Machine Learning Essentials with Python

At Course Completion

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll learn how to:

· Master the Python Programming for Data Science: Gain an in-depth understanding of Python's role in data science and AI, including proficiency in using key Python data science libraries like Pandas, NumPy, and Matplotlib.

· Understand the Fundamentals of AI and Machine Learning: Develop a strong grasp of AI and Machine Learning concepts, their applications, and how to differentiate between AI, Machine Learning, and Deep Learning.

· Dive into Supervised and Unsupervised Learning Techniques: Acquire hands-on skills to conduct Regression Analysis, Binary Classification, and k-means Clustering - key methods in Supervised and Unsupervised Learning.

· Apply Data Wrangling and Preprocessing Techniques: Learn to handle missing data, outliers, and categorical data effectively and perform feature scaling and normalization - crucial steps in Machine Learning projects.

· Create and Evaluate Machine Learning Models: Get a grip on the lifecycle of AI projects, including model creation, evaluation, validation, and the application of Ensemble Learning techniques.

· Understand and implement crucial data preprocessing techniques in Python: Attendees will acquire the ability to handle missing data, outliers, and categorical data, essential for creating reliable machine learning models.

· Develop competency in creating and interpreting data visualizations: Students will learn how to leverage Python's powerful libraries such as Matplotlib and Seaborn to create compelling visualizations and extract meaningful insights from data.

· Construct a machine learning pipeline for real-world applications: Participants will gain the practical know-how to carry a machine learning project from initial data collection through to final model deployment, using Python.

· (Optional / Bonus Topics): Implement AI into Real-World Applications: By the end of the course, you'll be able to build applications that integrate AI functionalities, using popular Python frameworks and modern AI technologies, like GPT-4.

Audience Profile

This course is ideally suited for Python developers, data analysts, and aspiring data scientists looking to expand their skills into AI and Machine Learning. It is also highly beneficial for product managers and business leaders aiming to acquire a hands-on understanding of AI's impact on product development and business strategy.

Prerequisites

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

· Basic Understanding of Python as well as familiarity with Python Libraries (Pandas and Numpy, etc.)

· Basic Math and Problem-Solving Skills

· Understanding of Basic Data Structures

Take Before: Students should have practical skills equivalent to or should have attended the following course(s) as a pre-requisite:

· TTPS4873: Fast Track to Python Programming for Data Science (3 days)

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