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Machine Learning Python Course Free

Python for Machine Learning

star 4.51  Beginner level 2.25 learning hrs 475.9K+ Learners

Learn Python for machine learning with NumPy arrays, array math, Pandas Series, DataFrames, Objects, and key functions. Join this free Python machine learning course to build data handling skills for real-world projects.

Instructor:

Mr. Bharani Akella

Python for Machine Learning

star 4.51  Beginner level 2.25 learning hrs 475.9K+ Learners
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Skills you will gain

NumPy Arrays

NumPy Operations

NumPy Math

Saving & Loading NumPy

Pandas Series

Pandas DataFrame

Pandas Functions (Mean

Median

Max

Min)

Data Manipulation

Supervised Learning

Unsupervised Learning

Machine Learning with Python

+8 More

What you'll learn

  • Join NumPy arrays using vstack, hstack, and column stack.
  • Extract array intersections and differences in NumPy.
  • Apply array math like sum, increment, mean, and median.
  • Calculate mean, median, max, and min with Pandas functions.

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    About this course

    The free machine learning Python course helps you build the core Python skills needed to work with data for machine learning tasks. You will learn how to use NumPy for array creation, joining arrays, finding intersections and differences, performing array-based math, and saving or loading array data. These skills help you prepare, organize, and process numerical data more efficiently before moving into machine learning workflows. 

    You will be able to use Pandas for data manipulation and analysis, including Series, DataFrames, and common functions such as mean, median, maximum, and minimum. By the end of the course, you will be able to handle data using NumPy and Pandas, perform basic data operations, and build a stronger foundation for machine learning, data analysis, and Python-based analytics projects.



    Stand out with an industry-recognized certificate

    Start learning

    10,000+ certificates claimed, get yours today!

    Get noticed by top recruiters

    Share on professional channels

    Globally recognised

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    Course outline

    Intro to Numpy

    In this section, you will be introduced to the NumPy library to add functions and methods to the program without actually writing the code.

    Joining NumPy Arrays

    There are three different modules to join Numpy arrays: vstack, hstack, and column stack. You will learn all these methods one by one in this module. 
     

    Numpy Intersection & Difference

    We shall understand what sect difference and intersection are at the beginning of this section. We shall then understand how to extract the exact elements by excluding other elements from the array with demonstrated code snippets.

    Numpy Array Mathematics

    This section will explain and demonstrate working with various mathematical operations using arrays like sum, increment, mean and median.

    Saving and Loading Numpy Array

    We will understand how to load and store a NumPy array in this section. We shall learn to work with arrays, starting from creating a NumPy array to storing and loading it with demonstrated sample codes.

    Intro to Pandas

    We shall understand Panel Data for data manipulation and analysis in Python. 

     

    Pandas Series Object

    We shall understand one-dimensional labeled arrays in this section. We will learn to import the Pandas library, create a series object using the inbuilt data type, and work with it with demonstrated code snippets.

    Intro to Pandas Dataframe

    After understanding the basics of Pandas Dataframe, you will learn to work with it through a sample demonstration in this section. 

     

    Pandas Functions

    We shall discuss the Pandas functions like mean, median, maximum, and minimum in this section. We shall also understand working with different methods for each of these functions.

    Get access to the complete curriculum once you enroll in the course

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    This course is ideal for

    • Beginners wanting to learn Python for machine learning.
    • Students and job seekers exploring data science roles.
    • Working professionals who analyze data in Python.
    • Job seekers who want a completion certificate.

    Python for Machine Learning

    rating icon 4.51

    2.25 Hours

    Beginner

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    Reviewer Profile
    Girdhari lal

    5.0

    ★★★★ ★
    “This Course Not Only Enhanced My Coding Skills but Also Boosted My Confidence in Tackling Complex Problems.”
    I just finished the Python course, and I can't express how grateful I am for the knowledge I've gained! The course provided a solid foundation in programming concepts, and I loved how hands-on the projects were. I feel empowered to tackle real-world problems and continue exploring the vast possibilities Python offers. Thank you to the instructors and everyone involved in creating such a great learning experience!
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    Reviewer Profile
    JAI AAKASH

