Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
AI & Machine Learning From Scratch: Build Real-World Models
26 students

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

Build real AI & Machine Learning models from scratch using Python, PyTorch & OpenCV — no ChatGPT, no pre-trained APIs
Created byAvik Das
Last updated 2/2026
English

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.

Course content

2 sections • 12 lectures • 3h 15m total length

  • About The instructor3:40
  • Course overview & learning outcomes19:00

    Explore AI and machine learning from scratch, covering Python foundations, data analysis with NumPy and pandas, ML algorithms, deep learning, CNN transfer learning, NLP, and vision applications.

  • ML vs DL vs AI vs Generative AI12:48
  • Industry ML workflow11:44

    Explore the end-to-end industry machine learning workflow from problem definition to deployment and monitoring, emphasizing data quality, feature engineering, model selection, and MLOps for real-world impact.

  • Tools, datasets & evaluation mindset10:15

    Explore machine learning tools, datasets, and an evolution mindset to build real-world models. Use Python, NumPy, Pandas, Matplotlib, and frameworks like sklearn, XGBoost, LightGBM, TransFlow, and PyTorch for deployment-ready workflows.

  • Career & portfolio roadmap14:46

    Outline a practical career and portfolio roadmap for ML engineers, emphasizing problem solving over certificates, essential Python and statistics skills, end-to-end projects, and strong GitHub portfolios to impress recruiters.

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.

Description

Artificial Intelligence (AI) and Machine Learning (ML) are transforming software development, cybersecurity, healthcare, and modern startups. However, most online courses only teach how to use AI tools or APIs, without explaining how AI systems actually work.

This course is designed to change that.

In this practical, project-based AI & Machine Learning course, you will learn how to build real AI models from scratch, understand the internal logic behind Machine Learning algorithms, and deploy trained models into real applications. This course does not depend on ChatGPT, Gemini, or pre-trained AI APIs. Instead, you will learn how AI is built, trained, evaluated, and used in real-world systems.

The course begins with a clear explanation of Artificial Intelligence, Machine Learning, and Deep Learning, helping you understand where and how each is used in industry. You will then learn Python for AI, including NumPy, Pandas, and data handling techniques that are essential for Machine Learning.

As you progress, you will implement core Machine Learning algorithms from scratch, such as regression, classification, optimization techniques, and model evaluation. You will gain a deep understanding of how models learn from data, how loss functions work, and how performance is improved.

A major focus of this course is image-based AI. You will work with real image datasets using OpenCV and PyTorch, learning how computers process images and how neural networks learn visual patterns. You will also explore the basics of Deep Learning and neural network training in a practical and easy-to-understand way.

Beyond training models, this course focuses on the real-world deployment of models. You will learn how to save models, expose them using REST APIs, and integrate AI into web or mobile applications. Important topics such as overfitting, optimization, security risks, and ethical AI are also covered.

This course is ideal for developers, full-stack engineers, cybersecurity professionals, students, and startup founders who want real AI and Machine Learning skills, not shortcuts.

By the end of this course, you will not just understand AI concepts — you will be able to build, train, and deploy AI models with confidence.


Who this course is for:

  • Beginners and intermediate learners who want to build strong foundations in machine learning, deep learning, and artificial intelligence from scratch.
  • Software developers and engineering students who want to transition into machine learning, AI engineering, computer vision, or NLP roles.
  • Data analysts and professionals who want to move beyond tools and understand the mathematics, models, and real-world workflows behind ML and AI systems.
  • Researchers and practitioners who want hands-on experience with modern techniques like Transformers, Vision AI, semi-supervised learning, and foundation models.

Report abuse