively in the workplace.
Absolutely! You will have 3 months access to course materials, including recorded sessions, study guides, and additional resources, so you can revisit them anytime.
Our instructors are industry experts with extensive experience in their respective fields. They are carefully vetted to ensure high-quality teaching, and they stay updated on the latest trends and technologies.
If you miss a live class, don’t worry! All sessions are recorded, and you’ll have access to the recordings, so you can catch up at your convenience.
Yes, you can download the recorded sessions for offline use. This ensures you can continue learning even without an internet connection.
Yes, we offer an option to purchase course recordings separately for certain programs. Please contact our team to know which courses qualify for this option.