Gain expertise to pre-process, prepare, clean up, extract and engineer new features from the raw and time-series data
Classes : 20 Days : 2 months Duration : Weekdays / Weekends
Real-life data are unstructured and messy. This is the reason why Feature Engineering tasks take approximately 70%-80% of the time in the Machine Learning modelling process.
In this extensive training, you will learn multiple feature engineering methods and techniques to extract and create features from raw data and time-series data that are suitable for forecasting with off-the-shelf regression models like linear regression, random forests, and gradient boosted machines.
You will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models.
You will be trained to use a huge variety of feature selection procedures used worldwide in different organizations and in data science competitions, to select the most predictive features.
At the end of the training, you will have a variety of tools and techniques to select and compare different feature subsets and identify the ones that returns the simplest, yet most predictive machine learning model. This will allow you to minimize the time to put your predictive models into production.
We shall cover the Numpy and Pandas libraries of Python during this training.
This training has been designed by experienced Data Scientists who will help you to understand the WHYs and HOWs of preprocessing.
Experts from the field of Maths, Data Science and Management, each with over 25 years of International experience working in EU/US/Australia
Who this training is for:
- Students who want to get started in pre-processing datasets for time series forecasting
- Data Scientists who want to learn feature engineering techniques for time series forecasting
- Data Scientist who want to improve their coding skills and programming practices for feature engineering
- Data Scientists who want to learn additional feature engineering techniques for time series
Part 1 - EDA: Get insights into your dataset
Part 2 - Data Cleaning: Clean your data based on insights
Part 3 - Data Manipulation: Generating features, subsetting, working with dates, etc.
Part 4 - Feature Engineering
Part 5 - Function writing with Pandas Dataframe
Part 6 - Time Series Data
Part 7 - Debugging
Part 8 - Interview Preparation
I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.
The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.