Machine Learning (Beginner to Advanced) - Powered by SkillCamper

Week 1 : [Introduction to Machine Learning] What is Machine Learning? Applications & Types Supervised vs. Unsupervised Learning Overview of Regression and Classification Tools: Jupyter, Scikit-learn basics Week 2: [Foundations of Machine Learning] Linear Regression: Concept, Coding & Interpretation Logistic Regression: Binary Classification Evaluation Metrics: Accuracy, Precision, Recall, F1-Score Data Splitting: Train-Test and Cross-Validation Week 3 [Advanced Supervised Learning] Decision Trees and Random Forests Gradient Boosting Machines (XGBoost, LightGBM, CatBoost) Model Tuning: Grid Search, Random Search Week 4 : [Unsupervised Learning and Dimensionality Reduction] Clustering: K-Means, Hierarchical Clustering PCA and t-SNE Real-life Unsupervised Applications Week 5 : [Probability, Statistics & ML Applications] Descriptive and Inferential Stats for ML Probability Distributions & Central Limit Theorem Hypothesis Testing in ML workflows Week 6 : [Time Series and Text Classification] Time Series Components and ARIMA Basics Forecasting with Facebook Prophet Text Preprocessing: Tokenization, TF-IDF, Word2Vec Sentiment Analysis with Naive Bayes / Logistic Regression Week 7: [NLP & ML Deployment] Sequence Modeling Overview Using Hugging Face Pipelines for Classification ML Model Deployment using Hugging Face Spaces Evaluation Techniques for NLP Models Week 8: [ML Ops & Capstone] Data Pipelines and Automation (ETL with Python) Model Monitoring and Retraining Basics Final Capstone Project: Real-world ML Problem Presentation and Feedback