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Machine Learning Python Course Free

Name: Python for Machine Learning
Rating: 4.51 (15992 reviews)

Python for Machine Learning

4.51 Beginner level 2.25 learning hrs 472.6K+ 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

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Skills you will gain

NumPy Arrays

NumPy Operations

NumPy Math

Saving & Loading NumPy

Pandas Series

Pandas DataFrame

+8 More

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

4.51

2.25 Hours

Beginner

472.6K+ learners enrolled so far

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Trusted by 10 Million+ Learners globally

4.6

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Learner reviews of the Free Courses

4.51

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24%

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Abishek SK (RA2111003040136)

4.0

★★★ ★ ☆

India

“The Hands-On Projects and Real-World Datasets Provided Excellent Practical Experience”

This course was a great introduction to Python for Machine Learning. I really enjoyed the clear explanations of core concepts like supervised learning, unsupervised learning, and deep learning. The hands-on labs with libraries like TensorFlow, Keras, and Scikit-learn helped solidify my understanding. I also appreciated the focus on real-world applications and the opportunities to work on projects that applied these techniques to real datasets. Overall, it's a great course for anyone looking to build a strong foundation in machine learning with Python.

Madhu Yadav

4.0

★★★ ★ ☆

India

“Python for Machine Learning (NumPy and Pandas)”

The "Python for Machine Learning" course at Great Learning offers a thorough introduction to key machine learning concepts and practical applications. The course content is well-structured, covering essential topics such as data preprocessing, feature engineering, and model evaluation. The hands-on projects and coding exercises provide valuable practical experience, reinforcing theoretical knowledge. The instructor's clear explanations and real-world examples greatly enhance the learning experience.

Mouli Gunisetti

5.0

★★★★ ★

India

“Comprehensive Introduction to Pandas and NumPy in Machine Learning”

I recently completed the Python for Machine Learning course with Great Learning, and I found it very informative and practical. The introduction to pandas and NumPy was particularly helpful, and the exercises gave me a solid hands-on experience with these libraries. The instructors were clear and made the concepts easy to understand. However, I would have appreciated more complex real-world examples in the exercises. Overall, it was a great learning experience!

Suvedha Balachandran

5.0

★★★★ ★

India

“Python for Machine Learning by Great Learning”

The "Python for Machine Learning" course at Great Learning offers an excellent foundation for anyone looking to explore the world of machine learning. The course provides a deep dive into Python programming, specifically tailored to the needs of machine learning applications. It covers key concepts such as data preprocessing, model building, and evaluating machine learning algorithms, using libraries like Pandas and NumPy. The course structure is well-organized, making it ideal for both beginners and those with some prior experience in Python.

windi ahmad

5.0

★★★★ ★

Indonesia

“Instructor, Topic Depth, Easy to Follow”

The instructor demonstrated a strong understanding of the topic, presenting complex concepts in a clear and engaging manner. The depth of coverage allowed for a thorough exploration of the subject, while the pacing was well-suited for learners at various levels. The use of practical examples and visual aids made the material easy to follow, enhancing comprehension. Overall, the instructor's ability to break down intricate ideas into digestible segments contributed significantly to a positive learning experience.

Dante Det

5.0

★★★★ ★

United States

“The Python for Machine Learning Certification is an Excellent Resource for Anyone Looking to Break into the Field of Machine Learning with a Strong Foundation in Python”

This certification strikes a great balance between theory and application, making it ideal for students, professionals, and anyone seeking to understand machine learning concepts through Python. While there’s room for improvement, the course delivers significant value and is a worthy investment in building foundational skills for a career in AI or data science.

Shannon Booth

5.0

★★★★ ★

United States

“Gaining a better understanding of both Numpy and Pandas”

I loved that I could take this course at my own pace, and the added notes feature to be able to type notes for myself along with the instructional videos as they were playing is very useful for me. I am the type that definitely learns through repetition and writing or typing things out for myself and being able to do that on the same screen while the video was playing helped to reinforce everything that was being discussed.

Jay Patel

5.0

★★★★ ★

United States

“Learning fundamentals of numpy and pandas the easy way”

Learning basic structure of python has many videos online, but this course was detailed about all pandas and numpy. It was great learning experiance.

727822TUAM040 PRAVEEN.A

5.0

★★★★ ★

India

“"Python for Machine Learning - Course Review”

The "Python for Machine Learning" course offers a comprehensive introduction to the key concepts and tools used in machine learning. It covers fundamental Python libraries such as pandas, NumPy, and scikit-learn, with hands-on examples to reinforce learning. The course excels at making complex topics like model training, evaluation, and data preprocessing accessible for beginners. Overall, it's a well-structured course that effectively bridges the gap between Python programming and machine learning applications, providing a solid foundation for further exploration in the fi

Vanessa Diaz

5.0

★★★★ ★

United States

“Python for Machine Learning Certification ”

The video lengths are suitable and manageable.Moreover, I genuinely enjoyed the instructor's explanations, as they were clear and insightful. He showed great examples that illustrated the concepts perfectly and provided the practical insights I was hoping to see. This approach made the learning experience both engaging and informative.

Deebak Harsha Sekar

5.0

★★★★ ★

Singapore

“Easy to understand python libraies and how to use them.”

