The “AI with Scikit-Learn” course is intended to provide participants with hands-on experience in machine learning using Scikit-Learn, one of the most powerful libraries in Python for building predictive models. This course will guide learners through essential concepts, including supervised learning with Scikit-Learn, unsupervised learning, data preprocessing, and model evaluation. Participants will develop the skills needed to
This program, run by the Oxford Training Centre, provides a systematic learning path that includes data pretreatment with Scikit-Learn, feature selection, model optimisation, and performance evaluation. Whether you are a newbie in AI or an experienced data professional, this course will provide you with the information and skills you need to succeed in Python-based machine learning projects.
Objectives and Target Group
Objectives
By the end of this training, participants will be able to:
- Understand machine learning with Scikit-Learn, including its core functionalities and applications.
- Master data preprocessing with Scikit-Learn, including handling missing values, feature scaling, and encoding categorical data.
- Implement supervised learning with Scikit-Learn, covering regression and classification techniques such as Decision Trees, Support Vector Machines, and Random Forests.
- Explore unsupervised learning in Scikit-Learn, including clustering methods like K-Means and DBSCAN.
- Perform model evaluation in Scikit-Learn, using cross-validation, confusion matrices, and performance metrics such as precision-recall and ROC curves.
- Work on practical examples with Scikit-Learn, applying machine learning to real-world scenarios in business, healthcare, and finance.
- Optimize models using advanced techniques, including Grid Search and Random Search for hyperparameter tuning.
- Leverage Python for machine learning workflows, integrating Scikit-Learn with Pandas, NumPy, and Matplotlib for data preprocessing and visualization.
Target Group
This course is suitable for:
- Data Scientists and Machine Learning Engineers looking to deepen their understanding of Scikit-Learn tutorial techniques.
- Python Developers interested in integrating machine learning models into their applications.
- AI Enthusiasts and Researchers eager to explore real-world Scikit-Learn examples and advanced implementations.
- Business Analysts and Decision Makers who want to leverage machine learning with Scikit-Learn for data-driven insights.
- Students and Academics focusing on AI, data science, and Python machine learning.
Course Content
The course is structured into comprehensive modules covering both theoretical concepts and practical implementations:
1. Introduction to Machine Learning and Scikit-Learn
- Overview of machine learning with Scikit-Learn
- Installation and setup of Scikit-Learn in Python
- Key components and architecture of Scikit-Learn
2. Data Preprocessing with Scikit-Learn
- Handling missing data and outliers
- Feature selection and engineering
- Encoding categorical data and feature scaling
3. Supervised Learning with Scikit-Learn
- Regression models: Linear Regression, Decision Trees, Support Vector Regression
- Classification models: Logistic Regression, Random Forest, Naïve Bayes, K-Nearest Neighbors
- Model performance metrics and evaluation techniques
4. Scikit-Learn Unsupervised Learning
- Clustering techniques: K-Means, Hierarchical Clustering, DBSCAN
- Principal Component Analysis (PCA) for feature reduction
- Applications of unsupervised learning in anomaly detection and segmentation
5. Scikit-Learn Model Evaluation and Optimization
- Cross-validation techniques and bias-variance tradeoff
- Hyperparameter tuning: Grid Search, Random Search
- Model deployment strategies and real-time implementation
6. Scikit-Learn Examples and Hands-on Projects
- Predictive analytics in finance: stock price prediction, risk assessment
- Customer segmentation and recommendation systems
- Fraud detection using machine learning with Scikit-Learn