Data Science Supervised Machine Learning in Python

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

Tailored for data scientists, analysts, and enthusiasts, this program provides an in-depth exploration of supervised learning techniques, empowering learners to build predictive models and make informed decisions based on data.

What You Will Learn:

  1. Introduction to Supervised Learning:
    • Gain a foundational understanding of supervised learning and its applications in data science.
    • Explore the key concepts of labeled datasets, features, and target variables.
  2. Data Preprocessing for Supervised Learning:
    • Learn effective techniques for cleaning, preprocessing, and transforming data for supervised learning tasks.
    • Explore methods for handling missing values, scaling features, and encoding categorical variables.
  3. Regression Analysis:
    • Dive into regression analysis for predicting continuous outcomes.
    • Understand linear regression, polynomial regression, and other regression techniques.
  4. Classification Algorithms:
    • Explore classification algorithms, including logistic regression, decision trees, and support vector machines.
    • Learn how to build and evaluate models for binary and multiclass classification problems.
  5. Model Evaluation and Hyperparameter Tuning:
    • Understand how to evaluate the performance of supervised learning models using metrics like accuracy, precision, and recall.
    • Dive into hyperparameter tuning to optimize model performance.
  6. Ensemble Learning:
    • Explore ensemble learning techniques, including bagging and boosting.
    • Understand how ensemble methods can enhance the accuracy and robustness of predictive models.
  7. Feature Selection and Dimensionality Reduction:
    • Learn techniques for feature selection and dimensionality reduction to improve model efficiency and interpretability.
    • Explore methods like PCA (Principal Component Analysis) and LASSO regression.
  8. Real-world Applications and Case Studies:
    • Apply your knowledge through hands-on projects and real-world case studies.
    • Gain practical experience in solving business problems using supervised machine learning.

The instructors provide a clear and comprehensive overview of supervised learning concepts and techniques. The hands-on exercises were instrumental in solidifying my understanding, especially in regression and classification tasks.


The instructors guide you through each step, from data preprocessing to building and fine-tuning models. The inclusion of ensemble learning and dimensionality reduction techniques provided a well-rounded view of the subject.


Your Instructor

Yoohoo Academy
Yoohoo Academy

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All Yoohoo Academy courses are taught by experts in their field who have a true passion for teaching and sharing their knowledge.

Frequently Asked Questions

When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.

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