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In this course, you will learn :
- Hands-on coding examples will help you understand the theoretical foundations.
- The capability of training, optimising, evaluating, and deploying various machine learning models.
- Familiarity with the process of selecting the best models to solve practical problems.
- Hands-on experience with various types of data for machine learning modelling is required.
- The ability to fine-tune various parameters in order to improve the accuracy of machine learning models.
- A working understanding of how to use hands-on projects and exercises on real-world data sets.
Syllabus :
- Linear Regression
- Regularization
- Bias-Variance Trade-off
- Categorical Features
- Logistic Regression
- Logistic Regression: Titanic Data
- Multiclass Classification and Handling Imbalanced Classes
- Project: Predicting Chronic Kidney Disease
- K-Nearest Neighbors
- Implementation of K-Nearest Neighbors
- Logistic Regression vs. KNN
- Decision Tree Learning
- Bootstrapping and Confidence Interval
- Support Vector Machine
- Practice and Comparisons
