Machine Learning Fundamentals: From Theory to Real-World Models

Step into the world of Machine Learning — where data meets intelligence. This course provides a solid foundation in ML concepts, algorithms, and practical tools used to build smart, predictive systems. You’ll learn: Core ML concepts: supervised vs. unsupervised learning, overfitting, bias-variance tradeoff Essential algorithms: linear & logistic regression, decision trees, k-NN, SVM, and clustering (K-Means) Model evaluation: accuracy, precision, recall, F1 score, ROC curves Hands-on with Python libraries: scikit-learn, NumPy, pandas, Matplotlib, and Seaborn Feature engineering, data preprocessing, and pipeline building Intro to neural networks and deep learning with TensorFlow or PyTorch Real-world projects in domains like healthcare, finance, and marketing Whether you're a beginner or looking to level up your data science skills, this course will teach you how to build, train, and deploy effective ML models. Key Features: Hands-on coding with real datasets (CSV, APIs, open data) Interactive notebooks and quizzes for every topic Capstone project: Build a predictive model from end to end Bonus: Intro to model deployment, explainability (SHAP), and ethical AI