Scikit-Learn Tips And Tricks

Master Scikit-Learn for Real-World ML

What you'll learn

Master the art of creating efficient pipelines for your machine learning models, and streamline your workflow to save time and improve productivity.

Acquire a comprehensive knowledge of the various ML tools available at your disposal, and learn how to leverage them to gain a competitive edge in the field.

Familiarize yourself with the best practices and industry standards in machine learning, and develop the skills to build robust and scalable ML models.

This course will equipt you with the skills and knowledge to validate your ML models with confidence.

Explore advanced techniques for optimizing and fine-tuning your ML models, taking your data analysis to the next level.

Requirements

Basic understanding of machine learning models in scikit-learn.

Description

If you're a data scientist looking to take your machine learning skills to the next level, this course is for you. Unlike other courses that cover a broad range of topics, this course is specifically designed to provide you with a comprehensive understanding of Scikit-Learn and its most useful features.  In addition to covering the basics of Scikit-Learn, this course will dive deep into topics such as cross-validation techniques, customized metrics, hyperparameter tuning, feature engineering, and pipelines. You'll not only learn how to build models but also how to optimize them for real-world applications.As someone who struggled to find the right course on Scikit-Learn, I created this course with the intention of filling the gap and providing a resource that I wished I had access to. By the end of this course, you'll have a mastery of Scikit-Learn that will set you apart as a skilled and knowledgeable data scientist. Whether you're just starting out or you're an experienced practitioner, this course has something for everyone. Join me on this exciting journey to master Scikit-Learn and take your machine learning skills to the next level!Throughout this course, you'll learn many tips and tricks for working with Scikit-Learn that are often overlooked in other courses. For example, you'll learn how to use pipelines to streamline your machine learning workflow and ensure that your data is processed consistently. You'll also learn how to use custom metrics to evaluate the performance of your models more effectively, and how to use hyperparameter tuning to optimize your model parameters for better performance. Additionally, you'll learn advanced techniques for feature engineering, including creating interaction terms and polynomial features, as well as for dealing with missing data. By the end of this course, you'll not only have a deep understanding of Scikit-Learn but also a toolbox of techniques and strategies for building better machine learning models.

Overview

Section 1: Course Overview

Lecture 1 Introduction

Lecture 2 Course Materials

Section 2: data in Scikit-Learn

Lecture 3 Toy Datasets

Lecture 4 Real World Data

Lecture 5 Generating Data

Section 3: model selection and validation

Lecture 6 Cross Validation Part 1

Lecture 7 Cross Validation Part 2

Lecture 8 Metrics and Scoring Part 1

Lecture 9 Metrics and Scoring Part 2

Lecture 10 Tuning Hyperparameters

Section 4: Feature Engineering

Lecture 11 Feature Engineering Part 1

Lecture 12 Feature Engineering Part 2

Lecture 13 Feature Engineering Part 3

Lecture 14 Feature Engineering Part 4

Section 5: Pipelines

Lecture 15 Pipelines Part A

Lecture 16 Pipelines Part B

This course is designed for both beginner and advanced learners who are interested in expanding their knowledge and understanding of scikit-learn. Whether you're just starting out in data science or looking to enhance your existing skillset, this course is perfect for anyone looking to gain a deeper understanding of the many available and less utilized functionalities in scikit-learn.,This course is ideal for data scientists, researchers, and analysts looking to advance their skills and knowledge of machine learning models in scikit-learn. Our course content is specifically designed to meet the needs of those who want to go beyond the basics and gain a more comprehensive understanding of this powerful tool. Whether you're looking to build more complex models, optimize your existing pipelines, or validate your results with confidence, this course has something for everyone who is serious about their data analysis skills.