Python and Machine Learning

Python and Machine Learning

About Me

Hi, my name is Ryan. I am a junior data scientist at Finatext, currently focussing on sentiment analysis of cryptocurrencies. I graduated with a First-class honours degree in BSc Economics from the University of Bath. I have had work experience in asset management, wealth management and data analytics consulting. I will be starting my Masters degree in Computing Science at Imperial College London in October 2018. I created this blog to document my learning journey in computer science, particularly in machine learning and artificial intelligence.

Work Experience

Finatext (December 2017 – Present)
Junior Data Scientist, Fintech

Mime Consulting (July 2017 – October 2017)
Summer Analyst, Data Analytics

Union Bancaire Privée (June 2016 – August 2016)
Summer Analyst, UBP Private Banking – Hedge Funds

Union Bancaire Privée (June 2015 – June 2016)
Junior Analyst, UBP Asset Management – Hedge Funds

Amplify Trading (June 2014 – July 2014)
Summer Analyst, Proprietary Trading

Completed

Coursera – Deep Learning Specialisation

Coursera – Sequence Models

Coursera – Convolutional Neural Networks

Coursera – Structuring Machine Learning Projects

Coursera – Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Coursera – Neural Networks and Deep Learning

Coursera – Machine Learning

Udemy – Python for Data Science and Machine Learning

Edx – CS50 Introduction to Computer Science

Latest Posts

Stanford NLP: Minimum Edit Distance
Stanford NLP: Minimum Edit Distance
Definition Minimum edit distance allows us to assess how similar two strings are. For example, the word 'graffe' has few words that are similar to...
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Key point summary: What AI can and can’t do (yet) for your business
Key point summary: What AI can and can’t do (yet) for your business
This article outlines the limitations of AI and highlights the advances that are poised to counter some of these limitations, creating a new wave of...
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Stanford NLP: Regular Expressions, Tokenisation, Normalisation, Stemming and Sentence Segmentation
Stanford NLP: Regular Expressions, Tokenisation, Normalisation, Stemming and Sentence Segmentation
Regular Expressions A formal language for specifying text strings. Note to use regexpal.com to practically learn about regular expression. Disjunctions Letters inside square brackets [] :...
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TensorFlow Basics – Part 1
TensorFlow Basics – Part 1
Basic Syntax, Graphs, Variables & Placeholders Basic Syntax In [1]: import tensorflow as tf In [2]: print(tf.__version__) 1.3.0 Tensors n-dimensional array In [3]: hello = tf.constant('Hello ') In [4]:...
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Introduction to Neural Networks
Introduction to Neural Networks
Manual creation of Neural Networks In this notebook we will manually build out a neural network that mimics the TensorFlow API. 1. Super() and Object-Oriented...
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Regular Expressions
Regular Expressions
Symbols Identifiers \d - any numbers \D - anything but a number \s - space \S - anything but a space \w - any character...
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Natural Language Processing with NLTK – Part 2
Natural Language Processing with NLTK – Part 2
Text Classification In [26]: import nltk import random from nltk.corpus import movie_reviews In [27]: documents = [(list(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] Shuffling...
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Natural Language Processing with NLTK – Part 1
Natural Language Processing with NLTK – Part 1
Preparation In [1]: import nltk Download all the packages In [2]: #nltk.download() Tokenisation Two types Word tokenisers - separate by words Sentence tokenisers - separate by sentences...
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Basic web scraping with BeautifulSoup4
Basic web scraping with BeautifulSoup4
Introduction BeautifulSoup is a python library for pulling data out of HTML and XML files. It provides idiomatic ways of navigating, searching, and modifying the...
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