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Self-paced course
Data Science in Python: Classification
Data Science in Python: Classification
Rating 4.7
29 reviews
29 reviews
29 reviews
Course Description
This is a hands-on, project-based course designed to help you master the foundations for classification modeling in Python.
We’ll start by reviewing the data science workflow, discussing the primary goals & types of classification algorithms, and do a deep dive into the classification modeling steps we’ll be using throughout the course.
You’ll learn to perform exploratory data analysis, leverage feature engineering techniques like scaling, dummy variables, and binning, and prepare data for modeling by splitting it into train, test, and validation datasets.
From there, we’ll fit K-Nearest Neighbors & Logistic Regression models, and build an intuition for interpreting their coefficients and evaluating their performance using tools like confusion matrices and metrics like accuracy, precision, and recall. We’ll also cover techniques for modeling imbalanced data, including threshold tuning, sampling methods like oversampling & SMOTE, and adjusting class weights in the model cost function.
Throughout the course, you’ll play the role of Data Scientist for the risk management department at Maven National Bank. Using the skills you learn throughout the course, you’ll use Python to explore their data and build classification models to accurately determine which customers have high, medium, and low credit risk based on their profiles.
Last but not least, you’ll learn to build and evaluate decision tree models for classification. You’ll fit, visualize, and fine tune these models using Python, then apply your knowledge to more advanced ensemble models like random forests and gradient boosted machines.
If you’re an aspiring data scientist looking for an introduction to the world of classification modeling with Python, this is the course for you.
Course Description
This is a hands-on, project-based course designed to help you master the foundations for classification modeling in Python.
We’ll start by reviewing the data science workflow, discussing the primary goals & types of classification algorithms, and do a deep dive into the classification modeling steps we’ll be using throughout the course.
You’ll learn to perform exploratory data analysis, leverage feature engineering techniques like scaling, dummy variables, and binning, and prepare data for modeling by splitting it into train, test, and validation datasets.
From there, we’ll fit K-Nearest Neighbors & Logistic Regression models, and build an intuition for interpreting their coefficients and evaluating their performance using tools like confusion matrices and metrics like accuracy, precision, and recall. We’ll also cover techniques for modeling imbalanced data, including threshold tuning, sampling methods like oversampling & SMOTE, and adjusting class weights in the model cost function.
Throughout the course, you’ll play the role of Data Scientist for the risk management department at Maven National Bank. Using the skills you learn throughout the course, you’ll use Python to explore their data and build classification models to accurately determine which customers have high, medium, and low credit risk based on their profiles.
Last but not least, you’ll learn to build and evaluate decision tree models for classification. You’ll fit, visualize, and fine tune these models using Python, then apply your knowledge to more advanced ensemble models like random forests and gradient boosted machines.
If you’re an aspiring data scientist looking for an introduction to the world of classification modeling with Python, this is the course for you.
Course Content
24.0 video hours
Skills you'll learn in this course
Build classifiers from logistic regression to random forests
Handle class imbalance, tuning & cross‑validation like a pro
Interpret feature importance & model metrics for decisions
Deploy Python classifiers in real‑world case studies end‑to‑end
Meet your instructors
Chris Bruehl
Analytics Engineer & Lead Python Instructor
Chris is a Python expert, certified Statistical Business Analyst, and seasoned Data Scientist, having held senior-level roles at large insurance firms and financial service companies. He earned a Masters in Analytics at NC State's Institute for Advanced Analytics, where he founded the IAA Python Programming club.
Student reviews
Maven Analytics has once again delivered an exceptional educational experience with their "Data Science in Python: Classification" course. This course stands out because of its detailed and structured approach, making complex topics accessible and engaging for learners at all levels. Detailed Curriculum and Structure From the outset, the course impresses with its well-organized curriculum. The curriculum impresses from the outset with its methodically laid out topics, comprehensively covering essential classification methods. Each section builds on the previous one, ensuring a smooth learning curve. The practical examples and hands-on exercises reinforce theoretical knowledge, facilitating a deeper understanding of classification in Python. Expert Instruction and Clarity Chris Bruel, the course instructor, demonstrates a high level of expertise and passion for teaching. His explanations are clear and concise, making even the most complex concepts easy to grasp. The step-by-step guidance through the assignment solutions walkthrough really helps you to verify your approach to the assignments. Engaging Learning Experience What sets this course apart is its engaging format. The datasets for projects and assignments adds to the learning process. The interactive nature of the course, coupled with quizzes and assessments, keeps learners motivated and invested in their progress. Conclusion Maven Analytics' "Data Science in Python: Classification" course is a must-take for anyone looking to master classification techniques using Python. Its comprehensive curriculum, expert instruction, and engaging format make it a standout choice. Whether you're a beginner or an experienced professional, this course will significantly enhance your data science skills.
