ScikitLearn Tutorials

Scikit-learn Tutorials Roadmap

What is Machine Learning?
- Understanding the concept of learning from data without being explicitly programmed.
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning).
What is Scikit-learn?
- A popular open-source machine learning library for Python.
- Built on NumPy, SciPy, and Matplotlib.
- Provides a wide range of supervised and unsupervised learning algorithms.
- Known for its consistent API.
Why Learn Scikit-learn?
- Easy to use and learn.
- Comprehensive set of algorithms.
- Well-documented.
- Industry standard for many ML tasks.
- Integrates well with the Python data science ecosystem.
Setting up Your Development Environment:
- Installing Python.
- Installing Scikit-learn and its dependencies (NumPy, SciPy, Matplotlib) using pip or conda.
- Using a Python environment manager (virtualenv, conda).
- Using a code editor or IDE (VS Code, PyCharm, Jupyter Notebooks).
Basic Scikit-learn Workflow:
- Loading data (using built-in datasets or external data).
- Splitting data into training and testing sets.
- Choosing a model.
- Training the model (fitting).
- Making predictions.
- Evaluating the model.

Working with Data:
- Understanding the structure of data in Scikit-learn (NumPy arrays, Pandas DataFrames).
- Loading data from different sources (CSV, NumPy arrays).
Data Preprocessing:
- Handling Missing Values:
  - Imputation strategies (mean, median, most frequent).
  - Using SimpleImputer.
- Encoding Categorical Features:
  - One-Hot Encoding (OneHotEncoder).
  - Label Encoding (LabelEncoder).
- Scaling and Normalization:
  - Standard Scaling (StandardScaler).
  - Min-Max Scaling (MinMaxScaler).
  - Robust Scaling (RobustScaler).
- Handling Outliers (brief introduction).
- Polynomial Features (PolynomialFeatures).

Introduction to Classification:
- Understanding the goal of classification (predicting discrete classes).
- Binary vs. Multiclass Classification.
Key Classification Algorithms in Scikit-learn:
- Logistic Regression (LogisticRegression):
  - Understanding the model.
  - Training and predicting.
- Support Vector Machines (SVM) (SVC, LinearSVC):
  - Understanding the concepts (hyperplanes, margins).
  - Different kernels.
- Decision Trees (DecisionTreeClassifier):
  - Understanding the tree structure.
  - Entropy and Gini impurity.
- Random Forests (RandomForestClassifier):
  - Ensemble learning.
  - Bagging.
- K-Nearest Neighbors (KNN) (KNeighborsClassifier):
  - Understanding the concept of neighbors.
- Naive Bayes (GaussianNB, MultinomialNB):
  - Understanding the probabilistic approach.
- Gradient Boosting (GradientBoostingClassifier, AdaBoostClassifier).
Evaluating Classification Models:
- Accuracy, Precision, Recall, F1-score.
- Confusion Matrix.
- ROC Curve and AUC.
- Cross-validation (cross_val_score, KFold).

Introduction to Regression:
- Understanding the goal of regression (predicting continuous values).
Key Regression Algorithms in Scikit-learn:
- Linear Regression (LinearRegression):
  - Understanding the model.
  - Training and predicting.
- Lasso and Ridge Regression (Lasso, Ridge):
  - Regularization techniques.
- Support Vector Regression (SVR) (SVR, LinearSVR).
- Decision Trees (DecisionTreeRegressor).
- Random Forests (RandomForestRegressor).
- K-Nearest Neighbors (KNN) (KNeighborsRegressor).
Evaluating Regression Models:
- Mean Absolute Error (MAE).
- Mean Squared Error (MSE).
- Root Mean Squared Error (RMSE).
- R-squared.
- Cross-validation.

Introduction to Clustering:
- Understanding the goal of clustering (grouping similar data points).
- No labeled data.
Key Clustering Algorithms in Scikit-learn:
- K-Means (KMeans):
  - Understanding the algorithm.
  - Choosing the number of clusters.
- DBSCAN (DBSCAN):
  - Understanding density-based clustering.
- Hierarchical Clustering (AgglomerativeClustering).
Evaluating Clustering Results (Challenges and Metrics):
- Silhouette score.
- Davies-Bouldin index.

Introduction to Dimensionality Reduction:
- Reducing the number of features while preserving important information.
- Dealing with the curse of dimensionality.
Key Dimensionality Reduction Techniques in Scikit-learn:
- Principal Component Analysis (PCA) (PCA):
  - Understanding the concept of principal components.
  - Applying PCA.
- t-Distributed Stochastic Neighbor Embedding (t-SNE) (TSNE):
  - Understanding the concept (primarily for visualization).

Understanding Model Selection.
Understanding Hyperparameters vs. Model Parameters.
Techniques for Hyperparameter Tuning:
- Grid Search (GridSearchCV):
  - Exhaustively searching a defined hyperparameter space.
- Randomized Search (RandomizedSearchCV):
  - Randomly sampling hyperparameters from a distribution.
- Cross-validation within hyperparameter tuning.

Understanding Pipelines (Pipeline):
- Sequencing multiple steps (preprocessing, modeling).
- Preventing data leakage during cross-validation.
Understanding Feature Unions (FeatureUnion):
- Combining multiple transformers.

Ensemble Methods (beyond Random Forests - e.g., Voting, Stacking).
Imbalanced Datasets (brief introduction to techniques like oversampling and undersampling, though often handled by other libraries like imbalanced-learn).
Working with Text Data (brief introduction to CountVectorizer, TfidfVectorizer).
Introduction to Model Interpretation (briefly mentioning feature importance).

Official Scikit-learn Documentation (scikit-learn.org).
Scikit-learn User Guide and Examples.
Online Courses and Specializations in Machine Learning with Python (Coursera, edX, Udacity, DataCamp, etc.).
Books on Machine Learning with Scikit-learn.
Participating in Community Forums (Stack Overflow, Reddit r/learnmachinelearning, Scikit-learn mailing list).
Exploring Open-Source Machine Learning Projects on GitHub.
Staying updated with new features and releases.