Udemy - AI and Machine Learning From Scratch - Build Real-World Models

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Udemy - AI and Machine Learning From Scratch - Build Real-World Models (Size: 1.4 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
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  1 - Introduction
  1. About The instructor.mp4 79.4 MB
  2 - SECTION 1 – Python Foundations & Workflow
  7. Install Python (Windows macOS Linux) and VS Code & Jupyter workflow.mp4 215.9 MB
  8. Virtual environments (venv).mp4 62.4 MB
  9. Python data types & variables.mp4 286.5 MB
  2. Course overview & learning outcomes.mp4 224.6 MB
  3. ML vs DL vs AI vs Generative AI.mp4 122.9 MB
  4. Industry ML workflow.mp4 112.7 MB
  5. Tools datasets & evaluation mindset.mp4 98.4 MB
  6. Career & portfolio roadmap.mp4 184.6 MB

Description


AI & Machine Learning From Scratch: Build Real-World Models

https://WebToolTip.com

Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 1m | Size: 1.35 GB

Build real AI & Machine Learning models from scratch using Python, PyTorch & OpenCV — no ChatGPT, no pre-trained APIs

What you'll learn
Build strong foundations in Python, statistics, linear algebra, and data handling required for real-world machine learning workflows.
Train, evaluate, and optimize machine learning models using supervised, unsupervised, semi-supervised, and self-supervised techniques.
Design and train deep learning models including CNNs, Vision Transformers, and NLP transformers using PyTorch and HuggingFace.
Create industry-ready ML, Computer Vision, and NLP projects with interpretability, evaluation metrics, and deployment-ready pipelines.

Requirements
Basic programming knowledge is helpful but not mandatory. All concepts are explained from scratch with practical, step-by-step examples.
Learners should have a computer (Windows, macOS, or Linux), a stable internet connection, and the ability to install free tools like Python and VS Code.
A willingness to learn mathematics, coding, and problem-solving is essential. No prior experience in machine learning or deep learning is required.

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