Master data preprocessing, feature engineering, and ML modeling techniques with a hands-on loan prediction project.
β±οΈ Length: 3.6 total hours
β 4.19/5 rating
π₯ 12,408 students
π March 2025 update
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Course Overview
- This concise ‘Data-Centric Machine Learning With Python: Hands-On Guide’ offers a practical deep dive into building robust ML models, emphasizing data’s critical role in the entire pipeline.
- Designed for practical application, the course uses Python to provide an end-to-end learning experience, bridging raw data to actionable insights.
- The curriculum centers around a hands-on loan prediction project, allowing learners to immediately apply data preprocessing, feature engineering, and ML modeling techniques to a real-world scenario.
- Despite its efficient 3.6 total hours, the course is packed with practical demonstrations and actionable insights for tangible skill acquisition.
- With a strong 4.19/5 rating from over 12,408 students and a recent March 2025 update, it’s a proven, high-quality resource for acquiring data-driven ML expertise.
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Requirements / Prerequisites
- Basic Python Proficiency: Foundational understanding of Python syntax, variables, data types, control flow, functions, and common data structures (lists, dictionaries).
- Conceptual Math/Statistics: Basic grasp of statistical concepts like mean, median, standard deviation, and correlation; no advanced math is required.
- No Prior Machine Learning Experience: This course is designed for beginners, introducing all fundamental ML concepts as needed.
- Development Environment Familiarity: Basic comfort with environments like Jupyter Notebooks or a similar Python IDE is helpful.
- Eagerness to Learn: A curious mindset and willingness to engage with hands-on coding exercises are essential.
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Skills Covered / Tools Used
- Data Preprocessing & Cleaning with Pandas and NumPy:
- Master handling missing values (imputation strategies like mean, median, mode).
- Detect and treat outliers effectively (IQR, z-score).
- Perform data cleaning: identifying duplicate records and correcting inconsistent entries.
- Implement data scaling and normalization (Min-Max, StandardScaler) for optimal model training.
- Skillfully encode categorical variables into numerical formats (One-Hot, Label Encoding).
- Feature Engineering for Enhanced Model Performance:
- Develop understanding of how to create powerful new features from existing data.
- Extract meaningful insights from date/time columns (day of week, month, elapsed time).
- Apply strategies for binning numerical features into categorical groups.
- Leverage domain knowledge to engineer relevant features, as in the loan prediction project.
- Machine Learning Modeling & Evaluation with Scikit-learn:
- Introduction to supervised learning paradigms (classification vs. regression).
- Essential practices: splitting data into training/testing sets and understanding cross-validation.
- Evaluate classification models using accuracy, precision, recall, F1-score, and ROC-AUC.
- Implement fundamental classification algorithms like Logistic Regression and Decision Trees/Random Forests.
- Explore basic concepts of hyperparameter tuning for model optimization.
- End-to-End Project Application (Loan Prediction):
- Apply all learned data-centric ML principles within a comprehensive loan prediction project workflow.
- Develop the ability to move from raw data ingestion to final model prediction and interpretation.
- Learn to interpret model outputs for data-driven decisions relevant to business context.
- Core Tools: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebooks.
- Data Preprocessing & Cleaning with Pandas and NumPy:
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Benefits / Outcomes
- Practical ML Workflow Mastery: Develop strong understanding of the entire ML workflow from data exploration to model evaluation.
- Enhanced Problem-Solving: Gain ability to systematically approach real-world data challenges, transforming messy data into insightful features.
- Proficiency in Python for Data Science: Solidify skills using Python’s leading data science libraries for efficient data manipulation and modeling.
- Portfolio-Ready Project: Complete a tangible loan prediction project for showcasing practical ML capabilities to employers.
- Deep Understanding of Data’s Role: Cultivate appreciation for why data quality and intelligent feature engineering are crucial for successful ML.
- Foundation for Advanced ML: Build a robust foundation preparing you for more advanced machine learning topics.
- Data-Driven Decision Making: Learn to interpret model results and performance metrics for informed decisions.
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PROS
- Highly Concise & Efficient: At just 3.6 hours, ideal for quickly acquiring core data-centric ML skills.
- Strong Practical Focus: Hands-on loan prediction project ensures immediate application and deeper understanding.
- Proven Quality & Popularity: Impressive 4.19/5 rating from over 12,408 students.
- Up-to-Date Content: March 2025 update guarantees relevant techniques and tools.
- Excellent Entry Point: Ideal for beginners, providing a solid, practical introduction to ML with Python.
- Skill-Oriented Learning: Directly addresses key industry skills, preparing learners for entry-level roles or further study.
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CONS
- Limited Depth for Advanced Topics: Due to its condensed nature, the course provides an excellent introduction but does not delve into complex theoretical aspects or a wide array of advanced algorithms in significant detail.
Learning Tracks: English,Development,Data Science
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