Python Machine Learning: A Guide For Beginners (2nd Edition) Kindle Edition
by Stefan Jansen
The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.
This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, learn, PyMC3, XP boost, lightgbm, and cat boost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNN's, using Keras and
PyTorch to exploit unstructured data for sophisticated strategies.
What you will learn
- Implement machine learning techniques to solve investment and trading problems
- Leverage market, fundamental, and alternative data to research alpha factors
- Design and finetune supervised, unsupervised, and reinforcement learning models
- Optimize portfolio risk and performance using pandas, NumPy, and sci-kit learn
- Integrate machine learning models into a live trading strategy on Quantopian
- Evaluate strategies using reliable backtesting methodologies for time series
- Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow
- Work with reinforcement learning for trading strategies in the OpenAI Gym
by Oliver Theobald
Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing statistical concepts and specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space.
In this step-by-step guide you will learn:
- The very basics of Machine Learning that all beginners need to master
- Association Analysis used in the retail and E-commerce space
- Decision Trees for visually mapping and classifying decision processes
- Regression Analysis to create trend lines and predict trends
- Data Reduction and Principle Component Analysis to cut through the noise