1. Introduction to Machine Learning: This provides an essential foundation in machine learning, covering its various types and the importance of ML in data analysis. 2. Data Preprocessing and Analysis: Focusing on the crucial steps of cleaning, transforming, and analyzing data in Python and R, this part is fundamental for preparing datasets for ML modeling. 3. Regression Analysis: Covering both simple linear and polynomial regression models along with multiple regression, to understand relationships within data. 4. Logistic Regression: Delving into logistic regression and its applications, including ROC analysis, essential for classification problems. 5-6. Unsupervised Learning Techniques: This section covers clustering and dimensionality reduction techniques, including k-means, hierarchical clustering, density-based clustering, and principal component analysis. 7-8. Supervised Learning Techniques: A detailed exploration of algorithms such as decision trees, random forests, and support vector machines, implemented in both Python and R. 9. Model Evaluation and Tuning: This part discusses methods for evaluating ML models and strategies to optimize their performance, focusing on the balance between overfitting and underfitting. 10-11. Advanced Topics in Machine Learning: Introducing more complex areas of ML, such as neural networks, deep learning, and reinforcement learning, with practical examples in Python and R. 12-13. Real-World Machine Learning Projects: Practical application of ML concepts through projects and case studies, leveraging Python and R in real-world scenarios.
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The course is designed to provide comprehensive training in machine learning (ML) techniques using two of the most popular programming languages in data science: Python and R. This course is suitable for students, data scientists, software engineers, and analysts who wish to deepen their understanding of machine learning and its applications. This course aims to equip participants with the skills to build, evaluate, and deploy machine learning models using Python and R effectively. It balances theoretical knowledge with practical applications, ensuring that participants can apply machine learning concepts to solve real-world problems.
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