Deep Learning. Becoming a Data Scientist: Profiling Cisco’s Data Science Certification Program. Today’s subject matter experts and specialists are tomorrow’s data scientists thanks to Cisco’s Enterprise Data Science Office.
Cisco Systems—a US technology company that develops, manufactures, and sells networking devices and management—has taken a forward-thinking and flexible approach to both finding and retaining talent in the face of rapid advances in machine learning and big data hype. In an interview with Kristen Burton, Director for the Enterprise Data Science Office and Digital Process Transformation, and Justin Norman, Manager of Cisco's Enterprise Data Science Office, I learned about Cisco’s Data Science Certification Program. Now in its 4th year, the continuous education program is helping Cisco develop big data skills in their employees in support of Cisco’s digital transformation. For many companies, Cisco's tactics might serve as a helpful blueprint for developing similar learning plans.
Cisco's Data Science Certification Program Click to expand. Level 1: Associate. Deep Learning. Software. Clustering Algorithms: K-Means, EMC and Affinity Propagation. It’s not a bad time to be a Data Scientist.
Serious people may find interest in you if you turn the conversation towards “Big Data”, and the rest of the party crowd will be intrigued when you mention “Artificial Intelligence” and “Machine Learning”. Even Google thinks you’re not bad, and that you’re getting even better. There are a lot of ‘smart’ algorithms that help data scientists do their wizardry. It may all seem complicated, but if we understand and organize algorithms a bit, it’s not even that hard to find and apply the one that we need. Courses on data mining or machine learning will usually start with clustering, because it is both simple and useful. K-Means Clustering After the necessary introduction, Data Mining courses always continue with K-Means; an effective, widely used, all-around clustering algorithm. The algorithm begins by selecting k points as starting centroids (‘centers’ of clusters). Java (Weka) Python (Scikit-learn) EM Clustering Affinity Propagation In The End…
Approaching (Almost) Any Machine Learning Problem. Abhishek Thakur, a Kaggle Grandmaster, originally published this post here on July 18th, 2016 and kindly gave us permission to cross-post on No Free Hunch An average data scientist deals with loads of data daily.
Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I’ve taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. We will be using python! Before applying the machine learning models, the data must be converted to a tabular form. The machine learning models are then applied to the tabular data. Figure from: A. Or, Technology. AWS simple icons. Amazon Elastic Compute Cloud (EC2) Amazon Simple Storage Service (S3) AWS Storage Gateway Service Amazon Elastic Block Storage (EBS) Amazon Virtual Private Cloud (VPC) Amazon Relational Database Service (RDS) RDS DB Instance Standby (Multi-AZ) RDS DB Instance Read Replica.
Getting Started. We've tried to make KNIME as easy to use as possible.
Below are some resources which may help you to use KNIME. Download Download the right KNIME version for your OS. Installation The installation of KNIME is fairly easy and straight forward: unpack and run. Read more about the license and installation here. Screencasts The screencasts will help you get started with your work using KNIME. Build a Workflow How to build your first workflow, configure and execute nodes, and inspect the results is described here. Workbench User Guide Learn more about the KNIME workbench and how to improve your performance using KNIME. Build a WorkflowHow to build your first workflow, configure and execute nodes, and inspect the results is described here.
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