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

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Numpy, Scipy, Matplotlib, Pandas, Ufunc : Machine Learning
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.13 GB [/center] | Duration: 4h 58m
Core data science and Machine Learning skills with NumPy, SciPy, Pandas, Matplotlib, Random and Ufunc.
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......
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 operationsCleaning, analyzing, and transforming data using PandasVisualizing data with Matplotlib for better insightsApplying SciPy tools for statistics and optimizationUnderstanding how these libraries support Machine Learning workflowsBy 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.
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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