Machine Learning with Python: Case Studies
About this course
Build practical machine learning skills with Python through projects based on real-world datasets. You’ll begin by setting up your environment and applying linear, polynomial, robust, and logistic regression to model relationships, optimize predictions, and solve classification problems. As you progress, you’ll implement k-means clustering, calculate centroids, and visualize data distributions. You’ll also prepare sequential datasets and interpret time series forecasts using airline passenger and Bitcoin price data. Classification projects introduce logistic regression, decision trees, KNN, LDA, and Naive Bayes, along with decision-boundary visualizations that show how models separate classes. The course culminates in a financial credit risk project focused on credit card default prediction. You’ll clean large-scale records, explore payment delays and standing credit data, engineer features, and evaluate models with confusion matrices and AUC curves while visualizing results with seaborn. Designed for learners seeking applied experience in Python and machine learning, this course connects algorithms with step-by-step implementation. Case studies in salary prediction, startup cost analysis, face detection, fruit classification, forecasting, and credit risk help you prepare data, train and compare models, interpret outputs, and turn results into actionable insights. Enroll to develop an end-to-end machine learning workflow through project-driven practice.
75/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- apply regression techniques
- implement clustering methods
- execute classification algorithms
- conduct feature engineering
- evaluate models using performance metrics
- visualize data for actionable insights
- prepare data for analysis
- train machine learning models
- interpret machine learning outputs
Course objectives
- gain practical experience through case studies
- develop technical expertise in machine learning
- enhance problem-solving skills in data scenarios
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