Advanced Python for AI

Advanced Python for AI

Accelerated 3-Day Immersion in Modern Machine Learning & Language Models - From Foundations to Agentic Systems

This intensive 3-day course is designed to transition participants from foundational Python programming to advanced applications in data science and analytics, with a concentrated focus on machine learning (ML) and deep learning (DL) principles. The curriculum integrates modern methodologies, including agentic development and Large Language Model (LLM) integration via APIs and on-premises installations, utilizing models from platforms like Hugging Face. Our trainer brings over 10,000 hours of training experience and more than 30 years of industry expertise, ensuring a rich and insightful learning journey.

Learning Outcomes

By the end of this course, participants will:

1. Master Python for Data Science: Develop proficiency in Python programming, focusing on libraries essential for data science and analytics, such as NumPy, Pandas, and Matplotlib.
2. Understand ML and DL Principles: Gain a comprehensive understanding of machine learning and deep learning algorithms, including their mathematical foundations and practical applications.
3. Implement Agentic AI Systems: Learn to design and deploy agentic AI systems capable of autonomous decision-making and task execution, enhancing automation in data science workflows.
4. Integrate LLMs via APIs: Acquire skills to integrate Large Language Models into applications using APIs, enabling advanced natural language processing capabilities.
5. Deploy On-Premises LLMs: Understand the process of installing and configuring LLMs on local infrastructure, selecting appropriate models from repositories like Hugging Face to meet specific organizational needs.
6. Apply ML and DL Techniques: Learn to select and apply appropriate machine learning and deep learning algorithms for various data types and predictive modeling challenges, including regression, classification, and clustering.
7. Evaluate and Optimize Models: Implement best practices for model evaluation, including cross-validation, performance metrics, and strategies to address overfitting and underfitting.
8. Execute End-to-End Projects: Gain experience in managing complete data science projects, from data collection and preprocessing to model training, evaluation, and deployment.
9. Explore Advanced AI Topics: Delve into cutting-edge areas such as transfer learning, generative adversarial networks (GANs), and reinforcement learning to stay abreast of industry advancements.
10. Develop Problem-Solving Skills: Enhance critical thinking and problem-solving abilities applicable to real-world data science and AI challenges, preparing for research or industry projects.

Prerequisites

To ensure a productive learning experience, participants must meet the following prerequisites:

• Proficiency in Python: Strong command of Python programming, including familiarity with syntax, control structures, data types, and functions. Experience with data science libraries such as NumPy and Pandas is essential.
• Mathematical Background: Solid understanding of algebra, calculus, statistics, and probability to interpret data and model outcomes effectively.
• Basic ML Knowledge: Familiarity with fundamental machine learning concepts and terminology, including supervised and unsupervised learning, overfitting, and model evaluation metrics.
• Software and Tools: Comfort with Python development environments (e.g., Jupyter Notebooks, PyCharm) and package management tools (conda, pip). Basic experience with version control systems like Git is advantageous.
• Analytical Skills: Ability to think critically about problems and data, with experience in data manipulation and visualization tools.
• Curiosity and Experimentation: An eagerness to learn new concepts, experiment with different algorithms, and apply creative solutions to problems in the rapidly evolving fields of data science and AI.

Course Outline

Python for Data Science and Analytics

• Introduction to Data Science with Python: Overview of data science principles and the role of Python in data analysis.
• Data Manipulation with Pandas: Techniques for data cleaning, transformation, and analysis using Pandas.
• Data Visualization with Matplotlib and Seaborn: Creating insightful visualizations to interpret data effectively.
• Hands-on Practice: Applied exercises with real-world datasets.

Machine Learning and Deep Learning

• Supervised Learning Algorithms: Implementing regression and classification models using Scikit-learn.
• Unsupervised Learning Techniques: Exploring clustering and dimensionality reduction methods.
• Model Evaluation and Validation: Assessing model performance and applying cross-validation techniques.
• Neural Network Architectures: Understanding the structure and function of neural networks.
• Building and Training Models: Designing and training deep learning models with TensorFlow and Keras.
• Convolutional Neural Networks (CNNs): Applying CNNs to image processing tasks.
• Recurrent Neural Networks (RNNs) and LSTM: Utilizing RNNs and LSTM for sequence modeling and natural language processing.

Day 3: Agentic AI and Language Models

• Introduction to Agentic AI: Exploring the concept of AI agents capable of autonomous decision-making and task execution.
• Designing Agentic Systems: Principles and frameworks for developing agentic AI systems.
• Implementing Autonomous Agents: Hands-on experience in building and deploying agentic AI applications.
• Understanding LLMs: Overview of Large Language Models and their applications in natural language processing.
• API Integration of LLMs: Techniques for integrating LLMs into applications using APIs.
• On-Premises LLM Deployment: Procedures for installing and configuring LLMs on local infrastructure, selecting models from Hugging Face.
• Advanced Topics: Transfer learning, GANs, and reinforcement learning basics.

Final Project

Participants will apply their newly acquired knowledge to complete a capstone project that integrates Python, machine learning, and LLM technologies to solve a real-world problem. Projects will be presented and discussed at the conclusion of the course.