UCI Data Preprocessing and Exploratory Data Analysis
"Unlocking the Power of Data: Mastering Data Preprocessing and Exploratory Data Analysis for Machine Learning at UCI"
Python,Data & Analytics,Data Science and AI ML
Lectures -5
Duration -34 mins
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Course Description
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.
Goals
You will understand how to evaluate Bard’s responses and check them for accuracy, quality, and relevance using Google Search or other sources
Prerequisites
Students will need a computer/laptop to do the practical implementation.
Curriculum
Check out the detailed breakdown of what’s inside the course
Setting the Foundation: Data Preprocessing and Exploratory Data Analysis
1 Lectures
- Setting the Foundation: Data Preprocessing and Exploratory Data Analysis 01:57 01:57
Accessing Data: UCI Machine Learning Repository
1 Lectures
Converting Categorical Data to Numerical: A Transformation Journey
1 Lectures
Mastering Data Preprocessing and Exploratory Data Analysis: A Hands-On Guide for
1 Lectures
Unveiling Toxicity: Exploratory Data Analysis for Comment Classification
1 Lectures
Instructor Details
AKHIL VYDYULA
Hello, I'm Akhil, an Associate Consultant at Atos India with a focus on the Advisory Consulting practice, specializing in Data and Analytics. My professional journey has led me through various facets of data analysis and modeling, particularly in the BFSI sector, where I've had the privilege of overseeing the full lifecycle of development and execution.
My skill set encompasses a wide range of data-related tasks, including data wrangling, feature engineering, algorithm development, model training, and implementation. I thrive on leveraging data mining techniques such as statistical analysis, hypothesis testing, regression analysis, as well as both unsupervised and supervised machine learning processes to extract meaningful insights and drive data-informed decisions. I'm particularly passionate about risk identification through decision models, and I've honed my expertise in Machine Learning Algorithms, Data/Text Mining techniques, and Data Visualization to effectively address these challenges.
Currently, I'm immersed in an exciting Amazon cloud project that involves end-to-end development of ETL processing. In this role, I craft ETL processing code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, execute scripts using EMR services, and load consolidated data into Postgres SQL (RDS/Redshift) on a full, incremental, and live basis. To streamline this process, I've automated it by creating jobs in Step functions, which trigger EMR instances to run scripts in a specific order and send notifications upon execution status changes. The scheduling of these Step functions is achieved through event bridge rules.
Additionally, I've worked extensively with AWS Glue, using it to replicate source data from on-premises systems to raw-layer S3 buckets via AWS DMS services. One of my key strengths lies in my ability to understand the nuances of data and apply the right transformations to convert data from multiple tables into key-value pairs. Furthermore, I've optimized the performance of stored procedures in Postgres SQL to execute second-level transformations by efficiently joining multiple tables and loading the data into final tables.
I'm passionate about harnessing the power of data to drive actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, feel free to connect with me. Let's explore the endless possibilities that data analytics has to offer!
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