Machine Learning course - Python, Jupyter, Docker!

In this section of the course, we will build a machine learning model to predict the type of car a person might prefer based on their age and gender. This project will provide practical experience in applying machine learning techniques to a real-world scenario using Python.

What You'll Learn:

- Data Collection and Preprocessing: We’ll start by gathering a dataset that includes age, gender, and car preferences. You will learn how to clean and preprocess this data using libraries like Pandas to handle missing values, encode categorical data (such as gender), and normalize numerical values (like age).

- Exploratory Data Analysis (EDA): We will explore the dataset to uncover trends and relationships between age, gender, and car preferences. Visualizations using libraries like Matplotlib and Seaborn will help us understand the data and guide our modeling decisions.

- Model Selection: Based on our data, we'll experiment with various machine learning algorithms such as Decision Trees, Logistic Regression, or K-Nearest Neighbors (KNN) to predict car preferences. You will learn how to select the best model by comparing their performance using metrics like accuracy, precision, and recall.

- Model Training and Evaluation: You will split the dataset into training and testing sets to build the model, ensuring it generalizes well to new data. We'll train the model using Scikit-Learn and evaluate its accuracy, adjusting hyperparameters to optimize performance.

- Model Interpretation: After training the model, we’ll analyze its predictions and discuss how age and gender influence the output. This section will give insights into how the model makes decisions, highlighting the importance of interpretability in machine learning.

By the end of this module, you'll have hands-on experience building a machine learning model to make predictions based on demographic features. This project will enhance your understanding of the machine learning workflow, from data preprocessing to model evaluation.

In this class, we will take the machine learning model you previously built and integrate it into a full-fledged web application. You’ll learn how to expose your model via an API, allowing other applications or users to send requests and get predictions. This session will cover essential concepts for deploying machine learning models in real-world applications and making them accessible over the web.

What You'll Learn:

- Building a Web Application: Using the popular Flask framework in Python, we'll create a simple yet powerful web application. You'll learn how to set up a basic Flask app, handle routing, and integrate HTML templates to create a user-friendly interface.

- Exposing the Machine Learning Model via an API: We’ll demonstrate how to expose your trained machine learning model as a REST API endpoint. This involves defining an API route in Flask that takes input data (age and gender), processes it, and returns predictions from your model in real-time.

- Sending Requests to the API: You will learn how to send HTTP requests to your API using Python’s `requests` library. This will allow users or other applications to input data (age and gender) and receive car preference predictions from the model. We’ll cover both GET and POST requests to ensure you have a complete understanding of how data can be passed to your API.

- Testing and Debugging: Before deploying your application, you’ll test the API locally, ensuring it correctly processes requests and returns accurate predictions. We’ll also cover debugging techniques to troubleshoot any issues that may arise.

- Packaging with Docker: Finally, we’ll package the web application and model into a Docker container, making it easy to deploy on any platform. This step ensures that your application, with all its dependencies, runs consistently in different environments.

By the end of this class, you will have developed a functional web application that can make predictions using your machine learning model via an API. This skill is essential for deploying machine learning models in production environments and allowing users to interact with your model in real-time.