Supervised Learning Workshop

Discover how you can supervise machine learning algorithms in Python and personalize predictive models with the help of real-world datasets Key Features Explore the fundamentals of supervised machine learning and its applications Learn how to label and process data correctly using Python libraries Gain a comprehensive overview of different machine learning algorithms used for building prediction models Book DescriptionWould you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.What you will learn Import NumPy and pandas libraries to assess the data in a Jupyter Notebook Discover patterns within a dataset using exploratory data analysis Using pandas to find the summary statistics of a dataset Improve the performance of a model with linear regression analysis Increase the predictive accuracy with decision trees such as k-nearest neighbor (KNN) models Plot precision-recall and ROC curves to evaluate model performance Who this book is forIf you are a beginner or a data scientist who is just getting started and looking to learn how to implement machine learning algorithms to build predicting models, then this book is for you. To expedite the learning process, a solid understanding of Python programming is recommended as you’ll be editing the classes or functions instead of creating from scratch.