Python Data Science: Classification Modeling

🌟 Dive into Data Science with Python! 🌟

Course Title: Data Science in Python: Classification Modeling

Your Learning Journey Awaits!

Welcome to the Data Science in Python: Classification Modeling course – your gateway into mastering classification modeling for data science using the versatile Python programming language. This hands-on, project-based experience is meticulously designed to equip you with the essential skills and practical knowledge required to build robust classification models.

Course Overview:

This course starts by introducing you to the core concepts of data science and machine learning, setting the stage for a deep dive into the world of classification algorithms. You'll explore the workflow of a data scientist, understand the types and goals of classification modeling, and get hands-on with data preprocessing, exploratory data analysis (EDA), and feature engineering.

What You'll Learn:

🚀 Intro to Data Science: Get acquainted with the fields of data science and machine learning, and review the essential skills that form the foundation of your data science workflow.

📊 Classification 101: Learn the ins and outs of classification, including key terms, types, and the modeling workflow.

🔍 Pre-Modeling Data Prep & EDA: Master the steps required for effective data preparation and exploratory data analysis to understand your datasets.

📈 K-Nearest Neighbors (KNN): Dive into the KNN algorithm, learning how to build and classify data points using Python.

📉 Logistic Regression: Grasp the mathematical underpinnings of logistic regression and practice fitting models, tuning parameters, and understanding outcomes.

🎯 Classification Metrics: Learn to evaluate classification models using precision, recall, F1 score, and ROC-AUC.

🌳 Imbalanced Data: Tackle the common challenge of imbalanced datasets by learning strategies to enhance your model's performance in these scenarios.

🌲 Decision Trees: Construct and assess decision tree models, which are adept at categorizing data based on various splits.

🌱 Ensemble Models: Venture into the realm of ensemble models, including random forests and gradient boosted machines, to combine the power of multiple models.

What's Included?

Joining this course grants you immediate, LIFETIME access to:

• 🎬 9.5 hours of high-quality video lectures
• 📋 18 homework assignments to solidify your learning
• 🤔 9 quizzes to test your knowledge
• ✍️ 2 comprehensive projects for real-world application
• 📚 Data Science in Python: Classification ebook (250+ pages) packed with in-depth information and insights
• 🖥️ Downloadable project files & solutions to assist your learning journey
• 🤔 Expert support and Q&A forum for all your queries
• 💯 30-day Udemy satisfaction guarantee for peace of mind

Who is this for?

If you're an aspiring data scientist eager to delve into the exciting world of classification modeling with Python, this is the course for you! Whether you're a beginner looking for your first introduction or someone aiming to sharpen their skills, this comprehensive course will guide you through each step with clarity and depth.

Ready to Transform Your Data into Insights?

Embark on your data science journey today with Data Science in Python: Classification Modeling. With expert guidance from Chris Bruehl at Maven Analytics, you'll unlock the potential of Python and become adept at crafting models that make sense of complex datasets.

👨‍💻 Happy learning! 👩‍💻

-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)