What you’ll learn
- NumPy For Data Analysis
- NumPy For Data Science
- Numerical Computation Using Python
- How To Work With Nd-arrays
- How To Perform Matrix Computation
- If students knows Python, that is well & good
- Anaconda Installation to work with the NumPy and Python
- Basic mathematics
- Willing to learn data analysis, data science or numerical computation for programm
Hi, welcome to the ‘NumPy For Data Science & Machine Learning’ course. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations. We know that the matrix and arrays play an important role in numerical computation and data analysis. Pandas and other ML or AI tools need tabular or array-like data to work efficiently, so using NumPy in Pandas and ML packages can reduce the time and improve the performance of the data computation. NumPy based arrays are 10 to 100 times (even more than 100 times) faster than the Python Lists, hence if you are planning to work as a Data Analyst or Data Scientist or Big Data Engineer with Python, then you must be familiar with the NumPy as it offers a more convenient way to work with Matrix-like objects like Nd-arrays. And also we’re going to do a demo where we prove that using a Numpy vectorized operation is faster than normal Python lists.
So if you want to learn about the fastest python-based numerical multidimensional data processing framework, which is the foundation for many data science packages like pandas for data analysis, sklearn, scikit-learn for the machine learning algorithm, you are at the right place and right track. The course contents are listed in the “Course content” section of the course, please go through it.
I wish you all the very best and good luck with your future endeavors. Looking forward to seeing you inside the course.
Towards your success:
Who this course is for:
- Data Analyst Beginners
- Business Analyst and AI Enthusiasts
- Python Developers Beginners
- Who Is Interested In ML, AI and Other Big Data Engineering
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