Andrew Hubbard
I truly enjoyed taking this course; it was an enriching experience that surpassed my expectations. Like any other course I've taken with Chris and Maven, this one has significantly fine-tuned my understanding of machine learning concepts. Chris's teaching style is exceptional; he has a unique ability to simplify even the most complex topics and convey the message in an easy-to-comprehend manner. Throughout the course, I found myself engaged and motivated to learn more, thanks to Chris's clear explanations and insightful examples. The content was well-structured and comprehensive, covering a wide range of topics from fundamental concepts to advanced techniques. I appreciate the practical approach taken in the course, which allowed me to gain hands-on experience through various exercises and projects. Overall, I'm extremely satisfied with the course, and I feel more confident in my machine learning skills. I highly recommend this course to anyone looking to deepen their understanding of machine learning and enhance their analytical capabilities.
Gal Beeri
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Data Science in Python: Classification
Data Science in Python: Classification
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
Course Description
This is a hands-on, project-based course designed to help you master the foundations for classification modeling in Python.
We’ll start by reviewing the data science workflow, discussing the primary goals & types of classification algorithms, and do a deep dive into the classification modeling steps we’ll be using throughout the course.
You’ll learn to perform exploratory data analysis, leverage feature engineering techniques like scaling, dummy variables, and binning, and prepare data for modeling by splitting it into train, test, and validation datasets.
From there, we’ll fit K-Nearest Neighbors & Logistic Regression models, and build an intuition for interpreting their coefficients and evaluating their performance using tools like confusion matrices and metrics like accuracy, precision, and recall. We’ll also cover techniques for modeling imbalanced data, including threshold tuning, sampling methods like oversampling & SMOTE, and adjusting class weights in the model cost function.
Throughout the course, you’ll play the role of Data Scientist for the risk management department at Maven National Bank. Using the skills you learn throughout the course, you’ll use Python to explore their data and build classification models to accurately determine which customers have high, medium, and low credit risk based on their profiles.
Last but not least, you’ll learn to build and evaluate decision tree models for classification. You’ll fit, visualize, and fine tune these models using Python, then apply your knowledge to more advanced ensemble models like random forests and gradient boosted machines.
If you’re an aspiring data scientist looking for an introduction to the world of classification modeling with Python, this is the course for you.
Course Description
This is a hands-on, project-based course designed to help you master the foundations for classification modeling in Python.
We’ll start by reviewing the data science workflow, discussing the primary goals & types of classification algorithms, and do a deep dive into the classification modeling steps we’ll be using throughout the course.
You’ll learn to perform exploratory data analysis, leverage feature engineering techniques like scaling, dummy variables, and binning, and prepare data for modeling by splitting it into train, test, and validation datasets.
From there, we’ll fit K-Nearest Neighbors & Logistic Regression models, and build an intuition for interpreting their coefficients and evaluating their performance using tools like confusion matrices and metrics like accuracy, precision, and recall. We’ll also cover techniques for modeling imbalanced data, including threshold tuning, sampling methods like oversampling & SMOTE, and adjusting class weights in the model cost function.
Throughout the course, you’ll play the role of Data Scientist for the risk management department at Maven National Bank. Using the skills you learn throughout the course, you’ll use Python to explore their data and build classification models to accurately determine which customers have high, medium, and low credit risk based on their profiles.
Last but not least, you’ll learn to build and evaluate decision tree models for classification. You’ll fit, visualize, and fine tune these models using Python, then apply your knowledge to more advanced ensemble models like random forests and gradient boosted machines.