I like how the Python libraries extract the data without writing many lines of code but within 1 line. The instructor and the videos were easy to follow and made me understand faster

Shikhar Agarwal

5.0

★★★★ ★

India

“Interesting short videos with proper explanation”

The course provided a comprehensive overview of Python's essential libraries for machine learning, including NumPy, Pandas, Matplotlib, and Scikit-learn. The hands-on exercises were particularly helpful in solidifying my understanding of key concepts. However, I would have appreciated more in-depth coverage of advanced topics like deep learning and natural language processing. Overall, the course was well-structured and valuable for building a strong foundation in Python for machine learning applications.

MADHAVAN V

5.0

★★★★ ★

India

“Python for machine learning beginner”

I recently completed the beginner course on Python for Machine Learning, focusing on NumPy and Pandas, offered by Great Learning. The course provided a solid foundation in these essential libraries. The explanations were clear and accessible, with practical examples that reinforced the concepts effectively. The hands-on exercises were particularly helpful in solidifying my understanding of how to manipulate and analyze data using NumPy and Pandas. Overall, it was a great introduction to these tools, and I feel well-prepared to apply them in more advanced machine learning contexts.

Gowri royal

5.0

★★★★ ★

India

“Key Features of Python for Machine Learning”

Key Features of Python for Machine Learning Ease of Use: Python’s simple syntax and readability make it accessible for beginners and efficient for experienced developers. Extensive Libraries: Python offers a rich ecosystem of libraries tailored for machine learning, such as: NumPy: For numerical computations and handling arrays. Pandas: For data manipulation and analysis. Scikit-learn: For implementing machine learning algorithms. TensorFlow and Keras: For building and training deep learning models. Matplotlib and Seaborn: For data visualization12. Community Support: Python has a large and active community, providing extensive resources, tutorials, and forums for troubleshooting and learning. Typical Workflow in Machine Learning with Python Data Collection: Gathering data from various sources. Data Preprocessing: Cleaning and preparing data for analysis. Exploratory Data Analysis (EDA): Understanding data patterns and relationships using visualization tools. Model Building: Selecting and training machine learning models using libraries like Scikit-learn or TensorFlow. Model Evaluation: Assessing model performance using metrics like accuracy, precision, and recall. Deployment: Integrating the model into a production environment for real-world use34. Python’s versatility and the availability of powerful libraries make it an ideal choice for machine learning projects, from simple linear regression to complex deep learning models. Would you like to dive deeper into any specific aspect of Python for machine learning? Learn more 1 geeksforgeeks.org 2 machinelearningmastery.com 3 geeksforgeeks.org 4 builtin.com +2 more Tell me more about deep learning with Python. How can I get started with Scikit-learn? No, thank you. Response stopped New topic Ask me anything or type "@" Using relevant sources

Raksha A Kotian

4.0

★★★ ★ ☆

India

“Python for Machine Learning/ Courses”

I really enjoyed the hands-on approach of the course, especially the practical exercises that reinforced key concepts. The clear explanations and real-world examples made complex topics easier to understand and apply. Additionally, the comprehensive coverage of Python libraries like NumPy and Pandas was extremely helpful for building a strong foundation in machine learning.

Vignesh Mudumba

5.0

★★★★ ★

India

“EXCELLENT COURSE IN ML AND PYTHON . GREAT TO START WITH ML.”

It is a very good experience learning this course .This is the best course to start as a beginner

Asia Siddiqui

5.0

★★★★ ★

“The Curriculum is Easy to Follow and Quizzes are Designed in a Good Way”

I really appreciated how well-organized the curriculum was. The clear progression of topics made it easy to understand each concept before moving on to the next. Additionally, the practical examples helped reinforce the material, making it more engaging and relatable. The resources provided were also very helpful in deepening my understanding. Overall, it was a great learning experience!

Bernice Yong

4.0

★★★ ★ ☆

Malaysia

“Deepening my understanding of real-world applications and discovering advanced techniques in data analysis.”

I particularly enjoyed exploring how theoretical concepts are applied in real-world scenarios, such as implementing machine learning algorithms and optimizing data processing workflows. Additionally, diving into advanced topics like big data analytics and quantum computing was both challenging and rewarding. The hands-on experience with various tools and technologies was invaluable in solidifying my knowledge and skills.

Saad Butt

4.0

★★★ ★ ☆

“Exploring Pandas and NumPy has been transformative, enhancing my data manipulation and analysis skills. ”

Diving into Pandas and NumPy has significantly enriched my data analysis journey. I developed expertise in handling large datasets efficiently, performing data cleaning, transformation, and advanced numerical computations. This hands-on experience taught me to streamline workflows and uncover actionable insights. These powerful tools have become essential for solving real-world problems and optimizing data-driven decision-making processes.

Justin Slater

5.0

★★★★ ★

United States

“"Unlock the Power of Python: Free Great Learning Course to Master Python Programming from Beginner to Advanced"”

The Great Learning Python Course is an excellent resource for anyone looking to learn Python programming for free. It offers a comprehensive curriculum that covers everything from basic syntax to advanced concepts, making it suitable for beginners as well as those with some prior programming experience.

What our learners enjoyed the most

Skill & tools

60%

60% of learners found all the desired skills & tools

Our course instructor

Mr. Bharani Akella

Data Scientist

Machine Learning Expert

5M+ Learners

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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