• Lesson 1: What is Scikit-Learn?
• Lesson 2: History and Importance of Scikit-Learn
• Lesson 3: Installing Scikit-Learn (Windows, macOS, Linux)
• Lesson 4: Importing and Using Scikit-Learn
• Lesson 5: Overview of Machine Learning Concepts

• Lesson 1: Types of Datasets (Structured vs. Unstructured)
• Lesson 2: Loading Datasets in Scikit-Learn
• Lesson 3: Understanding Data Formats (NumPy, Pandas, CSV, etc.)
• Lesson 4: Exploring and Visualizing Data
• Lesson 5: Handling Missing Values

• Lesson 1: Feature Scaling (Normalization & Standardization)
• Lesson 2: Handling Categorical Data (One-Hot Encoding, Label Encoding)
• Lesson 3: Dealing with Missing Data (Imputation)
• Lesson 4: Feature Engineering Basics
• Lesson 5: Splitting Data into Training and Testing Sets

• Lesson 1: Introduction to Regression Models
• Lesson 2: Simple Linear Regression
• Lesson 3: Multiple Linear Regression
• Lesson 4: Polynomial Regression
• Lesson 5: Evaluating Regression Models

• Lesson 1: Introduction to Classification Models
• Lesson 2: Logistic Regression
• Lesson 3: Decision Trees for Classification
• Lesson 4: Support Vector Machines (SVM)
• Lesson 5: Evaluating Classification Models

• Lesson 1: Understanding Overfitting and Underfitting
• Lesson 2: Cross-Validation Techniques
• Lesson 3: Performance Metrics for Regression Models
• Lesson 4: Performance Metrics for Classification Models
• Lesson 5: Confusion Matrix and ROC Curve

• Lesson 1: Introduction to Clustering
• Lesson 2: K-Means Clustering
• Lesson 3: Hierarchical Clustering
• Lesson 4: DBSCAN Algorithm
• Lesson 5: Evaluating Clustering Models

• Lesson 1: Introduction to Dimensionality Reduction
• Lesson 2: Principal Component Analysis (PCA)
• Lesson 3: Linear Discriminant Analysis (LDA)
• Lesson 4: t-SNE for Data Visualization
• Lesson 5: Feature Selection Techniques

• Lesson 1: What are Pipelines?
• Lesson 2: Building and Using Pipelines
• Lesson 3: Custom Transformers in Pipelines
• Lesson 4: Grid Search with Pipelines
• Lesson 5: Real-World Applications of Pipelines

• Lesson 1: Importance of Feature Selection
• Lesson 2: Univariate Selection Methods
• Lesson 3: Recursive Feature Elimination (RFE)
• Lesson 4: Feature Importance with Decision Trees
• Lesson 5: Handling High-Dimensional Data

• Lesson 1: Handling Large Datasets in Scikit-Learn
• Lesson 2: Exploratory Data Analysis (EDA)
• Lesson 3: Data Cleaning Strategies
• Lesson 4: Creating Custom Data Transformers
• Lesson 5: Case Study: Predicting House Prices

• Lesson 1: What is Ensemble Learning?
• Lesson 2: Bagging Techniques (Random Forest)
• Lesson 3: Boosting Techniques (AdaBoost, Gradient Boosting)
• Lesson 4: Stacking and Voting Classifiers
• Lesson 5: Comparing Ensemble Methods

• Lesson 1: Handling Imbalanced Data
• Lesson 2: Advanced Feature Transformation Methods
• Lesson 3: Using Polynomial Features
• Lesson 4: Interaction Features and Feature Generation
• Lesson 5: Feature Selection in High-Dimensional Datasets

• Lesson 1: Introduction to Hyperparameter Tuning
• Lesson 2: Grid Search vs. Randomized Search
• Lesson 3: Bayesian Optimization for Hyperparameter Tuning
• Lesson 4: Using Optuna for Model Tuning
• Lesson 5: Hyperparameter Tuning Best Practices

• Lesson 1: Ridge and Lasso Regression
• Lesson 2: Elastic Net Regression
• Lesson 3: Decision Tree Regression
• Lesson 4: Random Forest Regression
• Lesson 5: Gradient Boosting Regression

• Lesson 1: K-Nearest Neighbors (KNN)
• Lesson 2: Naive Bayes Classifier
• Lesson 3: Extreme Gradient Boosting (XGBoost)
• Lesson 4: LightGBM and CatBoost Classifiers
• Lesson 5: Multi-Label Classification

• Lesson 1: Spectral Clustering
• Lesson 2: Gaussian Mixture Models (GMM)
• Lesson 3: Affinity Propagation Clustering
• Lesson 4: Mean-Shift Clustering
• Lesson 5: Evaluating Clustering Performance

• Lesson 1: Time Series Data Preparation
• Lesson 2: Feature Engineering for Time Series
• Lesson 3: Autoregressive Models in Scikit-Learn
• Lesson 4: Time Series Forecasting with Machine Learning
• Lesson 5: Case Study: Stock Price Prediction

• Lesson 1: Introduction to Anomaly Detection
• Lesson 2: Isolation Forests
• Lesson 3: One-Class SVM
• Lesson 4: Local Outlier Factor (LOF)
• Lesson 5: Real-World Applications of Anomaly Detection

• Lesson 1: Saving and Loading Models
• Lesson 2: Deploying Models Using Flask
• Lesson 3: Deploying Models Using FastAPI
• Lesson 4: Integrating with Cloud Platforms (AWS, GCP)
• Lesson 5: Automating Model Deployment

• Lesson 1: Understanding Model Interpretability
• Lesson 2: Feature Importance and SHAP Values
• Lesson 3: LIME for Model Explanations
• Lesson 4: Fairness in Machine Learning Models
• Lesson 5: Ethical Considerations in ML

• Lesson 1: Combining Scikit-Learn with TensorFlow
• Lesson 2: Feature Extraction for Neural Networks
• Lesson 3: Hybrid Models (ML + DL)
• Lesson 4: Using Scikit-Learn in PyTorch Pipelines
• Lesson 5: Case Study: Deep Learning with Scikit-Learn

Course Duration: about 50+10 hours

The online class is held via Skype (or Zoom or Microsoft Teams) and the cost per hour of tutoring is only $15. At the end of this long course, you will master all the required basic and advanced concepts of Scikit-Learn Library and we will accomplish a real-world project together for about 10 hours, which fully prepares you to find a job as a entry level AI / ML engineer in AI / ML job markets.
To book this class, message or call my telegram or WhatsApp:
+98 (912) 490-8372 or +98 (935) 490-8372.
You can also send email to me:
abolfazl.mohammadijoo@gmail.com