Machine Learning - Beginner Course

Machine Learning for Beginners1

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1. 1Machine Learning Course for Absolute Beginners

Module 1: Introduction to Machine Learning3

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1. 1What is Machine Learning and How it Works (Beginner Guide)
2. 2Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
3. 3Real-Life Examples of Machine Learning: Recommendation Systems, Spam Filters, Chatbots

Module 3: Data Preprocessing5

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1. 1Machine Learning – Loading Datasets in Python (CSV, Excel, Built-in, and URLs)
2. 2Handling Missing Values and Outliers in Machine Learning
3. 3Encoding Categorical Data in Machine Learning (Beginner-Friendly Guide)
4. 4Machine Learning - Feature Scaling and Normalization
5. 5Train-Test Split in Machine Learning (With Examples and Python Code)

Module 4: Supervised Learning5

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1. 1Machine Learning - Linear Regression for Beginners
2. 2Machine Learning - Logistic Regression
3. 3Machine Learning - K-Nearest Neighbors (KNN)
4. 4Machine Learning - Decision Trees and Random Forest
5. 5Model Evaluation Metrics in Machine Learning (with Examples & Python Code)

Module 5: Unsupervised Learning3

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1. 1K-Means Clustering in Machine Learning: Explained with Examples and Python Code
2. 2Hierarchical Clustering in Machine Learning
3. 3Dimensionality Reduction with PCA in Machine Learning

Module 6: Improving Models4

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1. 1Cross-Validation Techniques in Machine Learning (With Examples)
2. 2Hyperparameter Tuning in Machine Learning
3. 3Understanding Overfitting vs Underfitting in Machine Learning
4. 4Bias-Variance Tradeoff in Machine Learning (with Python Examples)

Module 7: Real-World Projects4

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1. 1Titanic Survival Prediction Using Machine Learning
2. 2House Price Prediction Using Machine Learning (Step-by-Step Tutorial for Beginners)
3. 3Customer Segmentation Using Clustering - Machine Learning Project for Beginners
4. 4Email Spam Detection using Machine Learning