What you’ll learn
- Understanding Machine Learning Case Studies: Learn the practical application of machine learning through real-world case studies.
- Environment Setup for Machine Learning: Get hands-on experience in setting up the necessary environment for implementing machine learning algorithms
- Linear Regression Techniques: Understand and implement linear regression models, starting with the problem statement and progressing to regressions.
- Robust Regression and Logistic Regression: Explore robust regression techniques and delve into logistic regression for binary classification problems.
- k-Means Clustering: Gain insights into unsupervised learning with k-Means clustering, including creating scattered plots and calculating Euclidean distances.
- Time Series Modeling: Learn to model and analyze time series data, exploring applications like Bitcoin price prediction.
- Classification Algorithms: Master various classification techniques, including logistic regression, decision trees, k-nearest neighbors, linear discriminant ana
- Building Predictive Models: Understand the process of defining problem statements, preparing and cleaning data, and creating predictive models.
- Feature Engineering: Gain proficiency in feature engineering techniques, transforming variables, and preparing data for machine learning models.
- Visualization Techniques: Learn to visualize data using confusion matrices, AUC curves, SNS plots, and other visualization methods.
- Application in Finance: Apply machine learning to financial scenarios, exploring payment delays, standing credit, defaulting, and other relevant financials
- Throughout the course, participants will acquire practical skills and knowledge to tackle real-world machine learning challenges.
How to Enroll Hands-On Machine Learning: Python Project Showcase course?
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