Machine Learning with Python : Hands-On Free Course 🙋

Welcome to the “Machine Learning with Python” free course! This course is designed for anyone interested in learning machine learning concepts from scratch, starting with basic Python programming. Whether you’re student or working professional, or have a curious mind, this course is for you. We’ll take you through simple and practical lessons to help you get started on your journey to become a future machine learning expert!

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Audience

This course is ideal for:

• Any interested students and above.
• Beginners who are new to machine learning and Python programming.
• Anyone interested in applying machine learning to real-world problems in an easy-to-understand manner.

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Prerequisite

• Basic understanding of mathematics (simple algebra).
• Basic programming or machine learning experience is required.
• If you’ve worked with Python before, it will help, but don’t worry if you haven’t!
• If you have not completed python basics course, data analysis course, statistics course you can find these courses on same platform, we recommend to complete those before this course.

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Benefits

By taking this course, you will:

• Understand the Basics of Python programming.
• Learn what Machine Learning is and how it’s applied in real-world situations.
• Build your own machine learning models.
• Gain hands-on experience by working on fun and practical projects.
• Prepare for future advanced topics in data science, artificial intelligence, and more.

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What You’ll Gain from This Course

• Core Concepts of Machine Learning: Learn the fundamentals like supervised and unsupervised learning.
• Python for Machine Learning: Write basic Python programs and use libraries like Pandas, NumPy, and Scikit-Learn.
• Practical Problem Solving: Build machine learning projects that help you understand concepts better.
• Real-World Applications: Understand how machine learning is used in various industries like healthcare, education, finance, and entertainment.

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Job Roles

After gaining skills in machine learning, learners can aim not only for following but you may have more than this also:

• Machine Learning Engineer: Develop and implement machine learning models.
• Deep Learning Engineer: Develop and implement deep learning models.
• AI/ML Researcher: Explore cutting-edge advancements in artificial intelligence.
• Data Scientist: Solve complex data problems using machine learning and AI techniques.

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Sessions Outline

1. Python Programming : Hands-On Free Course 🙋
2. Data Analysis with Python : Hands-On Free Course 🙋
3. Statistics for Data Science : Hands-On Free Course 🙋
4. How Data and AI Are Going to Change the World
5. Databases, Data Warehouses, and Data Lakes: Understanding the Differences
6. Introduction of Machine Learning : How it is Changing Our World
7. Types of Machine Learning: Easily Explained !!
8. Mastering Supervised Learning: A Simple Guide to Regression & Classification
9. The Ultimate Guide to Regression : Methods, Examples, and Applications
10. Simple Linear Regression: Build a ML Model to Predict Electricity Consumption
11. How to Choose the Right Evaluation Metric for Regression ML Models
12. Mastering Multiple Linear Regression: Car Price Prediction Project
13. Understanding Classification in ML: Types, Applications, and Key Algorithms
14. Evaluating Classification Models: Choose Right Metric (Accuracy to AUC)
15. Ensemble Methods : Bagging, Boosting, Stacking and Voting
16. How Random Forest works internally: A Step-by-Step Guide with Examples
17. A Deep Dive into AdaBoost: Step-by-Step Heart Disease Classification Project
18. Stacking ML Models explained easily : Step-by-Step House Price Prediction Project
19. Voting Ensemble Method : Bank Loan Approval Prediction Project
20. Predicting Customer Churn Using Machine Learning
21. What is Clustering? Implement for your business use case?
22. Understanding Data Mining Systems and Association Rule Mining with the Apriori Algorithm
23. Essential Projects for Aspiring Machine Learning Engineers

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What’s Next?

After completing this course, you’ll have a strong foundation in Python and machine learning. The next steps could include:

• Exploring Advanced Machine Learning Topics like deep learning, neural networks, and natural language processing.
• Contributing to Open-Source Projects in machine learning.
• Enrolling in more advanced courses or specializations in AI and data science.

Join us on this learning journey to solve various industries problems with machine learning! 🤩