The Supervised Learning Workshop: Predict outcomes from data by building your own powerful predictive models with machine learning in Python

About the Book

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.

Table of Contents:
Table of Contents

1. Fundamentals of Supervised Learning Algorithms
2. Exploratory Data Analysis and Visualization
3. Linear Regression
4. Autoregression
5. Classification Techniques
6. Ensemble Modeling
7. Model Evaluation

About the Author :
Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry. Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning. Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in ML, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds a first-class honors bachelor's degree in both engineering and medical science from the University of Sydney, Australia. Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi. Contacted by Melwyn Dsouza on 14/05/22018 Contacted for HTML5 and CSS3 on July 22, 2019 by Sneha Shinde https://www.udemy.com/making-ionic-mobile-apps-with-ionic-creator/ Sukanya Mandal is a Data Scientist currently working with an MNC and an independent researcher. She takes pleasure in working with Data, getting underneath it, discovering those hidden insights by connecting the dots. An avid believer of opensource data science principles, she is currently contributing to signacore - an open-source project on robotics and reinforcement learning. An author and a blogger, her technological interests and competence lie in the area of Machine Learning, Deep Learning, Natural Language Processing and the Internet of Things. Her favorite language is Python and she has significant experience working on it, and gladly contributed her expertise in reviewing this course!