This Machine Learning course is designed for students and professionals who want to build practical ML skills from scratch. You will learn how to prepare data, build ML models, evaluate results, and deploy solutions using Python. This course prepares you for real-world roles like ML Engineer, Data Scientist, and AI Analyst.
What is ML? Types of ML
AI vs ML vs Deep Learning
Real-world ML applications
ML workflow understanding
NumPy for numerical operations
Pandas for data handling
Matplotlib & Seaborn for data visualization
Handling missing values
Outlier detection & treatment
Encoding categorical variables
Feature scaling (Normalization & Standardization)
Train-test split
Feature engineering
Linear Regression
Multiple Linear Regression
Polynomial Regression
Evaluation metrics (MAE, MSE, RMSE, R² Score)
Logistic Regression
K-Nearest Neighbors
Decision Trees
Random Forest
Naive Bayes
Support Vector Machine (SVM)
Evaluation metrics (Confusion Matrix, Accuracy, Precision, Recall, F1 Score)
K-Means
Hierarchical Clustering
DBSCAN
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