bmtgoncalves / Fromscratch
Licence: mit
Stars: ✭ 61
Labels
Projects that are alternatives of or similar to Fromscratch
Awesome Ai Ml Dl
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.
Stars: ✭ 831 (+1262.3%)
Mutual labels: algorithms, jupyter-notebook, natural-language-processing
Bet On Sibyl
Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)
Stars: ✭ 190 (+211.48%)
Mutual labels: algorithms, jupyter-notebook, machine-learning-algorithms
Machine Learning Notebooks
Machine Learning notebooks for refreshing concepts.
Stars: ✭ 222 (+263.93%)
Mutual labels: jupyter-notebook, natural-language-processing, machine-learning-algorithms
Innovative Hacktober
Make a pull request. Let's hack the ocktober in an innovative way.
Stars: ✭ 34 (-44.26%)
Mutual labels: algorithms, jupyter-notebook, machine-learning-algorithms
Nagisa Tutorial Pycon2019
Code for PyCon JP 2019 talk "Python による日本語自然言語処理 〜系列ラベリングによる実世界テキスト分析〜"
Stars: ✭ 46 (-24.59%)
Mutual labels: jupyter-notebook, natural-language-processing
Machine Learning From Scratch
Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.
Stars: ✭ 42 (-31.15%)
Mutual labels: jupyter-notebook, machine-learning-algorithms
Play With Machine Learning Algorithms
Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
Stars: ✭ 1,037 (+1600%)
Mutual labels: jupyter-notebook, machine-learning-algorithms
Python Tutorial Notebooks
Python tutorials as Jupyter Notebooks for NLP, ML, AI
Stars: ✭ 52 (-14.75%)
Mutual labels: jupyter-notebook, natural-language-processing
Minerva Training Materials
Learn advanced data science on real-life, curated problems
Stars: ✭ 37 (-39.34%)
Mutual labels: jupyter-notebook, machine-learning-algorithms
Numerical Linear Algebra
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Stars: ✭ 8,263 (+13445.9%)
Mutual labels: algorithms, jupyter-notebook
Fasttext multilingual
Multilingual word vectors in 78 languages
Stars: ✭ 1,067 (+1649.18%)
Mutual labels: jupyter-notebook, natural-language-processing
Algorithms
🍣 Implementations of fundamental algorithms and data structures. Happy Hacktoberfest!
Stars: ✭ 41 (-32.79%)
Mutual labels: algorithms, jupyter-notebook
Al Go Rithms
🎵 Algorithms written in different programming languages - https://zoranpandovski.github.io/al-go-rithms/
Stars: ✭ 1,036 (+1598.36%)
Mutual labels: algorithms, jupyter-notebook
Coursera Natural Language Processing Specialization
Programming assignments from all courses in the Coursera Natural Language Processing Specialization offered by deeplearning.ai.
Stars: ✭ 39 (-36.07%)
Mutual labels: jupyter-notebook, natural-language-processing
Nlp Various Tutorials
자연어 처리와 관련한 여러 튜토리얼 저장소
Stars: ✭ 52 (-14.75%)
Mutual labels: jupyter-notebook, natural-language-processing
Whitehat
Information about my experiences on ethical hacking 💀
Stars: ✭ 54 (-11.48%)
Mutual labels: jupyter-notebook, machine-learning-algorithms
Emotion Detector
A python code to detect emotions from text
Stars: ✭ 54 (-11.48%)
Mutual labels: jupyter-notebook, natural-language-processing
Vietnamese Electra
Electra pre-trained model using Vietnamese corpus
Stars: ✭ 55 (-9.84%)
Mutual labels: jupyter-notebook, natural-language-processing
Natural Language Processing
Resources for "Natural Language Processing" Coursera course.
Stars: ✭ 969 (+1488.52%)
Mutual labels: jupyter-notebook, natural-language-processing
Natural Language Processing From Scratch
A Pearson Live Training webinar: https://www.safaribooksonline.com/live-training/courses/natural-language-processing-nlp-from-scratch/0636920225683/
The rise of online social platforms has resulted in an explosion of written text in the form of blogs, posts, tweets, wiki pages, and more. This new wealth of data provides a unique opportunity to explore natural language in its many forms, both as a way of automatically extracting information from written text and as a way of artificially producing text that looks natural.
In this class we introduce viewers to natural language processing from scratch. Each concept is introduced and explained through coding examples using nothing more than just plain Python and numpy. In this way, attendees learn in depth about the underlying concepts and techniques instead of just learning how to use a specific NLP library.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
1. Text Representation (50m)
- Represent words and numbers
- Use One-Hot Encoding
- Implement Bag of Words
- Apply stopwords
- Understand TF/IDF
- Understand Stemming
- Break 10m
2. Topic Modeling (60m)
- Find topics in documents
- Perform Explicit Semantic Analysis
- Understand Document clustering
- Implement Latent Semantic Analysis
- Implement Non-negative Matrix factorization
- Break 10m
3. Segment 3 Sentiment Analysis (40m)
- Quantify words and feelings
- Use Negations and modifiers
- Understand corpus based approaches
- Break 10m
4. Segment 4 Applications (70m)
- Understand Word2vec word embeddings
- Define GloVe
- Apply Language detection
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].