Dive into the Core of AI
This program provides a comprehensive introduction to the foundational concepts of Machine Learning. You'll learn the theory behind common algorithms, understand how to prepare data for ML models, and gain hands-on experience in building and evaluating your first ML applications.
Supervised Learning
Explore classification and regression techniques, delve into algorithms like Linear Regression, Logistic Regression, Support Vector Machines, and Decision Trees. Understand model training, feature engineering, and evaluation metrics.
Explore Supervised LearningUnsupervised Learning
Master clustering techniques such as K-Means and Hierarchical Clustering, and learn dimensionality reduction methods like PCA. Discover how to find hidden patterns and structures in data without explicit labels.
Explore Unsupervised LearningModel Evaluation & Optimization
Learn crucial techniques for evaluating model performance, including cross-validation, precision, recall, and F1-score. Understand bias-variance trade-off and methods for hyperparameter tuning to optimize your models.
Explore EvaluationData Preprocessing
Discover essential steps for cleaning, transforming, and preparing your data for machine learning. Cover handling missing values, feature scaling, encoding categorical variables, and dealing with imbalanced datasets.
Explore Data Preprocessing