Machine Learning Courses

4 hours

32 chapters

Intermediate

21 STUDYING NOW

Acquired skills: Machine Learning with scikit-learn, Model Training and Evaluation, Hyperparameter Tuning

2 hours

19 chapters

Intermediate

2 STUDYING NOW

Acquired skills: Linear Regression with Python, Model Training and Evaluation

3 hours

24 chapters

Intermediate

2 STUDYING NOW

Acquired skills: Python Programming, Python Classification Models, Logistic Regression, Data Preprocessing, Model Training and Evaluation, Hyperparameter Tuning

4 hours

34 chapters

Intermediate

4 STUDYING NOW

Acquired skills: Clustering fundamentals and algorithms , Handling missing and categorical data , Data normalization and distance metrics , K-Means: principles and cluster optimization , Hierarchical clustering and dendrograms , DBSCAN: noise handling and irregular shapes , Gaussian Mixture Models: probabilistic clustering

5 hours

51 chapters

Beginner

12 STUDYING NOW

Acquired skills: Functions & Sets, Series Analysis , Limits & Derivatives , Integrals , Gradient Descent , Vectors & Matrices , Linear Transformations , Matrix Decomposition , Probability Rules , Bayes' Theorem, Statistical Measures , Probability Distributions

6 hours

37 chapters

Advanced

1 STUDYING NOW

Acquired skills: Reinforcement Learning Foundations, Multi-Armed Bandit Algorithms, Dynamic Programming Methods, Monte Carlo Techniques, Temporal-Difference Learning, Gymnasium Basics

1 hour

16 chapters

Beginner

2 STUDYING NOW

Acquired skills: Evolutionary optimization , Swarm intelligence, Genetic algorithms , Particle swarm optimization, Artificial immune systems, Neuroevolution

1 hour

12 chapters

Beginner

4 STUDYING NOW

Acquired skills: Data Cleaning , Missing Value Imputation , Outlier Detection , Feature Encoding , Feature Scaling , Data Transformation , Feature Engineering , Feature Selection , Pipeline Building

2 hours

12 chapters

Intermediate

Acquired skills: Dimensionality reduction , Principal component analysis (PCA) , Covariance and eigen decomposition

2 hours

16 chapters

Intermediate

Acquired skills: Classification metrics (Accuracy, Precision, Recall, F1, ROC–AUC) , Regression metrics (MSE, RMSE, MAE, R²) , Clustering evaluation (Silhouette, Davies–Bouldin, Calinski–Harabasz) , Dimensionality reduction evaluation , Anomaly detection evaluation , Cross-validation techniques

2 hours

9 chapters

Advanced

Acquired skills: Drift Detection Fundamentals, Statistical Drift Metrics, Kolmogorov–Smirnov Test, Population Stability Index, Model-Based Drift Detection, Monitoring Model Degradation

2 hours

19 chapters

Beginner

Acquired skills: Feature Scaling, Mean-Centering, Standardization, Normalization (L1, L2, Max), Whitening and Decorrelation, Preprocessing Pipelines, Data Leakage Prevention