Master Python With NumPy For Data Science & Machine Learning
Master Python With NumPy For Data Science & Machine Learning, available at Free, has an average rating of 3.5, with 17 lectures, based on 675 reviews, and has 97538 subscribers.
You will learn about NumPy For Data Analysis NumPy For Data Science Numerical Computation Using Python How To Work With Nd-arrays How To Perform Matrix Computation This course is ideal for individuals who are Data Analyst Beginners or Business Analyst and AI Enthusiasts or Python Developers Beginners or Who Is Interested In ML, AI and Other Big Data Engineering It is particularly useful for Data Analyst Beginners or Business Analyst and AI Enthusiasts or Python Developers Beginners or Who Is Interested In ML, AI and Other Big Data Engineering.
Enroll now: Master Python With NumPy For Data Science & Machine Learning
Summary
Title: Master Python With NumPy For Data Science & Machine Learning
Price: Free
Average Rating: 3.5
Number of Lectures: 17
Number of Published Lectures: 17
Number of Curriculum Items: 17
Number of Published Curriculum Objects: 17
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- NumPy For Data Analysis
- NumPy For Data Science
- Numerical Computation Using Python
- How To Work With Nd-arrays
- How To Perform Matrix Computation
Who Should Attend
- Data Analyst Beginners
- Business Analyst and AI Enthusiasts
- Python Developers Beginners
- Who Is Interested In ML, AI and Other Big Data Engineering
Target Audiences
- Data Analyst Beginners
- Business Analyst and AI Enthusiasts
- Python Developers Beginners
- Who Is Interested In ML, AI and Other Big Data Engineering
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:
Pruthviraja L
Course Curriculum
Chapter 1: Introduction – Installation and Setup
Lecture 1: What Is NumPy
Lecture 2: How To Install And Setup NumPy & Pandas
Lecture 3: How To Work With The Jupyter Notebook
Chapter 2: NumPy Basics
Lecture 1: Numpy Initialization
Lecture 2: Creating An Ndarrays
Lecture 3: Data Types
Lecture 4: Pseudorandom Number Generation
Chapter 3: Indexing In NumPy
Lecture 1: Indexing And Slicing
Lecture 2: Boolean Indexing
Lecture 3: Fancy Indexing
Chapter 4: File Handling In NumPy
Lecture 1: How To Save And Load In Numpy
Chapter 5: Numerical Computation in NumPy
Lecture 1: Mathematical & Statistical Methods
Lecture 2: Arithmetic Operations
Lecture 3: Universal Functions In Numpy
Lecture 4: Conditional Logics In Numpy
Lecture 5: Sorting In Numpy
Chapter 6: Thank You and Bonus Section
Lecture 1: Congratulations!
Instructors
-
Pruthviraja L
Software Trainer and Lead Instructor – Team UpGraduate
Rating Distribution
- 1 stars: 43 votes
- 2 stars: 45 votes
- 3 stars: 161 votes
- 4 stars: 201 votes
- 5 stars: 225 votes
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