Python for AI Projects: From Data Exploration to Impact

Course details

Experienced data analysts can drastically improve their competitiveness in the current market with some hands-on AI and machine learning training. In this course, Danny Ma—CEO and founder at Sydney Data Science and popular data science influencer—offers you an easygoing, practical guide to developing AI and machine learning algorithms using a data-driven approach. This course is specifically created for practitioners with a strong understanding of data analysis. Using simple Python code, you will learn how to utilize robust machine learning algorithms on familiar types of business data. This course enables you to bridge the gap between theory and actual use as you effectively apply your new skills on the job.

Learning objectives
Learn the basics of modern NLP, including the most common machine learning and AI algorithms.
Investigate three AI use cases, including natural language processing, customer recommendations, and website optimization.
Explore the pros and cons of some popular Python machine learning frameworks.
Learn about the Python computing environment for this course.
Build language models using website text data.
Explore traditional tabular machine learning techniques for supervised learning.
Implement unique data transformations for unsupervised learning problems.
Assess the performance of AI models beyond simple metrics and how this affects the bottom-line business outcomes.

Concepts

Introduction

Introduction

Introduction to AI

AI use cases
AI and machine learning (ML) frameworks
Python environment

Python Challenge 1 - Natural Language Processing (NLP)

Data exploration
Training data pipeline
Turning Raw Text into Business Insights with Python and NLP
Model fitting
Building Smarter Search - From Keywords to Semantic AI
Model metrics
From Search to Answers - Building AI Knowledge Solutions

Python Challenge 2 - Customer Recommendations

Data exploration
Preparing Customer Data for Predictions for Machine Learning
Training data pipeline
Building Classification Pipelines in Python
Model fitting
Model metrics
Training Purchase Prediction Models

Python Challenge 3 - GenAI

Data exploration
Setting up your Coding Environment
Setting up LLMs
Deploy AI Web Apps using Streamlit
Run an AI Chatbot from Explore California Dataset
Improving GenAI performance
Bringing It All Together - Improving your Chatbot with ML

Conclusion

Next steps