Data Science Bootcamp: Pay Only After You Get A Data Science Job.

Machine Learning Practical: 6 Real-World Applications

Machine Learning Practical: 6 Real-World Applications

> You will know how real data science project looks like > You will learn how to chain multiple ML algorithms together to achieve the goal > You will learn most advanced Data Visualization techniques with Seaborn and Matplotlib

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Data Science Career Track Prep Course

Data Science Career Track Prep Course

Learn the foundational coding and statistics skills needed to pass our Data Science Career Track admissions challenge.

> Use Python to complete real-world coding exercises and begin your data science journey > Confidently tackle our Data Science Career Track admissions challenge

http://bit.ly/2Xrb4fk

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Real-World Machine Learning Projects with Scikit-Learn

Real-World Machine Learning Projects with Scikit-Learn

> Work with Scikit-Learn's Machine Learning tools to build efficient real-world projects using Scikit-Learn > Use Support Vector Machines to learn how to train your model to predict the chances of heart disease > Understand the buying behavior of your customers using Customer Segmentation to drive the sales of your products

http://bit.ly/2LzHYC9

Applied AI: Artificial Intelligence with IBM Watson

Applied AI: Artificial Intelligence with IBM Watson

> Getting Started with AI using IBM Watson > Building AI Powered Chatbots Without Programming > Python for Data Science and AI >Building AI Applications with Watson APIs

(This is an affiliate link) http://bit.ly/2GzwWeZ

Python for Data Visualization & Fraud Detection Bootcamp

Python for Data Visualization & Fraud Detection Bootcamp

> Make a Credit Card Fraud Detection Model in Python. Learn how to keep your data safe! > Learn TensorFlow and how to build models of linear regression > Understand basic 3D plotting.

http://bit.ly/2OECIwO

Data Science at Scale

Data Science at Scale

> Data Manipulation at Scale: Systems and Algorithms > Practical Predictive Analytics: Models and Methods > Communicating Data Science Results

http://bit.ly/2rH3g7H

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