http://datacamp-community-prod.s3.amazonaws.com/af9bb467-170d-41c9-a0bd-26e675384c4e

Make your own neural networks with this Keras cheat sheet to deep learning in Python for beginners, with code samples!

Keras Cheat Sheet: Neural Networks in Python

http://datacamp-community-prod.s3.amazonaws.com/eb807da5-dce5-4b97-a54d-74e89f14266b

A handy scikit-learn cheat sheet to machine learning with Python, including steps to implement machine learning algorithms successfully and code samples. 

Scikit-Learn Cheat Sheet: Python Machine Learning

https://www.datacamp.com/community/podcast/scaling-machine-learning-adoption-a-pragmatic-approach

<p>In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs.&nbsp;</p>

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs. 

Scaling Machine Learning Adoption: A Pragmatic Approach

https://www.datacamp.com/community/blog/scaling-machine-learning-adoption-a-pragmatic-approach

https://www.datacamp.com/community/podcast/the-future-of-responsible-ai

<p>In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.</p>

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.

The Future of Responsible AI

https://www.datacamp.com/community/blog/the-future-of-responsible-ai

https://www.datacamp.com/community/news/data-science-gambling-and-bookmaking-with-marco-blume-h3szzd7jfyl

Marco Blume, Trading Director at Pinnacle Sports talks about the role of data science in large-scale bets and bookmaking, how Marco is training an army of data scientists and much more.

Data Science, Gambling and Bookmaking (with Marco Blume)

https://www.datacamp.com/community/news/managing-data-science-teams-with-angela-bassa-nktiamvwi6

Angela Bassa, the Director of Data Science at iRobot, talks about managing data science teams and much more.

Managing Data Science Teams (with Angela Bassa)

https://www.datacamp.com/community/news/k-means-clustering-in-r-tutorial-cq3latvdphb

Learn all about clustering and, more specifically, k-means in this R Tutorial, where you'll focus on a case study with Uber data.

K-Means Clustering in R Tutorial

https://www.datacamp.com/community/news/the-10-statistical-techniques-data-scientists-need-to-master-ed6o27bk1b9

In this article, I present 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.

The 10 Statistical Techniques Data Scientists Need to Master

<p>Today, high-performing insurance organizations are leveraging data science to advance their businesses in a competitive environment. In a <a target="_blank" href="https://www.datacamp.com/resources/webinars/future-of-data-science-in-insurance-1">recent webinar</a>, Sudaman Thoppan Mohanchandralal, Regional Chief Data and Analytics Officer at Allianz Benelux discussed the current state of data science in insurance and exciting use-cases of machine learning in the industry.     </p>
<h1 id="why-data-science-is-valuable-in-the-insurance-industry-today">Why Data Science is Valuable in the Insurance Industry Today</h1>
<p>Recently, the industry’s demand has shifted to more personalized services rather than those that appeal to mass markets. This <a target="_blank" href="https://www.mckinsey.com/industries/financial-services/our-insights/how-traditional-insurance-carriers-can-disrupt-through-personalized-marketing">McKinsey report</a> describes this shift Sudaman refers to. It explains how deep one-to-one relationships can be built with consumers at scale through strategic personalized marketing and service offerings.</p>
<p>These changes in needs and relationships are made possible by the massive growth of available data for individual customers as well as advances in machine learning and resources. In <a target="_blank" href="https://www.ibm.com/thought-leadership/institute-business-value/report/insurance-data-personalization">this article</a>, IBM discusses how insurers observe a 22-25% improvement in time to profitability when creating personalized services relative to mass-appealing products. These products can only be developed at scale using machine learning.  </p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1638880876/image3_lya3kn.png" /></center><br>
<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>
<h1 id="the-state-of-data-science-in-insurance">The state of data science in insurance</h1>
<p>Sudaman explains in the webinar that machine learning provides value when it is leveraged for predictions rather than causal inference and when the problem is sufficiently self-contained. As of today, machine learning fails when organizations attempt to use these algorithms as a crystal ball or black box rather than a tool to inform humans on directions to explore. Here’s a glimpse of some of the use-cases covered in the webinar:</p>
<h3 id="1-fraud-detection-in-insurance-claims">1. Fraud Detection in insurance Claims</h3>
<p>Machine learning performs very well on prediction tasks with a lot of data. Therefore, <a target="_blank" href="https://www.datacamp.com/courses/fraud-detection-in-python">fraud detection</a> is a great use case to train and deploy a classification algorithm such as logistic regression or a decision tree. This system can be used to flag claims that look suspicious. This makes fraud prevention significantly more manageable for an analyst as it will augment their workflow by surfacing the claims most likely to be fraudulent.</p>
<h3 id="2-identifying-customers-for-retention-activities">2. Identifying Customers for Retention Activities</h3>
<p>Another important use case for improving the scale of any organization is improving <a target="_blank" href="https://www.datacamp.com/courses/machine-learning-for-marketing-in-python">customer retention</a>. Machine learning can help to identify which customers are at risk of leaving your organization. Identifying these customers at scale and providing incentives for them to stay will lead to better customer lifetime value in the long run—enabling deeper personalization.</p>
<h3 id="3-optimizing-pricing-with-time-series-data-risk-premium-modeling-">3. Optimizing Pricing with Time Series Data (Risk Premium Modeling)</h3>
<p>Understanding a customer’s risk to file a claim is critical in determining the correct price and structure of a plan to provide them. All insurers are doing this at some level. With the expansion of available data, model accuracy to determine a customer’s risk profile can be improved to offer more <a target="_blank" href="https://www.datacamp.com/courses/life-insurance-products-valuation-in-r">competitive pricing</a> to individuals.</p>
<h3 id="4-future-disease-prediction">4. Future Disease Prediction</h3>
<p>Similar to the previous use case, understanding future disease risks is critical in portfolio optimization and pricing. <a target="_blank" href="https://www.lloyds.com/~/media/files/news-and-insight/coronavirus-hub/product-simplification-report/lloyds_product_simplification_report_final.pdf?la=en">This Lloyd’s of London report</a> discusses how the insurance industry will have to pay out upwards of $100 billion in COVID-related claims. While predicting the pandemic in advance would have been challenging, understanding and predicting health outcomes will enable insurers to prioritize and personalize their services.</p>
<h3 id="5-portfolio-optimization">5. Portfolio Optimization</h3>
<p>The final use case we will discuss is portfolio optimization. <a href="https://www.datacamp.com/courses/introduction-to-portfolio-risk-management-in-python">Portfolio management</a> consists of a few steps: identifying meaningful groupings of risk to be analyzed, informing optimal decisions, and identifying opportunities. Each of these subtasks is solvable through data science optimization techniques.</p>
<p>If you want to learn more about the future of data science in insurance, make sure to tune in to the webinar <a target="_blank" href="https://www.datacamp.com/resources/webinars/future-of-data-science-in-insurance-1">here</a>.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>

