Automated Machine Learning (Automl) With Pycaret

Launchpad for Citizen Data Scientists, Augmented Analysts, Data Storytellers and Insight Translators

What you'll learn

Students will develop a holistic perspective of data complexity, enabling them to approach data-driven challenges

Empower learners to navigate the intricacies of data analysis and machine learning without feeling constrained by tedious or overly technical aspects

Enable students to assess their strengths and weaknesses in data analysis and problem-solving, guiding them to customize their learning journey

Familiarize learners with various problem-solving frameworks that leverage AutoML

Students will gain a comprehensive overview of different AutoML methodologies, allowing them to identify suitable approaches for data analysis and problem-solvi

Requirements

Domain expertise, and familiarity with how machine learning projects are conceived of and executed

Some exposure to computational thinking and basics of python would be helpful

Description

As a Citizen Data Scientist or aspiring Citizen Data Scientist, have you ever looked at the complex realm of data science and found yourself mesmerized by its potential to decode intricate problems, foresee risks, and unlock unprecedented opportunities? You've likely pondered if you too could master this science, armed with the right tools and guidance.As a citizen data scientist, you might have contemplated:Sharing impactful data insights, as data scientists often seek industry-specific knowledge.Expanding your problem-solving abilities beyond the constraints of your current role.Crafting prototypes with the latest AutoML techniques.Envisioning yourself as a proficient problem-solver with an enhanced skill set.But perhaps, you faced frustrating obstacles along the way:Difficulties accessing the right data.Choosing the wrong coding solutions and libraries leads to complex coding challenges.Feeling left behind in the fast-paced world of data science.We at DatOlympia understand these struggles all too well. How do we solve this problem?We've recognized the vital need for simplifying the learning journey. With our expertise in evaluating organizational problem-solving abilities, we have honed our focus on making AutoML accessible to all.Our course is meticulously designed to eliminate frustrations, providing easy-to-retrieve datasets and leveraging the power of Google Colab. We guide you through hands-on notebooks, where you can gradually gain mastery over your data, make informed decisions, and showcase your industry insights.With a comprehensive curriculum designed to strike the perfect balance between simplicity and control, you'll learn to:Effortlessly navigate through the AutoML framework.Propose groundbreaking projects to your business.Build your Minimum Viable Product (MVP) with confidence.Develop an intuitive understanding of data interactions and algorithms.We've successfully guided over a thousand students through our AutoML program, emphasizing an approach that is both beginner-friendly and impactful. Our mission is to ensure that your progress isn't hindered by technical complexities but is empowered by a seamless learning experience.Step into the world of AutoML, where simplicity meets capability, and your data insights take flight. Join us on this transformative journey, and unlock the true potential of your data problem-solving skills.

Overview

Section 1: Introduction

Lecture 1 How this course is organised!

Lecture 2 What is a "Citizen Data Scientist"?

Lecture 3 Which type of Citizen Data Scientist are you?

Lecture 4 Who is the instructor?

Lecture 5 2 keys to maintain learning momentum

Lecture 6 Your first Google Colab Account

Lecture 7 PyCaret's 55 inbuilt datasets

Lecture 8 How to get the most out of this course?

Section 2: Supervised Learning: Regression and Classification

Lecture 9 Introduction to PyCaret

Lecture 10 How is AutoML so accessible to all: the secret

Lecture 11 Regression with PyCaret: the House Dataset (demonstration)

Lecture 12 Classification with PyCaret: the Wine Dataset (demonstration)

Section 3: Unsupervised Learning: Clustering

Lecture 13 What is Clustering?

Lecture 14 Indepth Demonstration: Pokemon

Section 4: Working with Text

Lecture 15 Demonstration 1: Text Classification: Easy

Lecture 16 Demonstration 2: Text Classification: Medium

Lecture 17 Demonstration 3: Capturing nuances in Sentiment Analysis using VaderSentiment

Section 5: Pattern Recognition in Categorical Information

Lecture 18 Introduction to Association Rule Mining

Lecture 19 Efficient Apriori with travel destination information

Section 6: Outliers: Working with Anomalous Data

Lecture 20 What is Anomaly Detection?

Lecture 21 Time Series Anomaly Detection: Apple Stock Data Demonstration

Section 7: Conclusion: Championing an Ecosystem that Empowers Citizen Data Scientists

Lecture 22 Ways to advocate for investment into a citizen data science ecosystem

Lecture 23 Your Citizen Data Science journey

Domain experts with an in-depth knowledge of their respective fields but may lack extensive experience in data science and machine learning,Citizen data scientists from non-technical backgrounds who have a strong interest in data analysis and data-driven problem-solving, and who are seeking to acquire practical data science skills without extensive programming knowledge,Creative thinkers and problem-solving enthusiasts, passionate about uncovering innovative solutions to complex challenges through data analysis,seasoned business strategist seeking to enhance their understanding of data analysis without delving into extensive coding