Best AI tools for< Python Machine Learning Engineer >

Data Science Dojo is a globally recognized e-learning platform that offers programs in data science, data analytics, machine learning, and more. They provide comprehensive and hands-on training in various formats such as in-person, virtual instructor-led, and self-paced training. The focus is on helping students develop a think-business-first mindset to apply their data science skills effectively in real-world scenarios. With over 2500 enterprises trained, Data Science Dojo aims to make data science accessible to everyone.

Google Colab, short for Google Colaboratory, is a free cloud service that supports Python programming and machine learning. It's a dynamic tool that enables users to write and execute Python code through a web-based interface, providing access to powerful computing resources without the need for local setup. Google Colab is particularly useful for data scientists, researchers, and students who require a convenient and accessible platform for developing and experimenting with machine learning models.

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.

Gradio is a tool that allows users to quickly and easily create web-based interfaces for their machine learning models. With Gradio, users can share their models with others, allowing them to interact with and use the models remotely. Gradio is easy to use and can be integrated with any Python library. It can be used to create a variety of different types of interfaces, including those for image classification, natural language processing, and time series analysis.

Google Colab is a free Jupyter notebook environment that runs in the cloud. It allows you to write and execute Python code without having to install any software or set up a local environment. Colab notebooks are shareable, so you can easily collaborate with others on projects.

AICorr.com is a website offering free coding tutorials with a focus on artificial intelligence, data science, machine learning, and statistics. Users can learn and practice coding in Python and SQL, explore projects with real data, and access a wealth of information in an easy-to-understand format. The website aims to provide up-to-date and relevant information to a global audience, ensuring a seamless learning experience for all.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and high-level mathematical functions to perform operations on these arrays. It is the fundamental package for scientific computing with Python and is used in a wide range of applications, including data science, machine learning, and image processing. NumPy is open source and distributed under a liberal BSD license, and is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike.

Blackbox is an AI-powered code generation, code chat, and code search tool that helps developers write better code faster. With Blackbox, you can generate code snippets, chat with an AI assistant about code, and search for code examples from a massive database.

Code Language Converter is an AI-powered tool that allows you to convert code from one programming language to another. Simply paste your code snippet into the converter and select the desired output language. The AI will then generate the converted code, which you can download or copy and paste into your project.Code Language Converter is a valuable tool for developers of all levels. It can save you time and effort by automating the code conversion process. Additionally, the converter can help you to learn new programming languages by providing you with a way to see how code is written in different languages.

AI Code Translator is an online tool that allows users to translate code or natural language into multiple programming languages. It is powered by artificial intelligence (AI) and provides intelligent and efficient code translation. With AI Code Translator, developers can save time and effort by quickly converting code between different languages, optimizing their development process.

Supersimple is an AI-native data analytics platform that combines a semantic data modeling layer with the ability to answer ad hoc questions, giving users reliable, consistent data to power their day-to-day work.

Anyscale is a company that provides a scalable compute platform for AI and Python applications. Their platform includes a serverless API for serving and fine-tuning open LLMs, a private cloud solution for data privacy and governance, and an open source framework for training, batch, and real-time workloads. Anyscale's platform is used by companies such as OpenAI, Uber, and Spotify to power their AI workloads.

BentoML is a framework for building reliable, scalable, and cost-efficient AI applications. It provides everything needed for model serving, application packaging, and production deployment.

PyAI is an advanced AI tool designed for developers and data scientists to streamline their workflow and enhance productivity. It offers a wide range of AI capabilities, including machine learning algorithms, natural language processing, computer vision, and more. With PyAI, users can easily build, train, and deploy AI models for various applications, such as predictive analytics, image recognition, and text classification. The tool provides a user-friendly interface and comprehensive documentation to support users at every stage of their AI projects.

Keras is an open-source deep learning API written in Python, designed to make building and training deep learning models easier. It provides a user-friendly interface and a wide range of features and tools to help developers create and deploy machine learning applications. Keras is compatible with multiple frameworks, including TensorFlow, Theano, and CNTK, and can be used for a variety of tasks, including image classification, natural language processing, and time series analysis.

Apache MXNet is a flexible and efficient deep learning library designed for research, prototyping, and production. It features a hybrid front-end that seamlessly transitions between imperative and symbolic modes, enabling both flexibility and speed. MXNet also supports distributed training and performance optimization through Parameter Server and Horovod. With bindings for multiple languages, including Python, Scala, Julia, Clojure, Java, C++, R, and Perl, MXNet offers wide accessibility. Additionally, it boasts a thriving ecosystem of tools and libraries that extend its capabilities in computer vision, NLP, time series, and more.

DataCamp is an online learning platform that offers courses in data science, AI, and machine learning. The platform provides interactive exercises, short videos, and hands-on projects to help learners develop the skills they need to succeed in the field. DataCamp also offers a variety of resources for businesses, including team training, custom content development, and data science consulting.

