Machine Learning Python Course Free

Python for Machine Learning

star 4.51  Beginner level 2.25 learning hrs 473.6K+ 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

Key Highlights

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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.



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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.

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Python for Machine Learning

rating icon 4.51

2.25 Hours

Beginner

473.6K+ learners enrolled so far

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Learner reviews of the Free Courses

4.51
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Reviewer Profile

4.0

India
“NumPy Array Mathematics, Introduction to NumPy”
Python is the go-to language for machine learning, offering an extensive array of libraries and frameworks that make it possible to develop and deploy machine learning models efficiently. Its balance of simplicity, power, and flexibility is why it's favored by both beginners and experts in the field. However, there are some limitations in performance, which can be mitigated by using complementary tools or languages when necessary.
Reviewer Profile

5.0

India
“Python's Easy-to-Read Syntax and Robust Libraries”
Python is widely used in machine learning because of its easy-to-read syntax and robust libraries. Data handling and preprocessing are often done with Pandas and NumPy, which help organize and clean data for analysis. Visualization libraries like Matplotlib and Seaborn enable data exploration, helping identify trends and inform feature engineering.
Reviewer Profile

5.0

India
“Python in Machine Learning: Data Preprocessing and More”
Python is a programming language used in many stages of machine learning, including data preprocessing: Python's libraries, like NumPy, Pandas, and scikit-learn, are used to manipulate data. Model development: Frameworks like TensorFlow and PyTorch are used to build and train machine learning models. Data visualization: Python can be used to create data visualizations. Integration with other languages: Python can be called by other languages to create a product.
Reviewer Profile

5.0

India
“Increase Knowledge and Learn New Technologies”
I am very grateful to learn new things through which I upskill my knowledge. I would like to thank the faculty members who are putting their efforts into teaching these things free of cost. Thank you to Great Learning for giving such an opportunity to us.
Reviewer Profile

5.0

India
“The Course Was Very Structured and Organized”
The course was very structured and organized. It was engaging. I have learned the concepts of NumPy and Pandas in a very interesting way.
Reviewer Profile

5.0

“Machine Learning: A Subset of Artificial Intelligence”
Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from and make predictions or decisions based on data. ML enables systems to improve automatically through experience without being explicitly programmed. It involves techniques like supervised learning, unsupervised learning, and reinforcement learning, and is widely used in areas like image recognition, natural language processing, and autonomous systems.
Reviewer Profile

5.0

Italy
“Python Course for Beginners with Two Libraries”
It's easy to understand, clear, and easy to try immediately. A good start to using Python in data quality.
Reviewer Profile

5.0

India
“Valuable Experience with NumPy and Pandas”
I honed my skills in NumPy for fast array operations and Pandas for data analysis, mastering data manipulation, aggregation, and visualization to handle complex datasets efficiently.
Reviewer Profile

5.0

India
“Python for Machine Learning: Key Libraries”
Key Libraries: NumPy: Fundamental for numerical computations and handling arrays. Pandas: Excellent for data manipulation and analysis, especially with tabular data. Matplotlib & Seaborn: Useful for data visualization to explore and present data insights. Scikit-learn: A versatile library for traditional machine learning algorithms, including classification, regression, and clustering. TensorFlow & PyTorch: Popular for deep learning applications, offering tools for building and training neural networks.
Reviewer Profile

5.0

India
“Python's Simplicity and Flexibility in Machine Learning”
Python is a popular programming language for machine learning because of its simplicity, flexibility, and extensive libraries: Simplicity: Python's syntax is easy to read and understand, making it a good choice for coding algorithms. Flexibility: Python is adaptable and can be modified without recompiling the source code. Extensive libraries: Python has a large number of libraries and frameworks that are good for machine learning, such as Scikit-Learn.

What our learners enjoyed the most

Our course instructor

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Mr. Bharani Akella

Data Scientist

Machine Learning Expert

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5M+ Learners
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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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