Machine Learning - Course Details
Dive into Machine Learning algorithms and techniques. From supervised and unsupervised learning to model evaluation and deployment. Build practical ML solutions.
- 4.5 Rating
What you'll learn?
- Supervised and unsupervised learning
- Regression and classification
- Model evaluation and tuning
- Feature engineering
- Deployment strategies
Requirements
- Python programming
- Basic statistics
- Understand fundamentals of Machine Learning (ML)
- Basic statistics: mean, median, variance, standard deviation
- Probability concepts: conditional probability, Bayes theorem
- Linear algebra basics: vectors, matrices, matrix operations
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Clustering: K-Means, Hierarchical, DBSCAN
- Dimensionality Reduction: PCA, t-SNE
- Cross-validation techniques
- Confusion Matrix, Accuracy, Precision, Recall, F1-score
- Overfitting vs Underfitting
- Bias-Variance tradeoff
- NumPy for numerical computations
- Pandas for data manipulation
- Data visualization: Matplotlib, Seaborn
- Regression models
- Classification models
- Clustering models
- Ensemble methods: Bagging, Random Forest
- Boosting algorithms: AdaBoost, Gradient Boosting, XGBoost, LightGBM
- Introduction to Neural Networks and Deep Learning
- Hyperparameter tuning and optimization
- End-to-end ML project: data preprocessing ? modeling ? evaluation
- Example projects: predicting house prices, classifying emails, customer segmentation, credit risk prediction
Document
No FAQ items for this course yet.
No videos for this course yet.
