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Machine Learning Algorithms in Python

Track :

Programming

Lessons no : 45

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What will you learn in this course?
  • Develop and implement supervised learning algorithms like linear regression and decision trees in Python using scikit-learn
  • Apply unsupervised learning techniques such as clustering and dimensionality reduction to real-world datasets in Python
  • Evaluate machine learning models using metrics like accuracy, precision, recall, and F1 score for classification tasks
  • Optimize hyperparameters of machine learning algorithms to improve model performance and generalization in Python
  • Implement ensemble methods like random forests and boosting algorithms to enhance predictive accuracy in Python
  • Handle missing data, feature scaling, and data preprocessing techniques for effective machine learning model training
  • Utilize Python libraries such as pandas, NumPy, and scikit-learn for data manipulation, analysis, and model development
  • Interpret model outputs and visualize results to communicate insights effectively using Python visualization tools
  • Build and deploy machine learning models in Python for practical applications like predictive analytics and automation
  • Identify appropriate machine learning algorithms based on data type, problem type, and project requirements in Python
  • Apply cross-validation and model validation techniques to ensure robustness and prevent overfitting in Python models
  • Develop end-to-end machine learning workflows in Python, from data preprocessing to model deployment

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Lessons | 45
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MANISH KUMAR

good 2025-09-03

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An “algorithm” in machine learning is a procedure that is run on data to create a machine learning “model.” Machine learning algorithms perform “pattern recognition.” Algorithms “learn” from data, or are “fit” on a dataset. There are many machine learning algorithms .