Crash Course: Python & Machine Learning with Jupyter for Practical Problem Solving

Categories: AI & IT
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About Course

Step into the world of data, AI, and problem-solving with this immersive crash course designed for future innovators! Whether you’re a student, a professional, or just curious about machine learning, this course empowers you to harness the full power of Python and Jupyter Notebooks to solve real-world challenges.

Through interactive lessons, mini-projects, and hands-on labs, you’ll explore everything from data analysis with Pandas to building predictive models with Scikit-learn—all within a modern, visual, and beginner-friendly environment. With a focus on practical application over theory, you’ll walk away with skills you can immediately apply in fields like business, science, and social analytics.

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What Will You Learn?

  • Set up Python and Jupyter using Anaconda
  • Write and run Python code in Jupyter notebooks
  • Use Numpy and Pandas for data analysis and manipulation
  • Visualize data with Matplotlib and Plotly
  • Apply machine learning using Scikit-learn
  • Evaluate ML models using real-world datasets
  • Tackle real-world projects like spam detection and COVID data analysis
  • Build and submit end-to-end ML reports using notebooks
  • Deploy ML models with tools like Streamlit or Gradio
  • Understand deep learning basics with TensorFlow and Keras

Course Content

Module 1: Getting Started with Python & Jupyter
This foundational module walks students through setting up the Anaconda distribution and launching Jupyter Notebooks, the primary interface for hands-on Python coding. Learners will get familiar with Python basics—variables, loops, data types—and understand how to effectively document and present work using Markdown and Code cells. It’s the ideal launchpad for those new to programming or data science.

  • 📘 Lesson 1.1: Installing Anaconda & Setting Up Jupyter Notebooks
  • 📘 Lesson 1.2: Python Basics – Variables, Data Types, Loops, and Functions
  • 📘 Lesson 1.3: Writing and Running Code in Cells (Markdown vs Code)
  • 📘 Lesson 1.4: Using Notebooks for Documentation and Reports

Module 2: Python for Data Science
In this module, students dive into the data science workflow, exploring numerical computing with NumPy and data manipulation with Pandas. They’ll also learn to create powerful visualizations using Matplotlib and Plotly to draw insights from data. The module concludes with a hands-on mini project where students analyze and visualize datasets like COVID-19 or climate data.

Module 3: Intro to Machine Learning
Students are introduced to the core concepts of machine learning, including supervised vs. unsupervised learning. Using the Scikit-learn library, they’ll build simple classification and regression models, and evaluate their performance with tools like confusion matrices and cross-validation. A mini project reinforces learning by applying ML to practical cases such as predicting student performance or housing prices.

Module 4: Solving Real Problems with ML
This module emphasizes using ML in real-world contexts, teaching students how to engineer features for better model performance and apply ML solutions to sectors like healthcare, business, and security. Students work on mini-projects like spam detection or fraud analysis and learn how to interpret and communicate their model results effectively through visual tools.

Module 6: Capstone Project & Certification
In this culminating module, students apply everything they’ve learned in a capstone project of their choice—whether analyzing social trends, predicting outcomes, or solving a community problem. They'll build a comprehensive notebook report, present their findings, and earn certification, signaling their readiness for internships, hackathons, or AI project teams.

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