Machine Learning with Python Course: Hands-On Training

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32 hours 4 Enrolled 4.8 (4) Intermediate

Machine learning with Python course: hands-on model building

Machine Learning with Python course provides hands-on training designed to teach students how to build intelligent systems using Python and modern Machine Learning techniques. This course covers the complete Machine Learning lifecycle, from data collection and preprocessing to model training, evaluation, optimization, and deployment.

Students will gain practical experience using industry-standard libraries such as NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, and Keras to solve real-world business problems through predictive analytics and intelligent decision-making.

Whether you’re a student, software developer, data analyst, AI enthusiast, or business professional, this course will equip you with the skills needed to build Machine Learning applications and start a career in Artificial Intelligence and Data Science.


Practical applications of machine learning with Python

Machine Learning powers many technologies we use every day, including:

  • Netflix & YouTube Recommendations
  • ChatGPT & AI Assistants
  • Fraud Detection Systems
  • Self-Driving Cars
  • Medical Diagnosis
  • Customer Behavior Prediction
  • Sales Forecasting
  • Image & Speech Recognition

Machine Learning is one of the most in-demand skills worldwide and a key component of modern AI solutions.

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$299.00 $399.00

What's included

  • Standard Track
  • 10 Weeks
  • 2 Classes per Week
  • 90 Minutes per Session
  • Total: 30 Hours
  • Weekend Track
  • 8 Weeks
  • Saturday & Sunday Classes
  • 2 Hours per Session
  • Total: 32 Hours
  • One-to-One Training
  • Customized Schedule

What Will I Learn?

  • ✅ Understand Machine Learning fundamentals
  • ✅ Analyze and prepare datasets
  • ✅ Build Machine Learning models using Python
  • ✅ Implement Supervised and Unsupervised Learning algorithms
  • ✅ Evaluate and optimize model performance
  • ✅ Create predictive analytics solutions
  • ✅ Develop real-world Machine Learning projects
  • ✅ Deploy trained models for practical use

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