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Python Machine Learning: From Beginner to Pro
Rating: 4.2 out of 5(795 ratings)
43,142 students

Python Machine Learning: From Beginner to Pro

Machine Learning Tutorial: Python-Based Predictive Analytics
Created byLearnify IT
Last updated 4/2026
English

What you'll learn

  • Gain a solid understanding of Python programming, including syntax, data structures, and control flow.
  • Explore the core principles and algorithms of machine learning, such as supervised and unsupervised learning.
  • Learn techniques for cleaning, preparing, and transforming data for machine learning models.
  • Discover methods for creating new features or selecting relevant features for model building.

Course content

1 section26 lectures5h 37m total length
  • Introduction2:38
  • Introduction to Machine Learning with Python4:08
  • AI vs ML vs Deep Learning4:58
  • How does Machine Learning Work7:36
  • Types of Machine Learning New Update5:30
  • Supervised learning & Examples New Update6:21
  • Unsupervised Learning & Examples8:48
  • Reinforcement Learning & Examples9:18
  • Examples of AI4:15
  • Deep Learning & Examples10:40
  • Jupyter & Installation23:26
  • Machine Learning Tutorial & Algorithms21:49

    Set up Anaconda Navigator and Jupyter Notebook, then run machine learning tutorials. Explore code mirror extensions and iris data sets, and run other languages in Jupyter.

  • Demo of Iris Dataset26:29
  • Linear Regression & Value of R254:08

    Explore linear regression fundamentals in Python, including simple and multiple regression, coefficients and intercept, R-squared interpretation, SSR and SST, p-values, and practical coding with NumPy, Pandas, and Matplotlib.

  • Statistics and Probability Concepts-15:20
  • Category of Data for Machine Learning10:26

    Explore data categorized by type (numerical, text, time series), structure (structured, unstructured, semi-structured), learning problem, source, and domain to inform preprocessing and feature engineering.

  • Qualitative data and Quantitative data in machine learning10:35
  • Information gain & Entropy and Confusion Matrix12:06
  • Types of event and Probability of Distribution10:48
  • How to Import Datasets in Jupyter11:17
  • Data Analysis24:15
  • Train & Test Data9:56
  • Logistic Regression Curve8:12
  • Decision Tree8:23
  • Class project 113:21
  • Class project 212:28

Requirements

  • No experience required

Description

Are you eager to dive into the exciting world of machine learning and harness the power of Python? This comprehensive course is designed to guide you from a beginner to a proficient machine learning practitioner.

Key Learning Objectives:

  • Master Python Fundamentals: Gain a solid understanding of Python programming, essential for machine learning.

  • Explore Machine Learning Concepts: Learn the core principles and algorithms of machine learning, including supervised and unsupervised learning.

  • Work with Real-World Datasets: Practice data cleaning, preprocessing, and feature engineering using real-world datasets.

  • Build Predictive Models: Develop various machine learning models, such as linear regression, logistic regression, decision trees, random forests, and neural networks.

  • Evaluate Model Performance: Learn to assess model accuracy, precision, recall, and other metrics.

  • Apply Machine Learning in Practice: Discover real-world applications of machine learning in fields like finance, healthcare, and marketing.

Course Highlights:

  • Hands-On Projects: Engage in practical exercises and projects to reinforce your learning.

  • Step-by-Step Guidance: Follow clear explanations and coding examples.

  • Real-World Examples: Explore real-world use cases of machine learning.

  • Expert Instruction: Learn from experienced machine learning professionals.

  • Lifetime Access: Enjoy unlimited access to course materials.

Who This Course is For:

  • Beginners in machine learning who want to learn Python.

  • Data analysts or scientists looking to enhance their skills.

  • Professionals seeking to apply machine learning to their work.

Who this course is for:

  • Beginners in machine learning who want to learn the fundamentals using Python.