Machine Learning Course for Beginners (Part 1) | Complete Fundamentals & Regression Tutorial

🚀 Start your complete Machine Learning journey here! This full masterclass from “Gen AI Cafe” is your one-stop guide to go from absolute beginner to building your first practical AI models. This video covers all the core fundamentals you’ll need for the rest of our 5-part series.
This is a 100% hands-on and demo-intensive course designed to give you practical skills without getting lost in abstract theory. We focus on what truly matters to get you started in Data Science and Artificial Intelligence.

Who is this course for?
This video is perfect for students, aspiring data scientists, developers looking to add AI skills, and any professional curious about how machine learning actually works. No prior experience is required!

In this fundamentals masterclass, you will learn:
✅ The Core Concepts: A clear introduction to AI, Machine Learning (ML), and Deep Learning (DL).
✅ Your First ML Model: A complete, hands-on walkthrough of building a Regression model from scratch.
✅ Setting Up Your Environment: A step-by-step guide to setting up your workspace with Jupyter Notebooks.
✅ Exploratory Data Analysis (EDA): Learn the essential first step in any ML project.
✅ The Full Machine Learning Pipeline: Understand the end-to-end process, from train/test split to making predictions.
✅ Avoiding Common Pitfalls: A practical explanation of Overfitting and Underfitting.
✅ Improving Your Model’s Accuracy: Learn essential techniques like Hyperparameter Tuning and Cross-Validation.

⏱️ TIMESTAMPS / CHAPTERS:

0:00 – Introduction: Foundations of AI, ML & DL
2:10 – Part 1: Rule-Based AI Explained
3:00 – Example: How an Air Conditioner Uses Rule-Based AI
6:45 – Example: Rule-Based Systems in Mobile Phones & Laptops
9:37 – Example: Rule-Based AI in Modern Cars & Offices
14:45 – Part 2: The Limitations of Rule-Based AI
16:18 – Why It’s Impossible to Write Rules for Image Recognition (Elephant Example)
20:14 – Part 3: Introduction to Machine Learning
24:02 – How Machine Learning Works: Learning from Data
26:21 – The Core of ML: Training, Fitting, and Predicting
30:10 – The Human Analogy: How a Child Learns
34:32 – When Machine Learning Fails: The Google Gorillas Case Study
44:09 – Part 4: The Different Types of Machine Learning
45:10 – What is Deep Learning?
48:18 – Supervised vs. Unsupervised Learning
51:02 – Regression vs. Classification Explained
1:00:05 – Part 5: A Deep Dive into Linear Regression
1:01:28 – Understanding the Straight Line Equation (y = mx + c)
1:07:01 – Visualizing Regression: Finding the “Line of Best Fit”
1:17:34 – Multiple Linear Regression: Using Multiple Features
1:20:21 – How to Interpret Regression Model Coefficients
1:25:26 – Part 6: Practical Case Study – Advertising Sales Prediction
1:28:30 – Introduction to the Jupyter Notebook Environment
1:46:09 – Understanding Correlation with a Heatmap
2:11:56 – Building the First Machine Learning Model in Scikit-Learn
2:13:41 – The Importance of Train-Test Split
2:28:38 – Experimenting with Different Algorithms (Gradient Boosting & Random Forest)
2:50:26 – Understanding Overfitting and Underfitting
3:00:02 – Hyperparameter Tuning Explained (n_estimators)
3:04:05 – What is Cross-Validation and Why is it Important?
3:12:30 – Using Pipelines for Cleaner Workflows

👋 I’m Sayan Dey, your host at Gen AI Cafe. With over 15 years of experience in enterprise AI and building LLM systems, I’m excited to guide you through the fascinating world of AI and Machine Learning.
🔔 Subscribe to Gen AI Cafe for more clear & practical AI tutorials in this series
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💬 Have questions or topics you’d like to see covered? Share them in the COMMENTS below!

🔗 Support Gen AI Cafe on Patreon: https://www.patreon.com/sayandey
🔗 Connect with Sayan/Gen AI Cafe on LinkedIn: https://www.linkedin.com/in/sayandey01/

➡️ Next Video: https://www.youtube.com/watch?v=jcUhKedqXY8&list=PLVaZwYq0P8s3KhrqdvnelWcT4JnXCNBbp&index=2
▶️ Full “Machine Learning and Deep Learning” Playlist: https://www.youtube.com/playlist?list=PLVaZwYq0P8s3KhrqdvnelWcT4JnXCNBbp

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