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Machine Learning A-Z™ with Python: Hands-On Bootcamp
New
Rating: 4.8 out of 5(3 ratings)
35 students

Machine Learning A-Z™ with Python: Hands-On Bootcamp

Master Machine Learning with Python, Scikit-Learn, Regression, Classification, Clustering, NLP, AI & Real Projects
New
Created byShayan Janati
Last updated 6/2026
English
English [Auto],

What you'll learn

  • Master machine learning with Python
  • Understand supervised and unsupervised learning
  • Work with Scikit-Learn professionally
  • Build regression models
  • Build classification systems
  • Create clustering and segmentation models
  • Perform feature engineering
  • Clean and preprocess datasets
  • Evaluate machine learning models
  • Understand bias and variance
  • Build recommendation systems
  • Work with Visualize machine learning dataNatural Language Processing (NLP)
  • Create end-to-end ML pipelines
  • Improve model accuracy and performance
  • Understand real-world ML workflows
  • Build portfolio-ready machine learning projects
  • Gain practical AI and data science experience

Course content

12 sections • 74 lectures • 10h 52m total length

  • Introduction6:12
  • What Is Machine Learning?6:27
  • Types of Machine Learning7:30
  • AI vs ML vs Deep Learning7:00
  • Setting Up the Environment4:47
  • Installing Python Libraries5:37

Requirements

  • Basic computer knowledge
  • Basic Python knowledge is recommended
  • No prior machine learning experience required

Description

Welcome to the ultimate hands-on Machine Learning Bootcamp with Python!

This course is designed to take you from beginner to building real machine learning models and AI systems using Python.

You will learn how modern machine learning works while building practical projects and solving real-world problems step-by-step.

Unlike theory-heavy AI courses, this bootcamp focuses heavily on hands-on implementation and practical workflows used by machine learning engineers and data scientists.

Throughout the course, you will build exciting machine learning projects including:

  • Prediction systems

  • Classification models

  • Recommendation systems

  • Customer segmentation projects

  • Fraud detection concepts

  • NLP and text analysis systems

  • Data analysis dashboards

  • Machine learning pipelines

  • AI-powered applications

  • Real-world predictive models

You will also learn how machine learning algorithms work internally, how models are trained and evaluated, and how professional ML workflows operate.

What Is Primarily Taught in Your Course?

Machine learning using Python and Scikit-Learn, including supervised learning, unsupervised learning, regression, classification, clustering, NLP, model evaluation, feature engineering, and real-world machine learning projects.

This course is beginner-friendly and explains everything step-by-step using intuitive examples and practical coding exercises.

By the end of this course, you will have practical machine learning skills, understand modern ML workflows, and be able to build your own machine learning projects confidently.

If you want to master machine learning with Python through practical projects, this course is for you.

Start building intelligent systems with Python today!

Who this course is for:

  • Beginners wanting to learn machine learning
  • Python developers interested in AI
  • Aspiring data scientists
  • Future machine learning engineers
  • Students learning artificial intelligence
  • Analysts wanting predictive modeling skills
  • Researchers and developers
  • Computer science students
  • Anyone interested in machine learning with Python

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