Introduction to Artificial Intelligence with Python
Explore the fundamentals of Artificial Intelligence and learn how to implement AI algorithms using Python. This hands-on training covers essential concepts, key algorithms, and practical applications in AI, perfect for beginners.
Duration: 1 Day
Hours: 3 Hours
Training: Live Training
Training Level: All Level
About the Course:
In this live training, participants will get an introductory understanding of Artificial Intelligence and how Python is used to develop AI models. We’ll walk through the foundational concepts of AI, followed by practical coding exercises to implement algorithms in Python. This session will equip you with the skills to start your AI journey and build your own simple AI models.
Course Objective:
- Gain a clear understanding of Artificial Intelligence and its key concepts.
- Learn how to set up and use Python libraries commonly used in AI.
- Implement AI algorithms using Python for real-world applications.
- Understand the basics of machine learning, neural networks, and natural language processing (NLP).
- Build a foundation to explore advanced AI topics.
Who is the Target Audience?
- Beginners interested in AI and Python.
- Python developers looking to transition into AI.
- Data science enthusiasts and professionals curious about AI applications.
- Students and anyone looking to explore AI programming.
Basic Knowledge:
- Basic understanding of Python programming.
- Familiarity with basic programming concepts (loops, functions, variables).
- No prior experience in Artificial Intelligence is required.
Curriculum
Total Duration: 3 Hours
Introduction to Artificial Intelligence
- What is AI?
- Types of AI: Narrow AI vs. General AI
- AI applications in real-world scenarios
- Brief overview of AI fields (Machine Learning, Deep Learning, NLP, etc.)
Python for AI
- Introduction to Python libraries: NumPy, Pandas, Matplotlib
- Setting up your Python environment for AI (installing packages, IDE setup)
- Working with data using Python for AI applications
Understanding Machine Learning
- What is Machine Learning?
- Types of Machine Learning: Supervised vs. Unsupervised Learning
- Basic ML algorithms: Linear regression, k-nearest Neighbors (k-NN), Decision Trees
- Implementing your first ML model in Python
- Hands-on: Build a simple classifier using Scikit-learn
Introduction to Neural Networks
- What are neural networks?
- Structure of a simple neural network
- Introduction to Deep Learning and Keras
- Building a basic neural network in Python
- Hands-on: Simple neural network implementation using Keras
Introduction to Natural Language Processing
- What is NLP?
- Common NLP tasks (text classification, sentiment analysis, etc.)
- Overview of popular NLP libraries (NLTK, SpaCy)
- Hands-on: Text classification example using Python
Real-World AI Applications
- How AI is used in various industries: healthcare, finance, and robotics
- Case studies of AI in action
Q&A and Wrap-up
- Answer any remaining questions