English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 47m | 626 MB
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.
This video course will take a practical approach and cover data science concepts in Go. There will be examples to implement and bring together all the material learned. The entire Go code will be walked through in detail along with the use of popular datasets used by the machine learning community.
What You Will Learn
- Perform data collection and use statistical models to perform data visualization in Go.
- Clean and filter data for data formatting.
- Implement models like Naïve Bayes to work efficiently with high speed in Go.
- Build an end-to-end model like Regression to analyze new data.
- Solving predictive analytics through decision trees model.
- Practical coverage on how to build data science pipeline in Go
- Explore how Go code differs with Python
Table of Contents
GETTING STARTED WITH GO
The Course Overview
Introducing Go for Data Science
Advantages of Go over Python for Data Science
Go Installation
DATA COLLECTION AND CLEANING WITH GO
Working with Static Typing in Go
Data Collection with Go – Let’s Use Twitter Data!
Data Cleaning with Go
Data Storage in JSON/CSV
PERFORM SENTIMENT ANALYSIS
Basic Machine Learning Tasks Like Cross Validation
Natural Language Processing Techniques
Build Naïve Bayes Model
Build Decision Trees
Other Machine Learning Techniques
Evaluation of Models
IMPLEMENTING RECOMMENDATION SYSTEMS IN GO
Different Types of Recommendation Systems
Linear Algebra for Recommendation Systems
Building a Content Recommendation System Using Item Similarity
Building a Collaborative-Filtering-Based Recommendation System
Advanced Recommendation Systems
ANALYZING TIME SERIES WITH STOCK FORECASTING
Analyzing Time Series and Learning Its Fundamentals
Building and Evaluating a Regression Model
Building a Stock Forecasting Model
Testing a Stock Forecasting Model
Understanding Data Visualizations in Go
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