Ementorhub

Skills you'll gain

Pandas (Python Package)

Data Quality

Data Analysis

Machine Learning

Feature Engineering

Exploratory Data Analysis

Unsupervised Learning

Time Series Analysis and Forecasting

Data Transformation

Regression Analysis

Classification And Regression Tree (CART)

Data Cleansing

Supervised Learning

There are 4 modules in this course

The course is divided into two main sections: In the initial section, participants will delve into the importance of the tabular format in classical machine learning. They will also grasp the distinction between supervised and unsupervised learning, along with common methodologies like classification and regression. The significance of defining the unit of analysis in dataset construction will be highlighted. Moreover, participants will witness demonstrations of data preparation within Driverless AI, showcasing its ability to automate preprocessing tasks and allow customization using Python code.Transitioning to the second section, the course will concentrate on time series data preparation. Fundamental aspects of time series problems will be explored, including the necessity of a date column and understanding the autoregressive nature of such data. The course will also address challenges associated with handling multiple series within a dataset and provide best practices for improving model performance. Jonathan will exemplify dataset preparation and splitting techniques tailored for time series analysis using the capabilities of Driverless AI. Enjoy the learning journey!

Fundamentals of Data Preparation

Advanced Applications

Course Completion Quiz