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Data Science Machine Learning

Track :

Programming

Lessons no : 24

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What will you learn in this course?
  • Master data cleansing, preparation, and analysis techniques for effective data science projects using Python and R tools
  • Apply machine learning algorithms such as regression, classification, and clustering to solve real-world problems
  • Develop predictive models with accuracy and interpretability for business decision-making and data-driven strategies
  • Implement feature engineering and selection methods to enhance machine learning model performance and robustness
  • Utilize data visualization and exploratory data analysis to uncover insights and communicate findings effectively
  • Integrate machine learning workflows into data pipelines for scalable and automated data science solutions
  • Evaluate model performance using metrics like accuracy, precision, recall, and F1 score for optimal results
  • Understand ethical considerations, data privacy, and bias mitigation in data science and machine learning applications
  • Leverage popular libraries such as scikit-learn, TensorFlow, and pandas for efficient data analysis and model building
  • Design and deploy machine learning models in cloud environments for real-time data processing and analytics

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Lessons | 24

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KADABALA CHAITANYA REDDY

Nice explanation 2025-06-20

Sameer

Good 2025-04-18

shawal khan

good 2023-12-01

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Data Science is the study of data cleansing, preparation, and analysis, while machine learning is a branch of AI and subfield of data science. Data Science and Machine Learning are the two popular modern technologies, and they are growing with an immoderate rate .