Day 7 - NumPy Complete Tutorial | Array Creation, Statistics & Linear Algebra in Python
NumPy – Complete Tutorial for Data Science & AI
In this video, you will learn NumPy (Numerical Python), the fundamental library for numerical computing in Python. NumPy is widely used in Data Science, Machine Learning, Deep Learning, Artificial Intelligence, and Scientific Computing for efficient array operations and mathematical computations.
Topics Covered
✔ Introduction to NumPy
✔ Why NumPy is Important for Data Science & AI
✔ Creating NumPy Arrays
✔ One-Dimensional (1D) Arrays
✔ Two-Dimensional (2D) Arrays
✔ Multi-Dimensional Arrays
✔ Array Attributes (Shape, Size, Dimensions, Data Types)
✔ Array Creation Functions (array(), zeros(), ones(), arange(), linspace(), random())
✔ Statistical Operations
✔ Mean, Median, Mode
✔ Variance and Standard Deviation
✔ Minimum and Maximum Values
✔ Sum and Average Calculations
✔ Linear Algebra Operations
✔ Matrix Addition and Subtraction
✔ Matrix Multiplication
✔ Transpose of a Matrix
✔ Dot Product
✔ Real-Time Coding Examples
Learning Outcomes
By the end of this video, you will be able to:
Create and manipulate NumPy arrays efficiently
Perform statistical analysis on datasets
Apply linear algebra concepts using NumPy
Understand the foundation of Data Science and Machine Learning workflows
Write optimized and high-performance numerical computation code
🚀 Why Learn NumPy?
NumPy is the backbone of:
Data Science
Machine Learning
Deep Learning
Computer Vision
Natural Language Processing (NLP)
Generative AI
Scientific Computing
Data Analytics
Most popular libraries such as Pandas, Scikit-Learn, TensorFlow, PyTorch, and OpenCV are built on or work closely with NumPy.
Who Should Watch?
• Python Beginners
• Data Science Aspirants
• AI & Machine Learning Enthusiasts
• Software Developers
• Engineering Students
• Interview Preparation Candidates
📌 Subscribe for more tutorials on Python, NumPy, Pandas, Data Visualization, Machine Learning, Deep Learning, Computer Vision, NLP, Generative AI, RAG, Prompt Engineering, FastAPI, Streamlit, AWS, Azure, Hugging Face, Ollama, Gemini, Grok, and Real-World AI Projects.
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