Accelerate Your Future with Artificial Intelligence Training

Ready to dive into the world of AI and build cutting-edge skills that shape tomorrow? Join IIPD Global’s Artificial Intelligence Professional Course and gain the essential tools to thrive in today's data-driven world.

Course Overview:
Our AI course is designed to take you from foundational theory to real-world applications. Whether you're an aspiring data scientist or a professional looking to upskill, this program offers a balanced blend of theory, programming, and practical AI deployment.
 

Key Features:.   

1. AI Fundamentals: Understand the evolution, types, and core concepts of AI.

2. Python for AI: Code using Jupyter Notebooks and essential Python libraries.

3. Math for AI: Learn key concepts from linear algebra, probability & calculus.

4. Machine Learning Models: Apply supervised, unsupervised & clustering methods.

5. Deep Learning: Build neural networks with CNNs, RNNs & LSTMs.

6. AI Tools: Use NumPy, Pandas & Scikit-learn for data processing and modeling.

7. Model Deployment: Learn APIs, Flask/FastAPI, and ONNX for integration.

8. Computer Vision & NLP: Work on image and text-based AI projects.

9. Ethical AI: Explore fairness, bias, and responsible AI practices.

Course Outcome:
 
By the end of this AI course, participants will be able to:

1. Grasp the foundational principles and history of Artificial Intelligence

2. Code AI solutions using Python and Jupyter Notebooks

3. Apply key mathematical concepts used in AI and ML algorithms

4. Build and evaluate supervised and unsupervised machine learning models

5. Use core AI libraries like NumPy, Pandas, and Scikit-learn for data handling

6. Develop and train deep learning models with CNNs, RNNs, and LSTMs

7. Tune models through cross-validation and hyperparameter optimization

8. Deploy AI models using APIs with Flask/FastAPI and ONNX frameworks

9. Solve real-world problems using Computer Vision and Natural Language Processing

10.Recognize ethical concerns, ensuring fairness and explainability in AI systems

Prerequisite:

To get the most out of this course, learners should have:

• Basic understanding of programming concepts

• Familiarity with high school-level mathematics (algebra, probability, and functions)

• Interest in data, automation, and problem-solving using technology

• No prior AI or machine learning experience required — beginners are welcome!

Course outline:

This comprehensive Artificial Intelligence course is designed to build a strong foundation in AI concepts, tools, and practical applications. From core principles to real-world deployment, learners will explore every stage of modern AI development.

Module 1: Core Concepts of AI

History and evolution of Artificial Intelligence

Types of AI: Narrow vs. General

Symbolic AI vs. Machine Learning

Module 2: Programming for AI

• Python fundamentals for AI applications

• Using Jupyter Notebooks for development

• Data structures and control flow in Python

Module 3: Mathematical Foundations

• Linear algebra basics for AI

• Probability and statistics

• Intro to calculus relevant to ML

Module 4: Classical Machine Learning

• Supervised vs. Unsupervised learning

• Regression & Classification algorithms

• Clustering techniques: K-Means, DBSCAN

Module 5: AI Tools and Libraries

• Hands-on with NumPy, Pandas, and Scikit-learn

• Data preprocessing and cleaning workflows

• Model training, validation, and evaluation

Module 6: Deep Learning Fundamentals

• Understanding Neural Networks

• Backpropagation and loss functions

• CNNs, RNNs, and LSTM architectures

Module 7: Model Evaluation & Optimization

• Bias-variance tradeoff explained

• Cross-validation techniques

• Hyperparameter tuning methods

Module 8: Building AI Systems

• Model serialization and deployment

• RESTful APIs with Flask and FastAPI

• ONNX and model interoperability

Module 9: Computer Vision & NLP

• Image classification and object detection

• Text classification and named entity recognition

• Word embeddings: Word2Vec, GloVe

Module 10: Ethics in AI

• Addressing fairness and bias

• Introduction to Explainable AI (XAI)

• Societal, ethical, and legal considerations

Methodology:
 
This Artificial Intelligence course offers a blend of theoretical knowledge and hands-on experience. Students will explore key AI concepts, including machine learning, deep learning, and neural networks, while gaining proficiency in Python programming and using tools like NumPy, Pandas, and Scikit-learn. The course emphasizes practical skills such as model evaluation, deployment, and AI system integration. Additionally, students will delve into the ethical aspects of AI, learning to mitigate bias and promote fairness. By the end, learners will be equipped to develop, deploy, and optimize AI systems for real-world applications.