Machine Learning Essentials with Python

Outline

1. Python for Data Science Quick Refresher

· Review and application of Python basics

· Relevance of Python in Data Science

· Exploring Python data science libraries: Pandas, NumPy, Matplotlib

· Introduction to Jupyter Notebook, Anaconda

· Lab: Solving basic data science problems using Python

2. Introduction to AI and Machine Learning

· Understanding the foundations and significance of AI and Machine Learning

· Differentiating between AI, Machine Learning, and Deep Learning

· Overview of the business applications of AI and Machine Learning

· Exploring types of Machine Learning: Supervised, Unsupervised, Reinforcement

· Deep dive into common Machine Learning algorithms • Introduction to TensorFlow and PyTorch

· Lab: Exploring Python libraries for Machine Learning

3. Supervised Learning: Regression and Classification

· Understanding Simple Linear, Multiple Regression, and Binary Classification

· Understanding the business context in Binary Classification

· Lab: Conducting Regression Analysis and Classification using Python

4. Unsupervised Learning: Introduction to Clustering

· Understanding the concept of Clustering in Unsupervised Learning

· Diving deep into k-means clustering algorithm

· Lab: Implementing k-means Clustering

5. Data Wrangling and Preprocessing Techniques

· Understanding the importance of data wrangling and preprocessing in Machine Learning

· Techniques for handling missing data, outliers, and categorical data

· Feature scaling and normalization techniques

· Lab: Applying data preprocessing techniques on a dataset

6. Practical Machine Learning Project Walkthrough

· Gaining insights into the lifecycle of AI projects in the industry

· Common challenges in implementing AI projects and solutions

· Step-by-step walkthrough of a real-life AI project from end-to-end

· Lab: Implementing a small-scale machine learning project

7. Model Evaluation and Validation

· Understanding model assessment metrics for both Regression and Classification

· Learning to split data for model training and testing

· Lab: Evaluating model performance on test data

8. Introduction to Ensemble Learning

· Learning the concept of Ensemble Learning and its importance

· Understanding simple methods for Ensemble Learning

· Lab: Implementing simple Ensemble Learning techniques

9. Explainable AI and Ethical Considerations in AI

· Understanding the importance of interpretability in Machine Learning

· Exploring techniques for making AI transparent

· Discussing ethical considerations in AI and ML

Lab: Visualizing Feature Importance in a model

10. Introduction to Neural Networks

· Grasping the basics of Neural Networks

· Learning about Feedforward and Backpropagation processes

· Lab: Building a basic Neural Network with Python

11. Data Visualization Techniques with Python

· Understanding the importance of data visualization in Machine Learning

· Exploring Python libraries for data visualization: Matplotlib, Seaborn

· Lab: Visualizing datasets using various plots

12. Machine Learning Pipeline and Model Deployment

· Understanding the concept of ML pipeline: Data collection, Preprocessing, Modeling, Evaluation, Deployment

· Lab: Creating a simple Machine Learning pipeline

Bonus Chapters / Time Permitting (or Day Four)

Bonus Chapter: Exploring Generative AI with GPT-4

· Understand Generative AI and how it powers GPT-4, using Python for interacting with these models

· Learn about the evolution of GPT models, and the specific advancements of GPT-4 in handling complex Python programming tasks

· Understand the potential applications of GPT-4 and how to implement them using Python

· Discuss the ethical considerations and Python coding practices for using powerful models like GPT-4 responsibly

· Lab: Creating a conversational bot using GPT-4 with Python

Bonus Chapter: Basics of Integrating AI into Applications

· Understand the concept of AI integration into simple applications

· Learn about the role of APIs in leveraging AI capabilities in applications

· Explore how Python can be used to connect applications to AI functionalities

· Discuss various simple AI plugins and extensions that can be integrated using Python

· Lab: Building a basic application integrating a pre-trained AI model

· Lab: Integrating a GPT-4 powered feature into a basic Python application

Bonus Chapter: Integrating AI into Web Applications

· Understand the concept of AI integration into web applications

· Learn about the Flask and Django frameworks for Python web development

· Discuss the role of APIs in leveraging AI capabilities in web applications

· Explore various AI plugins and extensions for web development

· Lab: Integrating a GPT-4 powered chatbot into a web application