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Machine Learning with Python

Track :

Programming

Lessons no : 21

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What will you learn in this course?
  • Master Python libraries like scikit-learn, TensorFlow, and Keras for effective machine learning model development and deployment
  • Implement data preprocessing, feature engineering, and data visualization techniques using Python for improved model accuracy
  • Design and train supervised and unsupervised machine learning algorithms to solve real-world problems with Python
  • Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score in Python-based projects
  • Apply Python to build scalable AI and machine learning applications across different platforms and environments
  • Utilize Python's flexibility to integrate machine learning models into existing software systems and workflows
  • Optimize machine learning models with hyperparameter tuning and cross-validation techniques in Python
  • Leverage Python's community resources and libraries to troubleshoot, enhance, and innovate AI solutions
  • Develop end-to-end machine learning projects using Python, from data collection to deployment
  • Understand ethical considerations and best practices for responsible AI development with Python

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Lessons | 21

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46 Reviews

KANAPARTHI SURYA TEJA

good
2025-11-20

Vidhur Rayachoti

Great Course, Taught a Lot
2025-11-14

ANINDITO BHATTACHARYA

very Good
2025-11-11

SHUBHAM SINGH RAWAT

GREAT COURSE
2025-11-11

DEIVY JULIAN VALENCIA C.C. 1125271495

.
2025-10-28

ISHITA DEB

Very helpful
2025-10-26

Nanduri Dundi

Good
2025-10-05

Sappati Durga Mukhesh

Its very helpfull to learn the ML basics
2025-10-02

Sudip Das

nice
2025-09-14

Mohammed Fazil

The content and explanation is good and I have learned about the Machine Learning and use of Python in this and I got very brief knowledge about it. Thank you for providing this course.
2025-09-06

PEYMAN AFSHARI BIJARBANEH

it is perfect.
2025-09-02

Muhammad Randy Akbar

Mind blowing
2025-08-07

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Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These add to the overall popularity of the language.