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:
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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.
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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.
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Regression Analysis:
- Dive into regression analysis for predicting continuous outcomes.
- Understand linear regression, polynomial regression, and other regression techniques.
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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.
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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.
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Ensemble Learning:
- Explore ensemble learning techniques, including bagging and boosting.
- Understand how ensemble methods can enhance the accuracy and robustness of predictive models.
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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.
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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.
— Melissa
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.
— James
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