    4.0

    ★★★ ★ ☆
    India
    “Mastering Python: A Comprehensive Guide to Building Intelligent Systems with Python, Leveraging Libraries, Techniques, and Best Practices to Unlock the Full Potential of Your Data”
    "Mastering Python for Machine Learning" is an essential resource for anyone looking to harness the power of Python in developing intelligent systems. This guide covers fundamental concepts, from data preprocessing to model evaluation, ensuring a solid foundation in machine learning. Readers will explore popular libraries like TensorFlow, Keras, and Scikit-learn, learning to implement various algorithms and techniques. The book emphasizes practical applications, with hands-on projects that demonstrate real-world problem-solving.
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    Reviewer Profile
    Shehan Perera

    5.0

    ★★★★ ★
    “Great Introduction to NumPy and Pandas!”
    The course does a fantastic job of breaking down complex library functions. I especially liked the quizzes on ndarray and DataFrame indexing—they really helped reinforce the difference between .loc and .iloc. It was easy to follow, though some more complex matrix operations could use a bit more depth.
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    Reviewer Profile
    Rubbaishe Tahir

    5.0

    ★★★★ ★
    “I Gained a Strong Foundation in Data Manipulation, Statistical Analysis, and Machine Learning Techniques.”
    I gained a strong foundation in data manipulation, statistical analysis, and machine learning techniques. The course also introduced me to various tools and libraries, such as NumPy, Pandas, and Matplotlib, which are crucial for any data scientist.
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    Reviewer Profile
    SHALINI G 717823I153

    5.0

    ★★★★ ★
    India
    “Python for Machine Learning: It Is Useful for My Career Path”
    Python is a leading language for machine learning due to its simplicity and powerful libraries like TensorFlow, PyTorch, and Scikit-learn. It allows for efficient model building and data analysis. With strong community support and extensive documentation, beginners can quickly learn and implement ML concepts. Python's flexibility enables rapid experimentation, making it an ideal choice for both academic and industry applications in machine learning.
    Read More
    Reviewer Profile
    Manoj Chathuranga

    4.0

    ★★★ ★ ☆
    “Handling and Processing Datasets Efficiently Using Pandas DataFrames.”
    The course effectively combines theoretical knowledge with practical applications, making it suitable for beginners and those looking to refresh their skills in machine learning. The interactive nature of the content, coupled with hands-on projects, greatly enhances the learning experience. The instructors were knowledgeable and provided clear explanations, ensuring a strong grasp of the concepts.
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    Reviewer Profile
    Komal Mandal

    4.0

    ★★★ ★ ☆
    India
    “The Learning Experience Was Highly Interactive, Combining Practical Projects with Clear, Expert Guidance.”
    I liked the course's focus on practical learning through real-world projects. The content was well-structured, and the instructors provided clear guidance.
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    Reviewer Profile
    Oviya R

    5.0

    ★★★★ ★
    India
    “Data Preparation for Machine Learning Is an Essential Phase That Directly Impacts the Performance and Accuracy of Your Model.”
    Data preparation for machine learning is an essential phase that directly impacts the performance and accuracy of your model. One of the first things you learn is the importance of understanding your data. Before diving into model building, you need to explore the dataset thoroughly to grasp its structure, patterns, and potential issues.
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    Reviewer Profile
    Travon Knight

    5.0

    ★★★★ ★
    United States
    “These Concepts Enhance Skills in Data Manipulation, Array Operations, and Debugging in Python, Key Tools for Data Analysis and Programming.”
    Pandas Series Indexing: Learned how to set and manipulate the index of a Pandas Series, including using methods like reindex, reset_index, and direct assignment. NumPy Array Shape: Understood how to interpret the shape of a 2D NumPy array, emphasizing the (rows, columns) format. Vertical Addition in NumPy: Discovered the use of np.sum with axis=0 to perform vertical addition of array elements, as well as distinguishing it from stacking (np.vstack) or horizontal summation (axis=1).
    Read More
    Reviewer Profile
    Anonymous

    5.0

    ★★★★ ★
    India
    “Python for Machine Learning: A Comprehensive Guide to Mastering One of the Most Powerful Tools in Data Science”
    Getting started with Python programming involves understanding its core elements. Python Basics cover the fundamental principles and simple operations. Syntax refers to the set rules that define how Python code is written and interpreted. Keywords are reserved words with predefined meanings and functions, like if, for, and while. Comments in Python, marked by #, explain the code without affecting its execution. Python Variables store data values that can change, and Data Types categorize these values into types like integers, strings, and lists, determining the
    Read More

    Our course instructor

    instructor img

    Mr. Bharani Akella

    Data Scientist

    Machine Learning Expert

    learner icon
    5.1M+ Learners
    video icon
    125 Courses
    Bharani has been working in the field of data science for the last 2 years. He has expertise in languages such as Python, R and Java. He also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow. He has been in the technical content side from last 2 years and has taught numerous classes with respect to data science.

