Machine Learning and AI with Python
(C) Copyright Elephant Scale
March 3, 2025
-
Machine Learning (ML) is changing the world. To use ML effectively, one needs to understand the algorithms and how to utilize them. This course provides an introduction into the most popular machine learning algorithms.
-
AI opens ways to building smart applications as never before. However, many use cases require implementing AI in a secure, responsible manner, including but not limited to:
- Not sending your data to third-party online AI services
- Keeping control over the data used for training
- Controlling actions taken by AI
- Python
- ML Concepts
- Regressions (Linear Regression, Logistic Regression
- Classification, clustering
- Prepare your data and store it in the semantic search databases
- Rules of sending questions to AI
- Secure AI implementations using local models or networked local copy of the model
- Best practices for cloud architecture
Data analysts, Software Engineers, Data scientists
Three days
Beginner to Intermediate
We will study and solve some of most common industry use cases; listed below
- Programming background
- Familiarity with Python would be a plus, but not required
- No machine learning or AI knowledge is assumed
Cloud based lab environment will be provided to students, no need to install anything on the laptop
- A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
- Chrome browser
- Introduction to Python
- Labs
- Working with Jupyter notebooks
- Numpy and Pandas
- Machine Learning landscape
- Understanding Deep Learning use cases
- Understanding AI / Machine Learning / Deep Learning
- Data and AI
- AI vocabulary
- Hardware and software ecosystem
- Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
- Linear Regression
- Labs:
- Use case: House price estimates
- Use case: logistic regression
- Introduction to AI
- Iterative development
- How to iteratively analyze and refine your prompts to generate marketing copy from a product fact sheet.
- Summarizing
- How to make an AI summarize a document with different requirements and in different formats
- Inferring
- How to make an AI infer sentiment and topics from product reviews and news articles.
- Transforming
- How to use Large Language Models for text transformation tasks such as language translation, spelling and grammar checking, tone adjustment, and format conversion.
- Expanding
- How to generate customer service emails that are tailored to each customer’s review.
- Chatbot
- How to use an AI to have extended conversations with chatbots personalized or specialized for specific tasks or behaviors.
- Labs: Prompt-Engineering
- Organize your private documents for the implementation and break them into meaningful fragments for storing in the semantic search engine
- Semantic search
- Retrieval Augmented Generation (RAG)
- Recommender systems
- Hybrid search
- Facial similarity search
- Anomaly detection
- Lab: LLM with Semantic Search
- Models, prompts, and parsers
- Memory
- Chains
- Q&A
- Evaluation
- Conversational bot
- Lab: Langchain
- Lab: Functions-Tool-Agents-Langchain
- Overview of Amazon, Azure, and Google clouds
- Evaluating and debugging Generative AI
- Practical examples and demos
- How to make sure your data stays private and under your control
- Preparing reports for executives
- Lab: Build-database-agents
- Customer service implementation with AI
- Lab: Serverless-LLM-Bedrock
- Lab: Agentic-on-Bedrock