5-Day Training Course Agenda on Artificial Intelligence
Course Title: Introduction to Artificial Intelligence (AI)
Duration: 5 Days (8 hours/day)
Format: Workshop-based, with theoretical sessions, practical exercises, and interactive discussions.
Target Audience: Beginners, IT professionals, engineers, students, and anyone interested in AI technologies and applications.
Day 1: Introduction to Artificial Intelligence
- 9:00 AM – 9:30 AM:
- Welcome and Course Overview
- Introduction to the course objectives, learning outcomes, and schedule.
- 9:30 AM – 10:30 AM:
- Defining Artificial Intelligence (AI)
- What is AI? A high-level understanding of AI as the simulation of human intelligence by machines.
- Core AI concepts: Machine learning (ML), deep learning (DL), natural language processing (NLP), and robotics.
- 10:30 AM – 11:30 AM:
- History and Evolution of AI
- Overview of AI development, key milestones, and breakthrough moments.
- AI from rule-based systems to modern AI driven by machine learning.
- 11:30 AM – 1:00 PM:
- Branches of AI
- Narrow AI vs. General AI.
- Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Robotics, and Expert Systems.
- 1:00 PM – 2:00 PM:
- Lunch Break
- 2:00 PM – 3:30 PM:
- Core AI Algorithms
- An introduction to key AI algorithms: Decision trees, k-nearest neighbors, neural networks, and more.
- 3:30 PM – 4:30 PM:
- AI Applications Overview
- AI in the real world: Autonomous vehicles, healthcare, finance, customer service, and more.
- 4:30 PM – 5:00 PM:
- Q&A and Group Discussion
- Open session for discussing Day 1 topics and asking questions.
Day 2: Machine Learning and Data Processing
- 9:00 AM – 10:30 AM:
- Introduction to Machine Learning
- Understanding machine learning as a core component of AI.
- Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
- 10:30 AM – 12:00 PM:
- Key ML Algorithms and Models
- Introduction to common algorithms: Linear regression, classification (SVM, Decision Trees), clustering, and more.
- Model training, validation, and evaluation.
- 12:00 PM – 1:00 PM:
- Lunch Break
- 1:00 PM – 3:00 PM:
- Data Processing and Feature Engineering
- The role of data in AI: Data collection, cleaning, transformation, and feature engineering.
- Tools for data processing: Pandas, NumPy, etc.
- 3:00 PM – 4:30 PM:
- Hands-on Exercise
- Build a basic machine learning model using Scikit-Learn in Python.
- Analyze and interpret model results.
- 4:30 PM – 5:00 PM:
- Q&A and Discussion
- Reflection on machine learning concepts and practical implementation.
Day 3: Deep Learning and Neural Networks
- 9:00 AM – 10:30 AM:
- Introduction to Deep Learning
- Understanding deep learning and how it relates to AI and ML.
- Basics of neural networks: What are neurons, layers, and how networks learn.
- 10:30 AM – 12:00 PM:
- Neural Network Architectures
- Deep Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their applications.
- Overview of backpropagation and gradient descent.
- 12:00 PM – 1:00 PM:
- Lunch Break
- 1:00 PM – 3:00 PM:
- Hands-on Deep Learning Exercise
- Build a neural network using TensorFlow or Keras for image recognition tasks.
- Understand model training, testing, and evaluation.
- 3:00 PM – 4:30 PM:
- Applications of Deep Learning
- AI in computer vision, natural language processing, and speech recognition.
- 4:30 PM – 5:00 PM:
- Q&A and Discussion
- Open session for clarifying deep learning concepts and hands-on learning experiences.
Day 4: AI Applications in NLP and Computer Vision
- 9:00 AM – 10:30 AM:
- Introduction to Natural Language Processing (NLP)
- Overview of NLP and its importance in AI.
- Key NLP tasks: Text generation, machine translation, speech recognition, and sentiment analysis.
- 10:30 AM – 12:00 PM:
- Hands-on NLP Exercise
- Build a text classification model using NLP techniques (e.g., BERT or GPT models).
- Evaluate model performance.
- 12:00 PM – 1:00 PM:
- Lunch Break
- 1:00 PM – 2:30 PM:
- Introduction to Computer Vision
- Overview of AI’s role in image recognition, object detection, and facial recognition.
- Applications of Convolutional Neural Networks (CNNs) in computer vision tasks.
- 2:30 PM – 4:00 PM:
- Hands-on Computer Vision Exercise
- Build an image classifier using CNNs to recognize objects from a dataset (e.g., CIFAR-10 or MNIST).
- 4:00 PM – 5:00 PM:
- Q&A and Group Discussion
- Open forum for discussing real-world applications of NLP and computer vision.
Day 5: Ethics, AI Trends, and Career Paths in AI
- 9:00 AM – 10:30 AM:
- Ethical Considerations in AI
- AI ethics: Bias, fairness, and transparency in AI models.
- Addressing challenges related to data privacy, algorithmic bias, and decision-making.
- 10:30 AM – 12:00 PM:
- The Future of AI and Industry Trends
- Latest advancements in AI: Autonomous systems, AI in healthcare, AI for sustainability, etc.
- Emerging AI technologies like Generative AI, Edge AI, and AI for IoT.
- 12:00 PM – 1:00 PM:
- Lunch Break
- 1:00 PM – 2:30 PM:
- Career Paths in AI
- Exploring AI roles: Data Scientist, Machine Learning Engineer, AI Researcher, AI Product Manager, and more.
- Skills and qualifications required for various AI careers.
- 2:30 PM – 4:00 PM:
- Building Your AI Portfolio
- Tips on creating AI projects, showcasing work, and building a portfolio to attract employers.
- Overview of certification programs and learning resources for AI professionals.
- 4:00 PM – 5:00 PM:
- Wrap-up and Feedback Session
- Summary of key takeaways from the course.
- Open feedback from participants and certification distribution.
Learning Objectives:
- Understand AI fundamentals and its key subfields (ML, DL, NLP, and Computer Vision).
- Gain hands-on experience in building machine learning and deep learning models.
- Explore real-world AI applications in various industries.
- Learn about ethical issues in AI and how to mitigate them.
- Identify career opportunities in AI and develop a plan for professional growth in the AI field.
TO JOIN THE COURSE….MAIL TO
mail@institute-of-it-trainings.com
+91 9811841782