Welcome to the 365 Days of ML Challenge Learning Journey repository! ✨
In this repository, you will find my learning journey as I embark on a year-long challenge to enhance my machine learning skills and knowledge. The purpose of this challenge is to engage in daily learning and practical application, pushing myself to explore various concepts, algorithms, and techniques in the field of machine learning.🎯
To guide my learning journey throughout the 365 days, I have outlined a comprehensive learning roadmap. This roadmap encompasses key topics and milestones that I aim to cover during the challenge. It includes:
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TensorFlow and Keras: Progressing from basic to advanced concepts of TensorFlow and Keras, including building neural networks, implementing deep learning algorithms, and optimizing models for better performance.
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Deep Learning Architecture Implementation from Scratch:🧠 Understanding the inner workings of deep learning algorithms and implementing them from scratch using Python and keras/Tensorflow to gain a deeper understanding of their mechanics.
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Research Paper Summarization and Implementation: Exploring cutting-edge research papers in the field of machine learning and summarizing their key findings. Implementing the proposed methods and comparing them against existing techniques.
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MLOps (Machine Learning Operations): ⚙️ Exploring the deployment and management of machine learning models in real-world scenarios. Learning about continuous integration, continuous deployment, and monitoring techniques to build scalable and reliable ML systems.
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Building Production-Level Projects:🏭 Working on projects that simulate real-world scenarios, including data preprocessing, feature engineering, model selection, and performance optimization. Emphasizing best practices for building production-level ML projects.
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Reinforcement Learning: 🤖 Delving into reinforcement learning algorithms, exploring topics such as Markov decision processes, Q-learning, policy gradients, and deep reinforcement learning, with applications in robotics, game playing, and more.
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Explainable AI:🕵️♂️ Understanding techniques and methods for interpretability and explainability in machine learning models. Exploring model-agnostic and model-specific interpretability techniques to ensure transparency and accountability in AI systems.
I believe in the power of collaboration and learning from others. I encourage fellow machine learning enthusiasts, beginners, and experts alike to get involved and contribute to this repository. Whether it's providing feedback, suggesting improvements, or sharing additional resources, your participation is greatly appreciated.🌟
If you would like to follow my journey or connect with me to discuss machine learning topics, feel free to reach out via the following channels:
Let's learn and grow together throughout this exciting 365 Days of ML Challenge!
Please note that while I strive to ensure the accuracy and quality of the content in this repository, the information provided is based on my personal learning experiences and may not represent industry standards or best practices. It is essential to conduct further research and refer to authoritative sources for comprehensive understanding.
This repository is licensed under the MIT license. Please refer to the LICENSE file for more details.
Happy learning! 🤗