    Frequently Asked Questions

    Will I receive a certificate upon completing this free course?

    Yes, upon successful completion of the course and payment of the certificate fee, you will receive a completion certificate that you can add to your resume.

    Is this course free?

    Yes, you may enroll in the course and access the course content for free. However, if you wish to obtain a certificate upon completion, a non-refundable fee is applicable.

    What will I learn in this free machine learning Python course?

    The Python for Machine Learning free course teaches NumPy and Pandas for machine learning tasks. You learn NumPy arrays, array joining and intersection, differences, array math, saving and loading arrays, Pandas Series, DataFrames, and functions such as mean, median, maximum, and minimum.

    Who should take this free Python machine learning course?

    The free course is useful for beginners who want to build data-handling skills before moving into machine learning projects. It is listed as a beginner-level course and focuses on practical Python libraries used in ML workflows.

    How long does this free Python-based machine learning course take to complete?

    The free ML with Python course includes 2.25 hours of learning. It gives you a short, focused way to build Python data handling skills used in machine learning.

    What skills will I gain from this free machine learning Python training course?

    This free ML with Python course helps you build the following skills:

    • NumPy Arrays

    • NumPy Operations

    • NumPy Math

    • Saving & Loading NumPy

    • Pandas Series

    • Pandas DataFrame

    • Pandas Functions (Mean, Median, Max, Min)

    • Data Manipulation

    • Supervised Learning

    • Unsupervised Learning

    • Machine Learning with Python


    What topics are covered if I want to learn Python for machine learning free?

    This course covers the following modules:

    • Intro to Numpy

    • Joining NumPy Arrays

    • Numpy Intersection & Difference

    • Numpy Array Mathematics

    • Saving and Loading Numpy Array

    • Intro to Pandas

    • Pandas Series Object

    • Intro to Pandas Dataframe

    • Pandas Functions


    Does this Python machine learning fundamentals course free include practical examples?

    The machine learning Python course includes worked examples and code for NumPy and Pandas. You learn by seeing how arrays, dataframes, and functions work in practical Python tasks.

    How does this free Python machine learning course help with real ML projects?

    The Learn Python for Machine Learning free course helps you clean, organize, transform, and analyze data using NumPy and Pandas. These are core steps before building machine learning models, because ML projects depend on well-prepared data.



    Will this free online Python course on machine learning teach both NumPy and Pandas?

    The Python for Machine Learning free course covers both libraries. NumPy is used for arrays, mathematical operations, and data I/O, while Pandas is used for Series, DataFrames, and common data analysis functions.



    What are the prerequisites required to learn the Python for Machine Learning course?

    Python for Machine Learning is a beginner's course, and you can begin the course with good knowledge of Python programming. However, if you are not familiar with it, we have a free Python fundamentals course that will clear your prerequisites.

    Will I have lifetime access to this free course

    Yes, once you enroll in the course, you will have lifetime access to this Great Learning Academy's free course. You can log in to the course and learn whenever you want to. 

     

    Is there any limit on how many times I can take this free course?

    Once you enroll in the Free Python for Machine Learning course, you have lifetime access to it. So, you can log in to this course anytime and learn it at your pace for free online. 

     

    Can I sign up for multiple courses from Great Learning Academy at the same time?

    Yes, you are free to enroll in as many courses as you want from Great Learning Academy. There is no stricture on the number of courses you can enroll in at once. The courses offered by Great Learning Academy are free, so we suggest you learn one by one to get the best out of them. 

     

    What are the steps to enroll in this Python for Machine Learning course?

    Enrolling in Great Learning Academy's Machine Learning with Python is a simple and straightforward approach. You will have to sign-up with your E-Mail ID, enter your user details, and then you can start learning at your own pace.


     

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