Kickstart with Python for Data Science

1. Refresh core Python knowledge

2. Explore Python's role in data science

3. Discover Python libraries: Pandas, NumPy, Matplotlib

4. Introduction to Jupyter Notebook and Anaconda

5. Lab: Approach basic data science issues with Python

Foundations of AI and Machine Learning

1. Learn core AI and machine learning principles

2. Identify differences among AI, ML, and DL

3. Business impacts of AI and ML

4. Study machine learning types: Supervised, Unsupervised, Reinforcement

5. Analyze common machine learning algorithms

6. Introduction to TensorFlow and PyTorch

7. Lab: Use Python libraries for machine learning tasks

Insights into Supervised Learning

1. Learn Simple Linear and Multiple Regression

2. Understand Binary Classification

3. Relate Binary Classification to business needs

4. Lab: Execute Regression and Classification analysis

The World of Unsupervised Learning

1. Conceptualize clustering in unsupervised learning

2. Examine k-means clustering

3. Lab: Apply k-means clustering techniques

Honing Data Wrangling Skills

1. Importance of data wrangling in machine learning

2. Techniques to manage missing data, outliers

3. Learn feature scaling and normalization

4. Lab: Employ data preprocessing strategies

Walkthrough: A Practical Machine Learning Project

1. Analyze AI project lifecycle

2. Address AI project challenges

3. Guided project from start to finish

4. Lab: Create a miniature machine learning project

Mastering Model Evaluation

1. Learn various model assessment metrics

2. Techniques for splitting data for testing

3. Lab: Test model accuracy using evaluation metrics

Harnessing Ensemble Learning

1. Understand Ensemble Learning significance

2. Explore simple Ensemble Learning methods

3. Lab: Use basic Ensemble Learning strategies

Navigating Explainable AI and Ethics

1. Discover the need for AI interpretability

2. Techniques to promote AI transparency

3. Ethical considerations in AI

4. Lab: Visualize model feature importance

Unveiling Neural Networks

1. Basics of Neural Networks

2. Feedforward and Backpropagation understanding

3. Lab: Create simple Neural Network with Python

Art of Data Visualization

1. The role of data visualization in ML

2. Familiarize with data visualization libraries

3. Lab: Visualize datasets using Python

Building a Machine Learning Pipeline

1. Concepts of ML pipeline: Data collection to Deployment

2. Lab: Develop a simple machine learning pipeline

Bonus: Tapping into Generative AI with GPT-4

1. Comprehend Generative AI's impact on GPT-4

2. Call out GPT technology advances

3. Consider ethical AI and coding practices

4. Lab: Construct a chatbot using GPT-4

Bonus: AI's Role in Applications

1. Integration of AI in applications overview

2. Importance of APIs for leveraging AI features

3. Lab: Develop an AI-integrated app

Bonus: Web Integration of AI

1. Outline AI use in web applications

2. Utilize Flask and Django for web development

3. Lab: Integrate a GPT-4 chatbot into a web app