AI Fundamentals & Deployment with Open Source Tools – 25 Hours Instructor-Led Training

Artificial Intelligence (AI) is revolutionizing how businesses, governments, and society solve complex problems. From autonomous vehicles to personalized recommendations, AI applications are transforming every sector. But what exactly is AI, and how can you build and deploy AI models using open source tools?

This instructor-led, hands-on course covers AI fundamentals, machine learning, deep learning concepts, and practical deployment strategies using popular open source frameworks such as TensorFlow, PyTorch, and Scikit-learn. Designed for beginners and professionals alike, you will gain the skills needed to build, train, and deploy AI models efficiently.

What is AI?

Artificial Intelligence refers to computer systems designed to perform tasks that typically require human intelligence. This includes learning from data, recognizing patterns, making decisions, and even understanding natural language. Machine Learning (ML) and Deep Learning (DL) are subsets of AI, focusing on algorithms that improve automatically through experience.

How to Build & Deploy AI Models with Open Source Tools?

The AI development lifecycle involves data collection, preprocessing, model building, training, evaluation, and deployment. Popular open source tools like:

• Python: The programming language of choice for AI development.
• TensorFlow & Keras: Frameworks for building and training neural networks.
• PyTorch: Flexible deep learning library favored for research and deployment.
• Scikit-learn: For traditional machine learning algorithms.
• Jupyter Notebooks: Interactive environments to experiment with code and data.
• Docker & Kubernetes: Tools to containerize and deploy models in production.

You will learn step-by-step how to preprocess data, design models, train with real datasets, optimize performance, and deploy models as APIs or web services for real-world use.

Table of Contents for the 25-Hour AI Training

1. Introduction to AI & ML Concepts – Understanding AI, ML, DL, and applications.
2. Python for AI – Essential Python programming, libraries, and environments.
3. Data Handling & Preprocessing – Cleaning, transforming, and visualizing data.
4. Supervised Learning Algorithms – Regression, classification basics with Scikit-learn.
5. Deep Learning Fundamentals – Neural networks, activation functions, and backpropagation.
6. Building Models with TensorFlow & Keras – Creating and training neural networks.
7. Advanced Models with PyTorch – Custom models, dynamic graphs, and GPU usage.
8. Model Evaluation & Tuning – Metrics, cross-validation, hyperparameter tuning.
9. Deploying AI Models – Serving models using Flask, FastAPI, Docker containers.
10. Scaling & Monitoring AI Services – Kubernetes basics, logging, and model updates.

What Advantages Will Learners Gain?

• 💡 Comprehensive Understanding: Grasp core AI concepts and practical workflows.
• 👨‍💻 Hands-on Experience: Build real AI models with live coding labs.
• 🛠️ Open Source Proficiency: Master industry-standard tools without proprietary constraints.
• 🚀 Deployment Skills: Learn to deploy models as APIs and services for production.
• 📈 Career Growth: Equip yourself for roles like AI Engineer, Data Scientist, and ML Engineer.
• 🎓 Project Portfolio: Develop deployable projects to showcase in interviews.
• 🤝 Expert Mentorship: Guidance from AI professionals throughout the course.
• ⌛ Flexible Learning: Complete in 20-25 hours, ideal for working professionals.

Who Should Attend This Course?

• Beginners eager to start a career in AI and Machine Learning.
• Software developers wanting to add AI skills to their toolkit.
• Data analysts and scientists looking to deepen AI knowledge.
• Tech professionals aiming to deploy AI solutions in production.
• Students and hobbyists interested in AI development with open source tools.

Ready to take the leap into Artificial Intelligence? Join our AI Fundamentals & Deployment with Open Source Tools course and transform your understanding into real-world skills. Start building AI models and deploying intelligent applications confidently with expert guidance.

Top 20 Questions & Answers on AI Fundamentals & Deployment with Open Source Tools

1. What is Artificial Intelligence (AI)?

AI refers to computer systems designed to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.

2. What is the difference between Machine Learning and Deep Learning?

Machine Learning is a subset of AI focused on algorithms that learn from data. Deep Learning is a further subset using neural networks with many layers to model complex patterns.

3. Why is Python popular for AI development?

Python offers simple syntax, extensive libraries (like TensorFlow, PyTorch, Scikit-learn), and strong community support, making it ideal for AI projects.

4. What are some common open source tools used for building AI models?

Popular tools include TensorFlow, PyTorch, Keras, Scikit-learn, Jupyter Notebooks, and libraries for data manipulation like Pandas and NumPy.

5. How do you preprocess data for AI models?

Preprocessing includes cleaning data, handling missing values, normalization or standardization, feature extraction, and splitting data into training and testing sets.

6. What is supervised learning?

Supervised learning trains models on labeled data where the correct output is known, such as classification and regression tasks.

7. What are neural networks?

Neural networks are computational models inspired by the human brain, composed of layers of interconnected nodes ("neurons") used to learn complex patterns.

8. How does backpropagation work?

Backpropagation is an algorithm that adjusts neural network weights by calculating the gradient of the loss function, enabling the network to learn from errors.

9. What is the role of activation functions in neural networks?

Activation functions introduce non-linearity, allowing neural networks to model complex data relationships. Examples include ReLU, Sigmoid, and Tanh.

10. How do TensorFlow and PyTorch differ?

TensorFlow uses static computation graphs, while PyTorch uses dynamic graphs. PyTorch is often preferred for research flexibility, TensorFlow for production deployment.

11. What is model evaluation?

Model evaluation involves measuring performance using metrics like accuracy, precision, recall, F1-score, or mean squared error to determine how well the model predicts.

12. Why is hyperparameter tuning important?

Hyperparameter tuning optimizes model settings (like learning rate, batch size) to improve accuracy and generalization on unseen data.

13. What are common deployment methods for AI models?

Models can be deployed as REST APIs using frameworks like Flask or FastAPI, containerized with Docker, and orchestrated via Kubernetes for scalability.

14. How does Docker help in AI model deployment?

Docker packages AI applications and dependencies into containers, ensuring consistent behavior across different environments.

15. What is Kubernetes and why is it useful?

Kubernetes automates deployment, scaling, and management of containerized applications, making it ideal for production AI services.

16. Can AI models run on GPUs?

Yes, GPUs accelerate AI training and inference by parallelizing large matrix computations, drastically reducing processing time.

17. What is overfitting in AI models?

Overfitting occurs when a model learns noise or details in training data, performing well on training but poorly on new data.

18. How can overfitting be prevented?

Techniques include using more training data, regularization methods (like dropout), early stopping, and cross-validation.

19. What role do Jupyter Notebooks play in AI development?

Jupyter Notebooks provide an interactive environment to write, test, and visualize code and data, perfect for experimentation and prototyping.

20. How long does it typically take to learn AI fundamentals and deployment?

With focused study, most learners can grasp the fundamentals and practical deployment skills in about 20-25 hours of instructor-led training.