Introductory Tutorials For Machine Learning
Last updated on 2019-02-08
About the Book
Machine learning is changing the way we engineer software. Tasks like parsing and summarizing text used to be done with thousands of lines of procedural code. Now they are done with neural networks containing millions of trained parameters. Whether you're a beginner or expert, some understanding of machine learning is now vital to being a software engineer.
Introductory tutorials provide a great place for a person to kickstart their machine learning journey. I recommend the tutorials in this book for somebody who is just starting machine learning career or wants to brush up their skills. A breadth of practical examples serve as a resource to people across domains and machine learning enthusiasts at any level. Written in simple English, technical concepts are explained in intuitive, easy-to-follow language.
Derrick Mwiti has been a valuable contributor to Heartbeat since we started the publication. He is incredibly generous in creating tutorials that support other developers. His passion for sharing machine learning knowledge can be seen in the care he has put into every page of this book. If you are looking for the best place to start your machine learning and data science career, I recommend that you start here.
Cofounder and CEO at Fritz
Boston, MA, USA
The State of Data Science and Machine Learning, Part 1: Education, job titles, and skills 7
What programming language should aspiring data scientists learn? 14
Thinking of blogging about Data Science? Here are some tips and possible benefits. 19
New to data science? Here are a few places to start 24
Data Visualization & Exploration using Pandas Only: Beginner 26
Introduction to Python Metaclasses 37
JSON Data in Python 44
Decorators in Python 52
Introduction to MongoDB and Python 61
Dash for Beginners 74
Introduction to Deep Learning with Keras 90
A Beginner’s Guide to Convolutional Neural Networks (CNN) 103
Guide to saving & hosting your first machine learning model 113
Introduction to Generative Adversarial Networks (GANs) 131
Introduction to Self-Organizing Maps (SOMs) 147
Introduction to Restricted Boltzmann Machines Using PyTorch 160
Boosting your Machine Learning Models Using XGBoost 172
Introduction to PyTorch for Deep Learning 179
How to Perform Neural Style Transfer with PyTorch 187
How to build a Simple Recommender System in Python 201
Detecting the Language of a Person’s Name using a PyTorch RNN 212
Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices 224
Sales Forecasting Using Facebook’s Prophet 231
Automated Machine Learning in Python 240
Using Caret in R to Classify Term Deposit Subscriptions for a Bank 248
Object Detection with Luminoth 254
Handling Text Data with a Keras Embedding Layer 260
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