Build a solid understanding of core ML algorithms
Data Preprocessing
Learn how to handle missing values, normalize and standardize data, encode categorical variables, and split data into training, validation, and test sets.
Supervised Learning Algorithms
Deep dive into various supervised learning algorithms including linear regression, logistic regression, decision trees, k-NN, SVM, and Naive Bayes classifiers.
Model Evaluation
Learn how to evaluate models using train/test split, cross-validation, and various metrics for regression and classification.
Unsupervised Learning Algorithms
Explore unsupervised learning algorithms including k-means clustering, hierarchical clustering, PCA, and the Apriori algorithm.
Tools and Libraries
Learn to use Scikit-Learn for implementing and evaluating models, Matplotlib and Seaborn for data visualization, and SciPy for scientific computing.
Projects
Apply your skills by building and evaluating various regression and classification models, performing clustering analysis, and conducting exploratory data analysis (EDA) projects.
