UCI Data Preprocessing & EDA: A Complete Guide

UCI Data Preprocessing and Exploratory Data Analysis

Unlocking the Power of Data: Mastering Data Preprocessing and Exploratory Data Analysis for Machine Learning at UCI

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Last Updated: 2024-12-27 11:12:38 English Course Type: Self Learning

To create a powerful business vision that will motivate you to succeed.
Setup and configuration of GitHub Copilot with popular programming languages
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You will understand how to evaluate Bard’s responses and check them for accuracy, quality, and relevance using Google Search or other sources

Welcome to the "UCI Data Preprocessing and Exploratory Data Analysis in Machine Learning" course, where we'll dive into the essential steps of preparing and understanding your data for effective machine learning. In this course, we will equip you with the knowledge and techniques necessary to harness the full potential of data in your machine learning endeavors using datasets from the UCI Machine Learning Repository.

Course Highlights:

1. Data Preprocessing Essentials: Begin by learning the critical steps involved in data preprocessing. You'll explore techniques for handling missing data, dealing with outliers, and performing data transformations to ensure the quality and integrity of your datasets.

2. UCI Machine Learning Repository: Gain familiarity with the UCI Machine Learning Repository, a valuable resource for access to a wide range of datasets. Learn how to retrieve, load, and work with datasets from this repository for various machine learning tasks.

3. Exploratory Data Analysis (EDA): Dive into the world of EDA, where you'll uncover hidden patterns and gain valuable insights from your data. Explore data visualization techniques, statistical summaries, and data profiling to understand your datasets thoroughly.

4. Feature Engineering: Discover the art of feature engineering and how to create informative features that improve the predictive power of your machine learning models. You'll learn techniques for selecting, transforming, and creating new features from existing data.

5. Data Preparation for Modeling: Understand the crucial steps of preparing data for machine learning models. This includes data encoding, splitting into training and testing sets, and ensuring that your data is ready for various algorithms.

6. Hands-on Projects: Apply your knowledge through hands-on projects and exercises. Work with real-world datasets from the UCI repository to practice data preprocessing and EDA techniques in the context of practical machine learning problems.

7. Data Visualization: Master data visualization techniques that help you communicate your findings effectively. Create impactful charts and graphs to convey your data-driven insights to stakeholders.

8. Best Practices and Pitfalls: Learn best practices for data preprocessing and EDA, as well as common pitfalls to avoid. Gain insights into how to make informed decisions at each stage of data preparation.

9. Real-world Applications: Explore real-world applications of data preprocessing and EDA across various domains, including healthcare, finance, and marketing. Understand how these techniques are applied to solve complex problems.

10. Preparing for Advanced Machine Learning: Set the stage for advanced machine learning tasks by mastering the fundamentals of data preparation and EDA. You'll be well-prepared to tackle more complex machine learning challenges.

Students will need a computer/laptop to do the practical implementation.

Developers who want to learn about the latest AI-powered tools for code completion, debugging, and more
Good to have (Not Mandatory) access to a Linux System to setup Docker to follow along

Section 1 : Setting the Foundation 1 Lectures 00:01:57
Lecture 1 :
Setting the Foundation: Data Preprocessing and Exploratory Data Analysis Preview
Section 2 : Accessing Data 1 Lectures 00:04:58
Lecture 1 :
Accessing Data: UCI Machine Learning Repository
Section 3 : Data Transformation 1 Lectures 00:08:26
Lecture 1 :
Converting Categorical Data to Numerical: A Transformation Journey
Section 4 : Hands-On Guide 2 Lectures 00:19:25
Lecture 1 :
Mastering Data Preprocessing and Exploratory Data Analysis: A Hands-On Guide
Lecture 2 :
Unveiling Toxicity: Exploratory Data Analysis for Comment Classification

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AKHIL VYDYULA,

284 Course Views

10 Courses

Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.

My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.

I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.

Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.

Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.

I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!