https://www.datacamp.com/community/blog/data-science-in-insurance-today

The insurance industry is rife with data and data science use-cases that provide value. In a recent webinar, Allianz Benelux Regional Chief Data & Analytics Officer Sudaman T M explored the state of data science in the industry today.

Data Science in Insurance Today

https://www.datacamp.com/community/blog/scaling-ai-adoption-in-financial-services

In this episode of DataFramed, we speak with Shameek Kundu,  Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.

Scaling AI Adoption in Financial Services

<h2>Introduction to Python for Data Analysis</h2><p>This chapter will get you started with Python for Data Analysis. We will cover the reasons to learn Data Science using Python, provide an overview of the Python ecosystem and get you to write your first code in Python!</p><h2>Python Libraries and data structures</h2><p>In this chapter, we will introduce some of the most common data structures in Python to you and take you through some of the libraries we commonly use in data analysis.</p><h2>Exploratory analysis in Python using Pandas</h2><p>We start with the first step of data analysis - the exploratory data analysis.</p><h2>Data Munging in Python using Pandas</h2><p>Pandas is at the heart of data analysis in Python. This chapter gets you started with Data Munging in Python using Pandas</p><h2>Building a Predictive model in Python</h2><p>We build our predictive models and make submissions to the AV DataHack platform in this section.</p><h2>Expert advice to improve model performance</h2><p>This chapter will help to understand the approach of data science experts, "How they do approach a challenge?", "How to select a right algorithm?", "How to combine outputs of multiple algorithms?" and "How to select the right value of model parameter also known as parameter tuning?".</p>

https://www.datacamp.com/community/open-courses/introduction-to-python-machine-learning-with-analytics-vidhya-hackathons

This course introduces you to data science concepts, data exploration, and preparation in Python to prepare you to participate in machine learning competitions on Analytics Vidhya.

Introduction to Python & Machine Learning (with Analytics Vidhya Hackathons)

<h2>Getting Started with Python</h2><p>In this chapter we will go trough the essential steps that you will need to take before beginning to build predictive models.</p><h2>Predicting with Decision Trees</h2><p>After making your first predictions in the previous chapter, it's time to bring you to the next level. In chapter 2 you</p><h2>Improving your predictions through Random Forests</h2><p>What techniques can you use to improve your predictions even more? One possible way is by making use of the machine learning method Random Forest. Namely, a forest is just a collection of trees...</p>

https://www.datacamp.com/community/open-courses/kaggle-python-tutorial-on-machine-learning

Always wanted to compete in a Kaggle competition but not sure you have the right skill set? In this course, you'll learn to predict survival rate for Kaggle's Titanic competition.

Kaggle Python Tutorial on Machine Learning

https://www.datacamp.com/community/tutorials/guide-for-automating-ml-workflows-using-pycaret

PyCaret Tutorial: A beginner's guide for automating ML workflows using PyCaret

https://www.datacamp.com/community/tutorials/deep-learning-python

This Keras tutorial introduces you to deep learning in Python: learn to preprocess your data, model, evaluate and optimize neural networks. 

This KERAS TUTORIAL introduces you to DEEP LEARNING in Python. Learn to PREPROCESS your data, MODEL, evaluate and optimize NEURAL NETWORKS. 

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