Artificial Intelligence – AI Certified Course

 Course Description:

This Artificial Intelligence (AI) Certified Course is designed to equip learners with a solid foundation in AI concepts, tools, and practical applications. The course covers key areas such as machine learning, deep learning, natural language processing, computer vision, and neural networks. Through hands-on projects and real-world case studies, participants will gain practical skills in building AI models, training algorithms, and deploying AI solutions across various industries.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Artificial Intelligence:

• Machine Learning Engineer
• Data Scientist
• Data Analyst
• NLP Engineer
• Computer Vision Engineer
• Robotics Engineer
• AI Product Manager
• AI Research Scientist

Essential Skills you will Develop Artificial Intelligence:

• Programming & Scripting
• Mathematics for AI
• Machine Learning
• Deep Learning
• Natural Language Processing

Tools Covered:

• Tensor Flow – Open-source framework by Google for deep learning and ML.
•  Torch – Deep learning framework by Facebook, widely used in research and production.
• Kera – High-level API for building and training neural networks (runs on  

Syllabus:

Module 1: Introduction to Artificial Intelligence Definition & History of AI Types of AI (Narrow, General, Super AI) AI vs Machine Learning vs Deep Learning Applications of AI in real-world sectors Ethical concerns and limitations.

Module 2: Python for AI Basics of Python programming for data handling and visualization Introduction to Notebook Functions, loops, and conditional statements.

Module 3: Data Handling and Preprocessing Data cleaning and transformation
Handling missing values and outliers Feature selection and extraction Scaling and normalization techniques.

Module 4: Machine Learning Fundamentals Supervised vs Unsupervised Learning
Linear Regression, Logistic Regression Decision Trees and Random Forests
Clustering (K-means, Hierarchical) Model evaluation metrics (accuracy, precision, recall).

Module 5: Deep Learning & Neural Networks Introduction to Neural Networks Activation functions  Forward and Backward Propagation Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs).

Module 6: Natural Language Processing (NLP) Text pre-processing (tokenization, stemming, lemmatization) Sentiment Analysis Word Embeddings (Word2Vec, GloVe)
Language Models and Transformers Chatbots and AI Assistants.

Module 7: AI Tools and Frameworks Tensor Flow and for deep learning learn for ML models OpenCV for Computer Vision NLTK and for NLP.

Module 8: AI in Real-World Applications AI in Healthcare, Finance, Education, and Retail Predictive Analytics and Recommendation Systems Computer Vision applications Autonomous Systems (e.g., self-driving cars).

Module 9: Ethics and Future of AI AI Bias and Fairness AI and Jobs: Impact on Workforce Explainable AI (XAI) AI Governance and Regulations Future trends in AI (AGI, Edge AI).

Module 10: Capstone Project & Certification Define and develop an end-to-end AI project Real-world problem statement Model development, training, and evaluation
Report and presentation Final assessment and certification.

Industry Projects:

• Predictive Maintenance
• Disease Diagnosis System  
• Product Recommendation System  
• Credit Scoring & Loan Default Prediction
• AI for Video Surveillance

Who is this program for?

• Students and Graduates
• Working Professionals
• Researchers and Academicians
• Entrepreneurs and Innovators
• Tech Enthusiasts

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com