This repository provides a comprehensive, phased roadmap for individuals aiming to become proficient in Machine Learning and Deep Learning. It targets aspiring ML engineers and researchers by curating a structured learning path, from foundational programming and mathematics to advanced deep learning architectures and MLOps.

How It Works

The pathway is organized into five distinct phases, guiding learners through essential concepts and tools. It emphasizes a hands-on approach by linking to numerous tutorials, courses, and documentation for Python, Git, mathematics, ML algorithms (like k-NN, decision trees, regression), data preprocessing, deep learning frameworks (TensorFlow, Keras), computer vision, NLP, and MLOps principles. Each phase builds upon the previous one, culminating in practical project work and continuous learning.

Quick Start & Requirements

This is a learning resource, not a software package. No installation is required. Users will need access to the internet to utilize the linked external resources.

Highlighted Details

• Covers foundational programming (Python, OOP, Git, Bash) and essential mathematics (linear algebra, calculus, discrete math).
• Details core ML concepts including instance-based, model-based, supervised, and unsupervised learning, along with model evaluation.
• Explores deep learning architectures like CNNs, RNNs, Transformers, and generative models (GANs, VAEs), with a focus on image and text processing.
• Includes crucial MLOps topics such as model deployment, monitoring, versioning, and cloud platform integration.

Maintenance & Community

This repository appears to be a curated list of resources rather than an actively maintained software project. There are no listed contributors, sponsors, or community links.

Licensing & Compatibility

The repository itself does not have a specified license. The linked external resources are subject to their own licenses and terms of use.

Limitations & Caveats

As a curated list, the repository does not provide direct code or interactive environments; it relies entirely on external links. The quality and availability of linked resources are subject to change.