25pg-Financial Machine-Learning w'Python
Budget
250$
per month
Posted: 5 years ago
Opened
- Description
- “Are you passionate and well-researched about Data Science- Financial Machine Learning with Python?
We’re looking to hire a “great” writer to do a short 25 page/10,000 word Kindle-formatted mini report to give to our readers online.
The ideal writer would preferably be from the US, UK, Canada, Australia or New Zealand.
Please read this content brief to the end, every detail is important and we’ll be checking against this brief when accepting or declining the content.
We’re keen for fluff-free writing please! :) And obviously we don’t accept any form of plagiarism. Everything you hand in must be unique content from your own research, thinking and creativity.
Here below is the Title, Description of the report, and the Information to be included as practical steps.
Before payment we would expect a bullet-form Outline of what you’ve written, and a brief Description. We’re keen to see that you are an authority, knowledgeable, super passionate, and have done the research.
This needs to be done ASAP. Thank you! Kristie Lee”
TITLE:
Data Science - Financial Machine Learning with Python
DESCRIPTION:
25 pg Report
The goal of the Report is to give a CLEAR and CONCISE explanation of Data Science - Financial Machine Learning with Python in (20+) clear Points/Tips; providing a hands-on and high-level introduction to the statistical concepts found in Machine Learning.
Where possible, you will be utilizing interrelated Exercises/Projects for the practical application of concepts, and including sample code. You will be giving clear explanations and visual examples, and providing code templates.
This is for people (probably men?) who already have an intermediate to advanced level of skill with Python.
INFO TO INCLUDE:
Understanding the difference between Machine Learning & Statistical Data Analysis
Intro to Data Science - Financial Machine learning with Python - (ML)- examples of tasks currently being performed by ML
ML - A “disruptive technology” - why?
In today’s world of finances - ML for investment opportunities - how this will create strong added value to your business
Different ML models and combining them to solve problems
How to create ML algorithms in Python and R, with clear explanations and visual examples ,providing code templates
How to structure big data so it works in ML algorithms;
How to do research with ML algorithms on that data
Making accurate predictions- creating trend lines- using linear regression, polynomial regression, and multivariate regression - backtesting your discoveries while avoiding false positives. Bias.
Using supercomputing methods -Implementing machine learning at massive scale with Apache Spark's MLLib
Scikit-Learn, Keras and TensorFlow explained- Tools, How-to's for building functioning systems
Dimensionality Reduction
Reinforcement Learning, NLP and Deep Learning - classifying images, data, and sentiments
Artificial intelligence, and neural networks
H2o Framework for deep neural networks
Classifying data using K-means clustering and k-nearest neighbors, support vector machines (SVM), KNN, decision trees, naive bayes, and PCA
Preparing data for analysis, use train/test and K-Fold cross validation to choose and tune your models
Clean your input data to remove outliers
Design and evaluate A/B tests using T-Tests and P-Values
Cross Validation, Data Scrubbing and Ensemble Modeling.
One-hot encoding, binning and dealing with missing data
How to build a Machine Learning Model to predict house values using Python - Build a computer game recommender system using item-based and user-based collaborative filtering
Skills:
Python,Algorithms,Analytics,Cluster Analysis,Data Modeling,Machine Learning,Data Visualization,Business Intelligence / Analysis
Working conditions:
250
- Category
Source: guru.com
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