Looking for something like Mallet? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 1 are free or have a free plan and 8 are open source.
Mallet provides machine learning tools for language processing, according to its project title.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | ML Course Notes | — | Not stated | — |
| 2 | ML YouTube Courses | — | Yes | — |
| 3 | Machine Learning Visualized | — | Yes | — |
| 4 | Machine Learning for Software Engineers | — | Not stated | — |
| 5 | Everything AI Ml | — | Yes | — |
| 6 | Hands-on Train and Deploy ML | — | Yes | — |
| 7 | Handson ML3 | — | Yes | — |
| 8 | ML Engineering | — | Not stated | — |
| 9 | LLM Course | — | Yes | — |
| 10 | Coding Questions | — | Not stated | Web |
A collaborative collection shares course and lecture notes on machine learning, natural language processing, and AI.
A curated repository helps readers discover machine learning and AI courses published on YouTube.
Jupyter notebooks implement and derive machine learning algorithms from first principles using NumPy.
A daily study plan for people working toward becoming machine learning engineers.
A curated collection gathers learning resources for generative AI, machine learning, LLMs, RAG, and related topics.
This tutorial walks through training and deploying a machine learning REST API that predicts cryptocurrency prices in ten steps.
A series of Jupyter notebooks teaches machine learning and deep learning in Python with Scikit-Learn, Keras, and TensorFlow.
This open book collects methods, tools, and instructions for training, fine-tuning, and running machine learning models.
This LLM course combines learning roadmaps with Google Colab notebooks for studying large language models.
StrataScratch offers over 1,000 data science interview questions across SQL, Python, statistics, machine learning, and data projects.
Machine Learning Systems is a two-volume textbook about how machine learning systems are built, covering the engineering behind AI.
Deep-ML provides hands-on exercises in machine learning and data science fundamentals.
This machine learning roadmap lays out a learning path that starts with mathematics and calculus fundamentals.
Machine Learning Visualized organizes its material as a collection of visual learning resources in a repository.
Codecademy’s Machine Learning Specialist path covers Python, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow.
This curated AI and machine learning guide organizes over 228 learning resources across 30 sections.
ProjectLearn teaches programming through projects across web, mobile, game development, machine learning, and artificial intelligence.
This repository walks through implementing a ChatGPT like language model in PyTorch, step by step.
A Python learning playground and cheat sheet organizes example scripts by topic, with explanations for the code.
Data Science IPython Notebooks collects notebooks about data science, including TensorFlow deep learning.
This Awesome DataScience repository collects learning material and resources about data science.
Scientific Python Lectures is a set of learning materials about Python for scientific computing.
Learn AI From Scratch presents a draft curriculum covering foundational topics for building an understanding of artificial intelligence.
The iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative
