Mastering scikit-learn : Building Machine Learning Models
Step-by-Step Guide: Creating Robust Machine Learning Solutions for Predictive Analytics and Model Interpretation
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32
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10.5 hours
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Dec 2023
last update
$19.99
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What you will learn
Linear Models: Includes models like Linear Regression, Logistic Regression, and Support Vector Machines (SVM).
Neighbors: K-Nearest Neighbors (KNN) for classification and regression.
Naive Bayes: Naive Bayes classifiers for classification tasks.
Decision Trees: DecisionTreeClassifier and DecisionTreeRegressor for classification and regression.
Ensemble Methods: Random Forests, Gradient Boosting (e.g., AdaBoost, XGBoost), and Bagging (e.g., BaggingClassifier).
Support Vector Machines (SVM): SVM for classification and regression.
Neural Networks: Multi-layer Perceptron (MLP) for deep learning.
Clustering: K-Means, Agglomerative Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models.
Dimensionality Reduction: Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE).
Manifold Learning: Locally Linear Embedding (LLE) and Isomap.
Various feature selection methods, such as Recursive Feature Elimination (RFE), SelectKBest, and SelectFromModel.
Tools for model selection, cross-validation, and hyperparameter tuning, such as GridSearchCV and RandomizedSearchCV.
Metrics for evaluating model performance, like accuracy, precision, recall, F1-score, and many others.
Data preprocessing tools for scaling, encoding categorical variables, and handling missing data.
Text feature extraction using techniques like Count Vectorization and TF-IDF.
Models for time series forecasting, including ARIMA and Exponential Smoothing.
Isolation Forest and One-Class SVM for identifying anomalies in data.
Algorithms that combine both labeled and unlabeled data for training, such as LabelPropagation and LabelSpreading.
Techniques for handling imbalanced datasets, like SMOTE (Synthetic Minority Over-sampling Technique).
Calibrating models to improve probability estimates, using methods like CalibratedClassifierCV.
Models and techniques for handling multiclass and multilabel classification problems.
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udemy ID
10/31/2023
course created date
12/28/2023
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