Which programming should i learn for ML?

Course Q&A Machine Learning Specialization Supervised ML: Regression and Classification
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which programming should i learn? Should I start with pandas and NumPY or any other?

Thanks in advance for advice

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Pandas, NumPy aren’t a programming language. They are python libraries. Consider them as various tools to make your task easier. If you are not familiar with python, I would suggest you learn python first. Coming to the question, which library to learn, both are very common libraries and you are going to use it in your machine learning journey along with others libraries like scikit-learn, matplotlib, etc. I would recommend to start with NumPy.

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Python is needed for data science and ML
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On ashupandey8738@gmail.com

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Congratulation, joining you up there soon

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I strongly recommend you gain a deep understanding of Python, which makes things easier when you face common libraries, automation problems, and real-life ML projects. Then you can go through Numpy and Pandas.

Some tips…

  1. Understand how lists, dictionaries, strings, sets, and tuples work in Python and what methods they have to manipulate data.
  2. Learn about loops and how you can manipulate object data (Lists, Tuples, Strings, Dictionaries) in Python.
  3. Learn about slicing, indexing, and useful methods of each data type.
  4. Learn how OOP works in Python.

Obviously, you’re going to gain more knowledge on the road. If you already have knowledge of previous topics, just ignore them and start with Numpy, I think that you can practice Numpy & and Pandas at the same time.

This is a useful Python course from Harvard, and it’s completely free.
course link: https://pll.harvard.edu/course/cs50s-introduction-programming-python

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Python is a common programming language in machine learning as it can support many types of ML. If you want to focus on convolutional neural networks or computer vision applications, then C++ is quite common as well. In addition, I noticed some open-source AI models like Rerun are written in Rust too (they said it has fewer bugs than C++).

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The programming language and libraries you should learn depend on your goals and interests in the field of data science and machine learning. Python is one of the most popular and versatile programming languages for data science, and it’s an excellent choice for beginners due to its readability and vast ecosystem of libraries. Here’s a suggested learning path:

  1. Python: Start by learning Python as your programming language. Familiarize yourself with the basics, data types, loops, functions, and other essential concepts.

  2. NumPy: Once you’re comfortable with Python, move on to NumPy. NumPy is a fundamental library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, as well as a collection of mathematical functions to operate on these arrays efficiently.

  3. Pandas: After learning NumPy, dive into Pandas. Pandas is another crucial library in Python for data manipulation and analysis. It provides data structures like DataFrames and Series that make it easy to handle and analyze structured data.

  4. Matplotlib and Seaborn: Next, explore data visualization using libraries like Matplotlib and Seaborn. These libraries allow you to create various types of charts and plots to visualize your data and gain insights.

  5. Machine Learning Libraries: Once you have a good foundation in Python and data manipulation, you can start exploring machine learning libraries like scikit-learn. Scikit-learn provides a wide range of tools for machine learning tasks, such as classification, regression, clustering, and more.

  6. Deep Learning Libraries: If you are interested in deep learning, you can then explore libraries like TensorFlow or PyTorch. These libraries are powerful frameworks for building and training deep neural networks.

  7. Real-World Projects: As you gain more confidence in these libraries, start working on real-world projects. Solving actual problems will help you reinforce your knowledge and build a portfolio to showcase your skills.

Remember, learning data science and machine learning is a continuous journey. Start with the basics, practice regularly, and gradually work on more complex projects to improve your skills. Online tutorials, courses, and practice on datasets are excellent resources to help you along the way.

Also, don’t forget to have fun and explore areas that interest you the most. Whether it’s data analysis, machine learning, natural language processing, computer vision, or any other domain, following your passion will make your learning experience more enjoyable and rewarding. Good luck on your data science journey!

Best regards
elirod

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