Machine Learning Courses
4,845 Learnerscourse
Introduction to Machine Learning with Python
4 hours
32 chaptersIntermediate
25 STUDYING NOW
Acquired skills: Machine Learning with scikit-learn, Model Training and Evaluation, Hyperparameter Tuning
course
Linear Regression with Python
2 hours 19 chaptersIntermediate
3 STUDYING NOW
Acquired skills: Linear Regression with Python, Model Training and Evaluation
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Classification with Python
3 hours 24 chaptersIntermediate
2 STUDYING NOW
Acquired skills: Python Programming, Python Classification Models, Logistic Regression, Data Preprocessing, Model Training and Evaluation, Hyperparameter Tuning
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Cluster Analysis with Python
4 hours 34 chaptersIntermediate
3 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
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Mathematics for Data Science with Python
5 hours 51 chaptersBeginner
11 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
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Data Preprocessing and Feature Engineering with Python
1 hour 12 chaptersBeginner
5 STUDYING NOW
Acquired skills: Data Cleaning , Missing Value Imputation , Outlier Detection , Feature Encoding , Feature Scaling , Data Transformation , Feature Engineering , Feature Selection , Pipeline Building
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Introduction to Reinforcement Learning with Python
6 hours 37 chaptersAdvanced
1 STUDYING NOW
Acquired skills: Reinforcement Learning Foundations, Multi-Armed Bandit Algorithms, Dynamic Programming Methods, Monte Carlo Techniques, Temporal-Difference Learning, Gymnasium Basics
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Loss Functions in Machine Learning
2 hours 15 chaptersIntermediate
1 STUDYING NOW
Acquired skills: Mathematical Foundations of Loss Functions, Risk Minimization Theory, Regression Loss Analysis, Classification Loss Analysis, Information-Theoretic Losses, Loss Function Selection and Comparison
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Advanced Tree-Based Models with Python
1 hour 9 chaptersIntermediate
Acquired skills: CatBoost Modeling, XGBoost Modeling, LightGBM Modeling, Model Regularization, Categorical Feature Handling, Model Interpretation, Model Blending, Deployment Best Practices
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Bio-Inspired Algorithms with Python
1 hour 16 chaptersBeginner
2 STUDYING NOW
Acquired skills: Evolutionary optimization , Swarm intelligence, Genetic algorithms , Particle swarm optimization, Artificial immune systems, Neuroevolution
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Evaluation Metrics in Machine Learning with Python
2 hours 16 chaptersIntermediate
1 STUDYING NOW
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
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Exploratory Data Analysis with Python
2 hours 18 chaptersBeginner
Acquired skills: Exploratory Data Analysis, Descriptive Statistics, Data Visualization with matplotlib and seaborn, Correlation Analysis, Multivariate Analysis, Data Storytelling
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20+ hands-on Real-world projectsPersonalized study tracksUnlimited workspaces100+ Top-Rated coursesCompletion certificatesAI-Assistant in all courses20+ hands-on Real-world projectsPersonalized study tracksUnlimited workspaces1. | Introduction to Machine Learning with Python |  4 hours |  32 chapters |
2. | Linear Regression with Python | 2 hours | 19 chapters |
3. | Classification with Python | 3 hours | 24 chapters |
4. | Cluster Analysis with Python | 4 hours | 34 chapters |
5. | Mathematics for Data Science with Python | 5 hours | 51 chapters |
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