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Hands-On Python Machine Learning with Real World Projects
Rating: 4.2 out of 5(518 ratings)
35,096 students

Hands-On Python Machine Learning with Real World Projects

Python Based Machine Learning Course with Practical Exercises and Case Studies
Created bySayman Creative Institute
Last updated 3/2026
English
English [Auto],

What you'll learn

  • Applications of machine learning
  • Data manipulation and analysis
  • Building a predictive model to forecast sales
  • Essential Python libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)

Course content

1 section • 14 lectures • 4h 23m total length

  • Introduction2:06

    Explore Python for machine learning with hands-on, project-based lessons. Learn basics of Python, Jupyter notebook interface, NumPy, Pandas data frames and series, and reading CSV files for real-world projects.

  • Introduction to machine learning in python11:10

    Explore Python foundations for machine learning, including basic syntax, data types, and essential libraries like numpy, pandas, and scikit-learn for data handling and modeling, highlighting Python's simplicity and readability.

  • Understanding Machine Learning14:41

    Understand how machine learning, a branch of artificial intelligence, uses data and algorithms to imitate human learning and improve accuracy, with examples like Gmail spam detection and YouTube recommendations.

  • Understanding Machine Learning New Update14:56

    Explain how machine learning uses data and algorithms to imitate how humans learn, with examples from Gmail spam messages, Alexa, and YouTube recommendations.

  • Why use Python for Machine Learning11:33

    Learn why Python dominates machine learning and deep learning. Explore Python's readability, concise coding, and rich libraries like NumPy, Pandas, scikit-learn, and frameworks such as TensorFlow, PyTorch, and Keras.

  • Getting Started New Update14:00

    Download and install Python and Anaconda, set up your path, verify versions, and launch Jupyter notebook to start working with NumPy and pandas for machine learning.

  • Basics of Python and Interface of the Jupyter Notebook20:25

    Explore the basics of Python and the Jupyter Notebook interface, including code and markdown cells, variables, data types, lists, and dictionaries.

  • Numpy and it_s Inbuilt Functions- Part 122:33

    Explore NumPy and its inbuilt functions for machine learning, focusing on NumPy arrays, one dimensional and two dimensional shapes, and array reshaping.

  • Numpy and it_s Inbuilt Functions- Part 243:50

    Explore numpy indexing to access single and two-dimensional array elements, learn key inbuilt functions like arrange, linspace, and copy, and create arrays with shapes and dtypes.

  • 8. Learning Data Frames and Data Series in Pandas33:58

    Learn data frames and data series in pandas, with practical steps in a python notebook. Explore indexing with loc and iloc, converting to arrays, and exporting to csv.

  • Reading the .CSV Files Using Different Parameters in Pandas- Part 1 New26:49
  • Reading the .CSV Files Using Different Parameters in Pandas- Part 230:58

    Demonstrates reading csv files with pandas by using read_csv with string IO, selecting specific columns with usecols, setting per-column dtypes, and exporting to csv.

  • Class Project 19:50

    Create a data frame in Jupyter notebook using pandas and numpy, including importing libraries, shaping a 3 by 5 array, naming rows and columns, and viewing with head.

  • Class Project 26:59

    Read a csv into a pandas data frame with pd.read_csv in a Jupyter notebook, import pandas and numpy, set the file path, and view with df.head and df.info.

Requirements

  • No experience required

Description

Are you ready to unlock the power of machine learning with Python? This comprehensive course is designed to equip you with the essential skills to build predictive models that can solve real-world problems.

From beginner to expert, we'll guide you through the entire machine learning process, starting with the fundamentals of Python programming. You'll learn how to:

  • Prepare and clean data for analysis

  • Explore different machine learning algorithms and their applications

  • Build and train predictive models using popular libraries like Scikit-learn and TensorFlow

  • Evaluate model performance and refine your approach

  • Apply machine learning techniques to a variety of real-world problems, including:

    • Regression: Predicting continuous values (e.g., house prices)

    • Classification: Categorizing data (e.g., spam detection)

    • Clustering: Grouping similar data points (e.g., customer segmentation)

    • Neural networks and deep learning: Building complex models for tasks like image and natural language processing

Throughout the course, you'll work on hands-on projects that will help you solidify your understanding and develop practical skills. We'll also provide you with real-world case studies to demonstrate how machine learning can be applied to solve business challenges.

By the end of this course, you'll be able to:

  • Confidently use Python for machine learning tasks

  • Build and deploy predictive models that drive business value

  • Stay up-to-date with the latest trends in machine learning

Who this course is for:

  • Anyone who want to learn machine learning with python

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