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Numpy, Scipy, Matplotlib, Pandas, Ufunc : Machine Learning
Rating: 4.5 out of 5(13 ratings)
3,297 students

Numpy, Scipy, Matplotlib, Pandas, Ufunc : Machine Learning

Core data science and Machine Learning skills with NumPy, SciPy, Pandas, Matplotlib, Random and Ufunc.
Created byLogic Labs
Last updated 1/2026
English

What you'll learn

  • Creating Arrays
  • Array Indexing
  • Data Types
  • Random Data Distribution
  • Binomial Distribution
  • Logistic Distribution
  • ufunc Simple Arithmetic
  • ufunc Rounding Decimals
  • ufunc Greatest Common Denominator
  • Pandas Series
  • Pandas Data Frames
  • Pandas Analyzing Data Frames
  • SciPy Sparse Data
  • SciPy Graphs
  • SciPy Spatial Data
  • SciPy Statistical Significance Tests
  • Matplotlib Plotting
  • Matplotlib Markers
  • Matplotlib Plot Labels & Titles
  • Matplotlib Histograms
  • Matplotlib Pie Charts and More......

Course content

6 sections • 57 lectures • 4h 59m total length

  • Creating Arrays4:33
  • Array Indexing5:03
  • Array Slicing4:27
  • Data Types4:41
  • Array Copy vs View5:38
  • Array Shape5:24
  • Array Reshaping4:45

    Learn how to reshape numpy arrays across 1d, 2d, and 3d forms using the reshape method. Understand unknown dimensions with -1 to automatically infer shapes and transform data structures.

  • Array Iterating6:46
  • Joining Array4:34
  • Searching Arrays5:30
  • Sorting Arrays4:36
  • Filter Array4:26

Requirements

  • No prior coding experience is required.

Description

This course is a complete guide to NumPy, SciPy, Pandas, Matplotlib, Random, Ufunc, and Machine Learning, designed for anyone who wants to build a strong foundation in data science using Python. Whether you are a beginner or an aspiring data analyst or machine learning engineer, this course will help you understand how these essential libraries work together in real-world applications.


You will start by learning NumPy, focusing on arrays, indexing, slicing, mathematical operations, Random, and Ufunc functions. These core concepts are the backbone of numerical computing in Python and are essential for efficient data processing and machine learning workflows.


Next, you will explore Pandas for data manipulation and analysis. You will learn how to work with Series and DataFrames, clean and transform data, handle missing values, and perform data analysis tasks efficiently. These skills are critical for preparing data before applying Machine Learning models.


The course also covers Matplotlib for data visualization and SciPy for scientific and mathematical computing. You will learn how to create meaningful charts and graphs, perform statistical analysis, and apply scientific functions that support data analysis and machine learning development.


Throughout the course, you will gain hands-on experience by practicing key skills such as:

  • Working with NumPy arrays, Random functions, and Ufunc operations

  • Cleaning, analyzing, and transforming data using Pandas

  • Visualizing data with Matplotlib for better insights

  • Applying SciPy tools for statistics and optimization

  • Understanding how these libraries support Machine Learning workflows


By the end of this course, you will understand how to combine NumPy, SciPy, Pandas, Matplotlib, Random, and Ufunc to build efficient data pipelines and prepare data for Machine Learning projects. You will be able to analyze datasets, visualize patterns, and confidently work with Python’s most powerful data science libraries.


Enroll now and start your journey into Machine Learning by mastering NumPy, SciPy, Pandas, Matplotlib, Random, and Ufunc through practical examples and hands-on learning.

Who this course is for:

  • Anyone who wants practical experience with Numpy, Scipy, Matplotlib, Pandas, Ufunc and Random
  • Students and professionals working with Python data analysis
  • Aspiring Machine Learning engineers and data analysts
  • Beginners learning data science and Machine Learning

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