Ementorhub

What you'll learn

Prepare data for further analysis by removing noise, identifying outliers, & merging data from multiple sources

Create and evaluate features for machine learning applications

Explore special techniques for handling textual, audio, & image data

Perform unsupervised machine learning

Skills you'll gain

Unstructured Data

Data Processing

Natural Language Processing

Text Mining

Matlab

Data Cleansing

Exploratory Data Analysis

Feature Engineering

Data Transformation

Data Manipulation

Data Visualization

Histogram

Image Analysis

Data Integration

Dimensionality Reduction

Predictive Modeling

Descriptive Statistics

There are 5 modules in this course

These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed Exploratory Data Analysis with MATLAB. Throughout the course, you will merge data from different data sets and handle common scenarios, such as missing data. In the last module of the course, you will explore special techniques for handling textual, audio, and image data, which are common in data science and more advanced modeling. By the end of this course, you will learn how to visualize your data, clean it up and arrange it for analysis, and identify the qualities necessary to answer your questions. You will be able to visualize the distribution of your data and use visual inspection to address artifacts that affect accurate modeling.

Organizing Your Data

Cleaning Your Data

Finding Features that Matter

Domain-Specific Feature Engineering