Here is What You'll Learn
Understand data extraction, exploration and visualization
There are no shortcuts for data exploration. We cover several data exploration aspects, including missing value imputation, outlier removal and the art of feature engineering
Learn how to prepare data for ML algorithms
Data preparation may be one of the most difficult steps in any machine learning project. The reason is that each dataset is different and highly specific to the project. Nevertheless, there are enough commonalities across predictive modeling projects that we can define a loose sequence of steps and subtasks that you are likely to perform.
Learn how to train and finetune models
There are mainly three different ways in which a pre-trained model can be re-purposed. They are, Feature extraction . Copy the architecture of a pre-trained network. Freeze some layers and train the others.
Learn how to deploy into a serialized object for future use
Serialization is a process 'to arrange in a series and broadcast it to the outer world'. We send a serializing object to the network stream and publish or send it to a directory to store its form for the future use.
Open Q&A and Networking
Lastly, get all our queries resolved with an open Q&A session.
About Fatos Ismali
Fatos is a passionate technologist bringing with him a wealth of experience from both the start-up and corporate worlds. He has a huge interest in Deep Learning and Data Engineering and has seen himself apply his skills in industries such as Financial Services, Retail, Public Sector, Biomass Energy Production, Media and Publishing. Fatos holds a BSc in Computer Science and a Master’s in Data Warehouses and Business Intelligence. He has worked for Oracle as a Cloud Architect and now works for Microsoft as a Data Solutions Architect focusing on Data & AI. He is the founder of one of the biggest Data Science communities in London - Data Science Initiative.
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