Python for Data Science Quick Refresher

1. Review and application of Python basics

2. Relevance of Python in Data Science

3. Exploring Python data science libraries: Pandas, NumPy, Matplotlib

4. Introduction to Jupyter Notebook, Anaconda

5. Lab: Solving basic data science problems using Python

Introduction to AI and Machine Learning

1. Understanding the foundations and significance of AI and Machine Learning

2. Differentiating between AI, Machine Learning, and Deep Learning

3. Overview of the business applications of AI and Machine Learning

4. Exploring types of Machine Learning: Supervised, Unsupervised, Reinforcement

5. Deep dive into common Machine Learning algorithms

6. Introduction to TensorFlow and PyTorch

7. Lab: Exploring Python libraries for Machine Learning

Supervised Learning: Regression and Classification

1. Understanding Simple Linear, Multiple Regression, and Binary Classification

2. Understanding the business context in Binary Classification

3. Lab: Conducting Regression Analysis and Classification using Python

Unsupervised Learning: Introduction to Clustering

1. Understanding the concept of Clustering in Unsupervised Learning

2. Diving deep into k-means clustering algorithm

3. Lab: Implementing k-means Clustering

Data Wrangling and Preprocessing Techniques

1. Understanding the importance of data wrangling and preprocessing in Machine Learning

2. Techniques for handling missing data, outliers, and categorical data

3. Feature scaling and normalization techniques

4. Lab: Applying data preprocessing techniques on a dataset

Practical Machine Learning Project Walkthrough

1. Gaining insights into the lifecycle of AI projects in the industry

2. Common challenges in implementing AI projects and solutions

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

4. Lab: Implementing a small-scale machine learning project

Model Evaluation and Validation

1. Understanding model assessment metrics for both Regression and Classification

2. Learning to split data for model training and testing

3. Lab: Evaluating model performance on test data

Introduction to Ensemble Learning

1. Learning the concept of Ensemble Learning and its importance

2. Understanding simple methods for Ensemble Learning

3. Lab: Implementing simple Ensemble Learning techniques

Explainable AI and Ethical Considerations in AI

1. Understanding the importance of interpretability in Machine Learning

2. Exploring techniques for making AI transparent

3. Discussing ethical considerations in AI and ML

4. Lab: Visualizing Feature Importance in a model

Introduction to Neural Networks

1. Grasping the basics of Neural Networks

2. Learning about Feedforward and Backpropagation processes

3. Lab: Building a basic Neural Network with Python

Data Visualization Techniques with Python

1. Understanding the importance of data visualization in Machine Learning

2. Exploring Python libraries for data visualization: Matplotlib, Seaborn

3. Lab: Visualizing datasets using various plots

Machine Learning Pipeline and Model Deployment

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

2. Lab: Creating a simple Machine Learning pipeline

Bonus Chapters / Time Permitting

Bonus Chapter: Exploring Generative AI with GPT-4

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

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

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

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

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

Bonus Chapter: Basics of Integrating AI into Applications

1. Understand the concept of AI integration into simple applications

2. Learn about the role of APIs in leveraging AI capabilities in applications

3. Explore how Python can be used to connect applications to AI functionalities

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

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

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

Bonus Chapter: Integrating AI into Web Applications

1. Understand the concept of AI integration into web applications

2. Learn about the Flask and Django frameworks for Python web development

3. Discuss the role of APIs in leveraging AI capabilities in web applications

4. Explore various AI plugins and extensions for web development

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