You know your business will be transformed by AI soon. That is why you’re here.
What you might not know is how to get started, what AI can be used for in your business, or how to implement a particular machine learning algorithm. No need to worry: We have 130 AI and Machine Learning experts to help you.
Our Getting Started with AI package is designed to show you how you can use artificial intelligence to automate, maximise efficiencies and generate value within your company.
1. Structure data
Understand what different data sources can be merged.
Learn how to built an integrated database.
Get data engineering advice.
2. Identify Opportunities
How can the machine learning algorithm help?
Understand what questions can be answered with machine learning.
Identify the processes that can be automated with machine learning.
3. Learn & Optimize
Become self-sufficient in using data science and machine learning in your business.
Clear up the jargon: What is the difference between machine learning & data science?
A portfolio of powerful machine learning projects to choose from for your organization.
A roadmap of how to get started with machine learning and AI.
Tools for Machine Learning & Data Science:
R, Matlab, Python: scikit-learn library
Are you making the most of your data? Work with a machine learning expert on-demand.
We are here to help you innovate by providing you with access to an exclusive network of data scientists.
There is currently much uncertainty about what constitutes a good data scientist. At the same time, organizations need to harness the power of their data in order to stay competitive. Brainpool wants to fill that gap.
We are a network of experienced data scientists from world-class institutes who are ready to help you in this journey.
For Data Scientists
Brainpool provides top academics and experienced professionals in data science with a platform to locate exciting industry projects on contract basis. Network with other experts.
Be an expert adviser for our clients who are working on everything ranging from recommendation engines to deep learning problems, optimization problems, data visualizations and social network analysis, to name a few.
Receive great compensation for high quality work. Immediate rewards. We encourage outside-the-box thinking among academics.
Unlock consistent alpha by finding hidden patterns in financial time series.
Build supervised algorithms that predict future prices and liquidity
movements, by extracting hidden features within data. Building
flexible models that learn from new trends by adapting to the free
flow market information. Determine assets that are mispriced as
found in niche markets. Risk Management:
Improve the accuracy of risk modelling by identifying key data
features and nonlinear patterns within large datasets. Enhance
your decision making with well-trained deep learning models,
that ensure a more efficient means to manage risk. Build early
warning systems that automate reporting, portfolio monitoring,
and contingency plans. Portfolio Optimization:
Construct well-diversified portfolios using unsupervised learning,
such as cluster analysis to identify assets with similar
characteristics, and reinforcement learning to allocate capital
dynamically in an ever changing environment. Use regularisation
and semi-supervised learning for portfolios with label data more
expensive to assemble. Natural Language Processing (NLP):
Make use of NLP to more intricately assess value in investment
strategies such as long/short equity and discretionary with models
that more accurately gauge market sentiment. Provide highly relevant
answers to questions related to financial planning. Fraud Detection and Identity Management:
Machine learning in fraud detection has become the go-to technology
for classification of fraudulent behaviour. Supervised learning methods
such as neural networks have shown to be very effective at detecting
fraud when trained on a large amount of financial statement data. Credit-rating:
Determine the credit-rating of a borrower from social media activities
and transaction history. Build new credit markets with customer and
clients that lack a long-term credit history. Use supervised learning
to more accurately assess companies’ credit scores, e.g., using neural
Improve customer segments and targeted advertising using machine
learning segmentation methods (e.g. cluster analysis, k-means,
Nearest Neighbour). Classify customers using Supervised Learning
Models, find new audiences using recommendation systems increase
efficiency of your media spend. Behavioural Analysis:
Find patterns in the way customers interact with your brand by using
predictive modelling and forecasting. Optimise conversions, increase
customer satisfaction. Social Media - Early Opportunity Detection:
Analyse real-time Twitter and Facebook data streams to capture current
sentiments with respect to brands, products or adverts. Get a head
start on sentiment outbreaks to uncover important opportunities and
avoid PR crises. Sales and Marketing Integration:
Build an easy to navigate interface to measure an integrated impact of
sales teams and marketing campaigns. Track direct correlation between
media budgets and number of products sold. Influencers Strategy:
Improve efficiency of your campaigns using social network analysis.
Find audience most susceptible to the message, and use this audience
to amplify the impact of your campaigns. Find and target. Implicit Survey Design:
Improve accuracy of surveys by using established psychological tools
and test instruments, such as gamification. Learn about your audience
to make informed decisions. Neuroscience (Beta):
Optimise website usability and impact using eye tracking methods to.
Investigate consumer’s perception of adverts using brain imagining
Build self-learning models that predict sales, help increase sales
revenue, and reduce storage costs. Using Linear Latent Variable
Models (LAVA) and/or Elastic Nets to estimate the latent factors
that highlight customers purchasing behaviour. Big Data Analytics and Visualisation:
Systematic analysis of big data is crucial when exploring
under-performing streams of sell revenue. By deploying a combination
of large scale analytics and data visualisation we can illuminate
hidden campaign strategies, such as cross sales, which will alleviate
such poorly performing skus. Supply Chain:
Implement statistical models with demand and supply uncertainty
features that are inherent to the supply chain process. The
perturbation of these model treat hidden externalities and
generate a robust toolkit for modelling supply chain. Some
additional areas where machine learning could help you’re your
business is planning group problems, optimising stock levels,
and warehouse automation. Backtesting Campaign Strategies:
Campaigns can be costly if they are not implemented correctly, and
thoroughly backtested. Finely tuning tune back-testing models will
help build a well-constructed cost effective campaign strategies,
giving management at all levels the details and implications for
deployment. Targeted Campaign and Retail Segmentation:
Have a nuanced view of public opinion and target customers more
accurately with Multi-level Regression and Poststratification (MRP).
Create retail segmentation with artificial neural networks (ANNs)
giving you a better understanding of your customers shopping habits.
Improve your customer service with specialized chat bots that interact
with patients through chat windows. Automate scheduling follow-up
appointments with patients. Minimise human error by ensuring they are
directed to the appropriate healthcare department, and reduce kpi times. Disease Identification/Diagnosis:
Build state of the art classification algorithm for diagnosing patients
based on mere mobile phone photos. Identify rare diseases with learning
algorithms such as functional-gradient boosting (FGB), which self-report
behavioural data to allow distinguishing between people with rare and
more common chronic illnesses. Personalized Treatment:
Supervised learning will allow physicians to select from more limited
sets of diagnoses. An example of this is the estimation of patient risk
factors relative to symptoms and genetic information. Such models can be
calibrated and trained on micro biosensors and mobile phone applications
which will give more sophisticated health data to assess treatment efficacy.
Reduce treatment cost and optimize individual patent health. Drug Discovery:
Machine learning in early-stage drug discovery can be used to estimate the
success rate of initial screening of drug compounds relative to biological
factors. The application of unsupervised learning (k-nearest neighbor
algorithm) to precision medicine has identified mechanisms in multi-factor
diseases, and created alternative treatments and therapies. Clinical Trial Research:
Selecting and identifying ideal candidates for clinical trails by sampling
from a broader range of data to find features that are currently underutilised,
an example of this could be social media and number of doctor visits. Use
machine learning to improve the safety of the trialists by monitoring their
health in real-time remotely. Epidemic Outbreak Prediction:
The monitoring and predicting of epidemic outbreaks has been performed
successful by machine learning technologies for a number of years now.
Collecting vast amounts of data from satellites, historical healthcare
databases, and social media; one can train support vector machines and deep
neural networks potential outbreaks such as malaria and ebola.
Who we work with
Brainpool gave us the expertise we needed to complete an urgent project for one of our biggest clients. It is our go-to place for AI and Machine Learning experts.