Course Overview:
In an era where data drives decisions, mastering advanced machine learning techniques is crucial. This course equips participants with the skills needed to stay competitive in the job market. This course delves into the advanced concepts and techniques of machine learning using the powerful Scikit-Learn library in Python. Participants will explore various advanced algorithms, feature engineering methods, and model evaluation techniques essential for tackling complex real-world problems. Through a combination of theoretical insights and practical exercises, attendees will enhance their skills in deploying machine learning models effectively, enabling them to make data-driven decisions in their organizations.
Duration
10 Days
Who Should Attend
- Data Scientists and Analysts seeking to enhance their machine learning capabilities.
- Software Engineers interested in applying machine learning to software development.
- Business Analysts looking to leverage data for strategic decision-making.
- Researchers and Academics wanting to deepen their understanding of machine learning methods.
- Anyone with a foundational understanding of machine learning concepts who wants to advance their skills.
Course Objectives
By the end of this course, participants will be able to:
- Understand and implement advanced machine learning algorithms using Scikit-Learn.
- Conduct effective feature engineering and selection to improve model performance.
- Evaluate and fine-tune machine learning models using best practices.
- Deploy machine learning models for real-world applications.
- Analyze and interpret results to derive actionable insights.
Course Outline:
Module 1: Introduction to Advanced Machine Learning
- Overview of Machine Learning
- Review of Supervised vs. Unsupervised Learning
- Introduction to Scikit-Learn
Module 2: Advanced Regression Techniques
- Linear Regression and Regularization Techniques
- Polynomial Regression
- Advanced Topics in Regression Analysis
Module 3: Classification Algorithms
- Support Vector Machines (SVM)
- Decision Trees and Random Forests
- Gradient Boosting Machines (GBM) and XGBoost
Module 4: Unsupervised Learning Techniques
- K-Means Clustering and Hierarchical Clustering
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
Module 5: Feature Engineering and Selection
- Importance of Feature Engineering
- Techniques for Feature Selection
- Dealing with Missing Values
Module 6: Model Evaluation and Validation
- Train-Test Split vs. Cross-Validation
- Metrics for Evaluation (Accuracy, Precision, Recall, F1 Score)
- Hyperparameter Tuning with Grid Search and Random Search
Module 7: Ensemble Learning Techniques
- Bagging vs. Boosting
- Stacking Models
- Practical Applications of Ensemble Methods
Module 8: Time Series Analysis with Scikit-Learn
- Introduction to Time Series Forecasting
- Feature Engineering for Time Series Data
- Implementing Models for Time Series Prediction
Module 9: Model Deployment
- Overview of Model Deployment Techniques
- Creating REST APIs for Machine Learning Models
- Monitoring and Maintenance of Deployed Models
Module 10: Capstone Project
- Real-world project.
- Presentation of projects and peer reviews.
- Wrap-up and Q&A session.
