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Machine Learning with Python
IntermediateFree LearningMachine Learning

Machine Learning with Python

by UptoSkills Team

Missions

14

Quests

96

Games

42

XP

700

Coins

35

Free Learning

Certificate optional — ₹499, or ₹199 on Pro

Self-paced
Certificate

Key Benefits

Earn XP & Level Up

Gain 700 XP to unlock badges and achievements

Collect Coins

Earn 35 coins to redeem rewards

Professional Certificate

QR-verified and recruiter-checkable — ₹499, or ₹199 on Pro

Unlock Achievements

Complete challenges and earn exclusive badges

About This League

Machine learning is a core competency for data-driven roles in the tech industry. This comprehensive league provides a structured pathway to understanding and applying key ML algorithms and techniques using Python. It's designed for students aiming to enhance their skills for internships and placements.

What you will learn

Upon completing this league, you will be able to:

  • Implement various regression and classification models in Python for predictive analytics.
  • Perform effective feature engineering and selection to improve model performance.
  • Apply unsupervised learning techniques for clustering and dimensionality reduction.
  • Build and tune machine learning pipelines for efficient model development.
  • Explain model predictions using interpretability techniques and deploy models to production environments.

What this league covers

ModuleWhat it covers
Module 1: Python for Machine Learning RefresherEssential Python libraries like NumPy and Pandas, plus data visualization with Matplotlib and Seaborn.
Module 2: Feature Engineering FoundationsHandling missing data, encoding categorical variables, feature scaling, interaction, and polynomial features.
Module 3: Regression Models: Linear and BeyondImplementing Linear Regression, Polynomial Regression, regularization techniques, and evaluation metrics.
Module 4: Classification Models: Mastering the BasicsApplying Logistic Regression, KNN, and SVM, alongside classification evaluation and probability calibration.
Module 5: Tree-Based Models: Decision Trees and Ensemble MethodsBuilding Decision Trees, Random Forests, Gradient Boosting, XGBoost, LightGBM, and CatBoost.
Module 6: Model Selection and Hyperparameter TuningUnderstanding the bias-variance tradeoff, cross-validation, and optimization techniques like Grid Search.
Module 7: Unsupervised Learning: Clustering TechniquesApplying K-Means, Hierarchical, and DBSCAN clustering, with methods for evaluating cluster performance.
Module 8: Dimensionality Reduction: PCA and MoreUtilizing PCA, LDA, t-SNE, and UMAP for reducing feature spaces and autoencoders.
Module 9: Model Explainability (Interpretability)Understanding model behavior with Permutation Importance, SHAP, LIME, and PDP plots.
Module 10: Time Series Analysis and ForecastingDecomposing time series, applying ARIMA and SARIMA models, and using Prophet for forecasting.
Module 11: Advanced Feature Engineering TechniquesExploring Target Encoding, automated feature generation with Featuretools, and text data features.
Module 12: Handling Imbalanced DatasetsTechniques for imbalanced data including SMOTE, resampling, and anomaly detection.
Module 13: Building ML Pipelines with Scikit-learnCreating end-to-end ML workflows, custom transformers, and parallel processing in pipelines.
Module 14: Deploying Machine Learning ModelsModel persistence, building APIs with Flask, containerization with Docker, and cloud deployment.

You will be equipped to tackle complex ML challenges and contribute to production systems. MLOps Fundamentals is a natural next step.

A look inside

Machine Learning with Python screen 1Machine Learning with Python screen 2Machine Learning with Python screen 3

Prerequisites

  • Comfortable writing Python, including lists, dictionaries and functions.
  • Comfortable with mean, median, distributions and basic probability.
  • Understand supervised learning, training/test splits and overfitting.

Learning Objectives

  • Implement and optimize at least three different machine learning models (e.g., Regression, Classification, Clustering) using Python's scikit-learn library on real-world datasets, achieving a minimum performance improvement of 15% as measured by a relevant evaluation metric (e.g., F1-score, RMSE).
  • Create and deploy a complete machine learning pipeline, including data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, using Python and relevant libraries like Pandas and scikit-learn, and document each step in a reproducible manner.
  • Analyze the performance of different machine learning models using techniques such as cross-validation, learning curves, and confusion matrices, and justify the selection of the best model based on both performance metrics and business requirements.
  • Understand and apply advanced optimization techniques such as grid search, random search, and Bayesian optimization to fine-tune the hyperparameters of a machine learning model, demonstrably improving its performance by at least 10% compared to default parameters.
  • Implement at least two different feature selection methods (e.g., Recursive Feature Elimination, SelectKBest) to reduce dimensionality and improve model performance, while also analyzing the impact of feature selection on model interpretability.