Hands-On Fundamentals of Data Science with Go [Video]

Hands-On Fundamentals of Data Science with Go

Hands-On Fundamentals of Data Science with Go [Video]

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 47m | 626 MB
eLearning | Skill level: All Levels

Hands-On Fundamentals of Data Science with Go [Video]: Learn the Fundamentals of Data Science with Go

Go (also known as Golang), created at Google, is increasingly proving to be faster, easy to code in, highly efficient and concurrent programming languages. It is the next-gen language of data science, machine learning and AI in general – as it strikes a great balance between productivity and maintainability of code. Many data scientists prototype models, which are then deployed to production by someone else, Go will allow you to do both! In these videos, you will get complete hands-on guidance on how to perform data mining, natural language processing, machine learning, linear algebra and understand in detail how you can use these to boost data science projects in your teams using Go. You will gain practical coverage on how to do data collection, data cleaning and mining, use of statistical models for analysis and data visualization. You will also get to use cutting-edge libraries in Go, and use them with popular datasets used by the machine learning community. The course would also guides you to build real-life hands on projects like twitter bot which tweets on your behalf, sentiment analysis on movie reviews using Naïve Bayes and decision trees, two different kinds of recommendation systems to recommend movies and a regression model to perform stock prices forecasting, along with performing your own data visualizations in Go.

You will get a complete hold on the use of statistics, linear algebra and understand in detail how you can boost your data science using Go. You will gain practical coverage on how to do data collection, data sanitation and munging, the use of statistical models for analysis and data visualization. The video would also get you couple with the fundamentals of machine learning along with a quick run through in implementing models such as Decision Trees, Naive Bayes, SVM and so on. You will also get to know how you can work with big data processing tools like Apache Spark and Kafka in your data science projects. The course would also get you through a couple of examples like recommendation system, sentimental analysis and stock prices forecasting.

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