Pickl.AI is a platform offering professional certification courses in Data Science, empowering individuals to enhance their career prospects. The platform provides a range of courses tailored for beginners, students, and professionals, covering topics such as Machine Learning, Python programming, and Data Analytics. Pickl.AI aims to equip learners with industry-relevant skills and expertise through expert-led lectures, real projects, and doubt-clearing sessions. The platform also offers job guarantee programs and short-term courses to cater to diverse learning needs.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

Fueling-Ambitions-Via-Book-Discoveries is an Advanced Machine Learning & AI Course designed for students, professionals, and AI researchers. The course integrates rigorous theoretical foundations with practical coding exercises, ensuring learners develop a deep understanding of AI algorithms and their applications in finance, healthcare, robotics, NLP, cybersecurity, and more. Inspired by MIT, Stanford, and Harvard’s AI programs, it combines academic research rigor with industry-standard practices used by AI engineers at companies like Google, OpenAI, Facebook AI, DeepMind, and Tesla. Learners can learn 50+ AI techniques from top Machine Learning & Deep Learning books, code from scratch with real-world datasets, projects, and case studies, and focus on ML Engineering & AI Deployment using Django & Streamlit. The course also offers industry-relevant projects to build a strong AI portfolio.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

AI-resources is a repository containing links to various resources for learning Artificial Intelligence. It includes video lectures, courses, tutorials, and open-source libraries related to deep learning, reinforcement learning, machine learning, and more. The repository categorizes resources for beginners, average users, and advanced users/researchers, providing a comprehensive collection of materials to enhance knowledge and skills in AI.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

This repository is a collection of free resources and a roadmap designed to help individuals learn Machine Learning and Artificial Intelligence concepts by providing key concepts, building blocks, roles, a learning roadmap, courses, certifications, books, tools & frameworks, research blogs, applied blogs, practice problems, communities, YouTube channels, newsletters, and must-read papers. It covers a wide range of topics from supervised learning to MLOps, offering guidance on learning paths, practical experience, and job interview preparation.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

The 'awesome-mobile-robotics' repository is a curated list of important content related to Mobile Robotics and AI. It includes resources such as courses, books, datasets, software and libraries, podcasts, conferences, journals, companies and jobs, laboratories and research groups, and miscellaneous resources. The repository covers a wide range of topics in the field of Mobile Robotics and AI, providing valuable information for enthusiasts, researchers, and professionals in the domain.

MarkLLM is an open-source toolkit designed for watermarking technologies within large language models (LLMs). It simplifies access, understanding, and assessment of watermarking technologies, supporting various algorithms, visualization tools, and evaluation modules. The toolkit aids researchers and the community in ensuring the authenticity and origin of machine-generated text.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

This repository provides a ready-to-run basic Slack AI solution that allows users to summarize threads and channels using OpenAI. Users can generate thread summaries, channel overviews, channel summaries since a specific time, and full channel summaries. The tool is powered by GPT-3.5-Turbo and an ensemble of NLP models. It requires Python 3.8 or higher, an OpenAI API key, Slack App with associated API tokens, Poetry package manager, and ngrok for local development. Users can customize channel and thread summaries, run tests with coverage using pytest, and contribute to the project for future enhancements.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

Awesome AI & Data GitHub-Repos is a curated list of essential GitHub repositories covering the AI & ML landscape. It includes resources for Natural Language Processing, Large Language Models, Computer Vision, Data Science, Machine Learning, MLOps, Data Engineering, SQL & Database, and Statistics. The repository aims to provide a comprehensive collection of projects and resources for individuals studying or working in the field of AI and data science.

Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

AI guide for Python certification PCEP and PCAP with project-based, exam-focused learning.

I'm your Machine Learning and Python mentor, ready to teach and guide you.

Refactor python code. Python expert with proficiency in data science, machine learning (including LLM apps), and both OOP and functional programming.

A specialized AI sharing insights about Manojkumar Palanisamy, his Python, GPT, and machine learning expertise, and interests.

Advance your Python skills and break through tough coding problems with expert mentoring, blending real-world wisdom and cutting-edge techniques.

I'm a Python and PyTorch expert with knowledge of ML infrastructure requirements ready to help you build and scale your ML projects.

Bilingual Python engineer with expertise in English and Chinese.

Dynamic, interactive game for learning Python with adaptive paths and community features.

一名使用python语言的，专业的AI工程师

Teaches you how to use the Metaphor Search API using our Python SDK

为你写代码或者提供代码建议优化

Your friendly guide in Python game dev, helping you discover answers!

I provide Python interview prep and conduct mock interviews.

Python code annotator and test generator

A Python and programming expert, guiding users on best practices for writing clean, efficient, and well-documented Python code.

A Python tutor creating stepwise coding challenges.

Experienced Python Developer offering expert coding advice and debugging help

Versatile Python programming assistant, adept in TDD and pytest across various projects.

Beginner-friendly Python guide including practical projects

Python代码辅助，专注于最佳实践、代码质量和flet UI