If you’re an aspiring data scientist looking for an introduction to the world of classification modeling with Python, this is the course for you.
Curriculum
1
Getting Started
1
Getting Started
1
Getting Started
2
Intro to Data Science
2
Intro to Data Science
2
Intro to Data Science
3
Classification 101
3
Classification 101
3
Classification 101
4
Data Prep & EDA
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Data Prep & EDA
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Data Prep & EDA
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
K-Nearest Neighbors
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
K-Nearest Neighbors
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
K-Nearest Neighbors
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
Logistic Regression
6 MIN
6 MIN
6 MIN
6 MIN
6
Logistic Regression
6 MIN
6 MIN
6 MIN
6 MIN
6
Logistic Regression
6 MIN
6 MIN
6 MIN
6 MIN
7
Classification Metrics
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
7
Classification Metrics
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
7
Classification Metrics
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Imbalanced Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Imbalanced Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Imbalanced Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
9
Mid-Course Project
6 MIN
6 MIN
9
Mid-Course Project
6 MIN
6 MIN
9
Mid-Course Project
6 MIN
6 MIN
10
Decision Trees
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
Decision Trees
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
Decision Trees
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
11
Ensemble Models
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
11
Ensemble Models
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
11
Ensemble Models
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
12
Classification Summary & Next Steps
12
Classification Summary & Next Steps
12
Classification Summary & Next Steps
13
Final Project
6 MIN
6 MIN
13
Final Project
6 MIN
6 MIN
13
Final Project
6 MIN
6 MIN
Meet your instructors
Chris Bruehl
Analytics Engineer & Lead Python Instructor
Chris is a Python expert, certified Statistical Business Analyst, and seasoned Data Scientist, having held senior-level roles at large insurance firms and financial service companies. He earned a Masters in Analytics at NC State's Institute for Advanced Analytics, where he founded the IAA Python Programming club.
Student reviews
Maven Analytics has once again delivered an exceptional educational experience with their "Data Science in Python: Classification" course. This course stands out because of its detailed and structured approach, making complex topics accessible and engaging for learners at all levels. Detailed Curriculum and Structure From the outset, the course impresses with its well-organized curriculum. The curriculum impresses from the outset with its methodically laid out topics, comprehensively covering essential classification methods. Each section builds on the previous one, ensuring a smooth learning curve. The practical examples and hands-on exercises reinforce theoretical knowledge, facilitating a deeper understanding of classification in Python. Expert Instruction and Clarity Chris Bruel, the course instructor, demonstrates a high level of expertise and passion for teaching. His explanations are clear and concise, making even the most complex concepts easy to grasp. The step-by-step guidance through the assignment solutions walkthrough really helps you to verify your approach to the assignments. Engaging Learning Experience What sets this course apart is its engaging format. The datasets for projects and assignments adds to the learning process. The interactive nature of the course, coupled with quizzes and assessments, keeps learners motivated and invested in their progress. Conclusion Maven Analytics' "Data Science in Python: Classification" course is a must-take for anyone looking to master classification techniques using Python. Its comprehensive curriculum, expert instruction, and engaging format make it a standout choice. Whether you're a beginner or an experienced professional, this course will significantly enhance your data science skills.
Andrew Hubbard
I truly enjoyed taking this course; it was an enriching experience that surpassed my expectations. Like any other course I've taken with Chris and Maven, this one has significantly fine-tuned my understanding of machine learning concepts. Chris's teaching style is exceptional; he has a unique ability to simplify even the most complex topics and convey the message in an easy-to-comprehend manner. Throughout the course, I found myself engaged and motivated to learn more, thanks to Chris's clear explanations and insightful examples. The content was well-structured and comprehensive, covering a wide range of topics from fundamental concepts to advanced techniques. I appreciate the practical approach taken in the course, which allowed me to gain hands-on experience through various exercises and projects. Overall, I'm extremely satisfied with the course, and I feel more confident in my machine learning skills. I highly recommend this course to anyone looking to deepen their understanding of machine learning and enhance their analytical capabilities.
Gal Beeri
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Data Science in Python: Classification
Data Science in Python: Classification
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
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