Becoming an expert in anything requires commitment to learn and consistency to reach your goals. The same is true for all professions whether #AI, engineering or even medical studies. Knowledge acquisition is critical in skill development as this raises your level of expertise. When was the last time you dedicated yourself to becoming an expert?
Unlike the past when #artificialintelligence was a new concept for many, the mention of AI has become mainstream and buzzyworthy. My personal journey in the field of AI has been one filled with consistent learning and studying with diverse resources.
Unlike the past when artificial intelligence was a new concept for many, the mention of AI has become mainstream and buzzworthy.
You can become an expert in data science todayby reading the right books.
Here are 10 of the best books from 2019 and 2020 in the Data Science, Machine Learning, and Applied AI domains for your reading list:
Author: Christoph Molnar (2019)
Interpretable Machine Learning by Christoph Molnar focuses on interpretability of decisions and models of machine learning. Adoption of #machinelearning for research and product development holds great potential but the lack of predictive ability by computer systems limits the adoption of ML. The author offers a detailed analysis of interpretable models from linear regression, decision trees and decision rules.
Interpretation of black box models is another key area covered in the book where the author offers lessons on LIME and Shapley values for prediction purposes. Molnar dives deeper into accumulated local effects as part of agnostic methods used in AI.
Interpretable Machine Learning focuses on critical analysis for the dynamics of interpretation and how to make better choices for interpretation of machine learning.
You might ask this question: How can I interpret my models with machine learning? Molnar answers this question by exploring the merits and demerits of interpretation approaches to offer readers a clear picture of the best solutions for their projects.
How can I interpret my models with machine learning? Molnar answers this question by exploring the merits and demerits of interpretation approaches to offer readers a clear picture of the best solutions for their projects.
Data scientists encounter challenges interpreting their machine learning models and through Molnar’s lesson on structured data, you start to understand practical applications of interpretation to achieve the best results. The author keeps in mind the diverse nature of the data science industry by offering timely examples about interpretation of machine learning models.
Author: Wes McKinney (2017)
Python for Data Analysis by Wes McKinney helps readers to learn data science by using the Python programming language where readers enjoy the simple language used by the author to explain technical concepts. Published in 2017 and authored by Wes McKinney, the book is ideal for beginners in the #datascience field who want to understand scientific computing as applied in the industry.
The author covers key areas in data science including dataset crunching and manipulation in Python. I admire this book for its flexibility in covering subject areas in python that most readers would want to discover when learning Data Science for the first time. McKinney offers solutions you can use to address data analysis challenges by using effective methods with popular packages such as pandas and numpy.
Author: Andriy Burkov (2019)
Based on theory and practical applications, this book takes readers through machine learning in a simplified manner. The author offers background of information to readers about basic machine learning topics by taking them through important discussions needed to improve their understanding. AI is a diverse field, machine learning is critical to becoming a professional, and this author takes care of these considerations all in Python.
From the first page to the last, Burkov engages with readers by taking them through the world of machine learning systematically. If you are looking for a book that will give you an accurate assessment of the machine-learning field and practical use cases, then this is your book. The Hundred-Page ML Book provides resources that enable readers to implement solutions in the real world. At the same time, machine learning engineers find this book practical because of the approach used by the author to explain statistical and mathematical concepts.
Authors: Shanqing Cai, Stanley Bileschi, Eric D. Nielsen with Francois Chollet (2020)
The authors teach with use cases for developers including transferring applications to the web, browser language processing and image browser processing
Author: John T. Wolohan (2020)
Scaling ML in production requires extensive processing power such as GPUs and TPUs. Between threading, processes, and concurrency, Mastering Large Datasets with Python teaches you practical tools to work with parallel and distributed systems. Wolohan teaches how to start with simple, small projects that scale into Big Data pipelines. According to Wolohan, using functional approaches in Python is important for achieving optimal results.
Distributed technology is explored to prepare students for the large datasets on cloud-based systems. If you are interested in building systems with Python, massive data sets, and distributed data science models, this book will guide you with step-by-step processes.
For students looking forward to build enormous data science models, this book will guide you through the process by helping you scale in the best manner possible.
Authors: Nina Zumel, John Mount (2019)
Taught for R programming, Practical Data Science with R selects practical examples students need to understand data science and apply their skills accordingly in R.
Readers learn about statistical analysis interpretation, the data science workflow, and presentation design.
Author: Ben Weber (2020)
Predictive models are critical for any data scientist seeking to achieve good outcomes on an organizational level. Building a scalable model is challenging and skilled data scientists can effectively deploy models in production. Weber teaches about data science automation methods and how data scientists can take charge of their workflows for better results. An understanding of data from the initial to the production phases is another case example illustrated in the book and offers meaningful insights to readers.
Areas such as cloud deployment, developing web end points and models of machine learning are additional examples covered in the book. Weber teaches from a top-down approach: build reproducible models that can scale well in production. Between PySpark, Pub/Sub techniques, and Kafka, Weber deeps dive into essential data science tools.
Areas such as cloud deployment, developing web end points and models of machine learning are additional examples covered in the book.
Author: Jake Vanderplas (2016)
Python is the dominant programming language for data science programmers, and through detailed analysis including Pandas, Scikit-Learn, and NumPy, Vanderplas provides all resources you need to understand data at the foundational level. Heralded as one of the first true data science resources in Jupyter, Vanderplas’ teaches students how to effectively manipulate data in pandas.
The Python Data Science Handbook is a must-have if you want to learn data science, and is often the first book I recommend to new students in the field. From data cleaning and data transformations to design thinking on how to develop machine learning models based on their data, Vanderplas provides a rich resource of use cases.
The Python Data Science Handbook is a must-have if you want to learn data science, and is often the first book I recommend to new students in the field.
Authors: Eric Redmond, Jim Wilson (2018)
Massive data systems require large databases and database frameworks. Data Scientists must be comfortable working with multiple database systems, and Seven Databases in Seven Weeks dives deep into Redis, Neo4J, CouchDB, MongoDB, HBase, Postgres, and DynamoDB. Redmond and Wilson provide practical data model systems that imitate database systems at Fortune 500 companies.
Seven Databases in Seven Weeks dives deep into Redis, Neo4J, CouchDB, MongoDB, HBase, Postgres, and DynamoDB.
Authors: Alberto Artasanchez, Prateek Joshi (2020)
Artificial Intelligence with Python provides an overview of data science, machine learning and AI applied across industries. The authors focus on students learning the essentials of building ML pipelines. AI development tools and the cloud are additional topics you can learn from AI with Python.
Artasanchez and Joshi have updated their best-selling book for TensorFlow 2.0 and the latest Python 3.9. Students dive deep into feature engineering and data pipelines, as well as advanced use cases such as speech recognition and chatbots. The authors also guide students to implement and deploy their machine learning systems through neural networks, deep learning and the cloud.
You have the grit to be an Expert Data Scientist
From teaching thousands of students at The Carpentries, Galvanize, and General Assembly, I have narrowed dozens of books into these 10 resources on data science, machine learning and AI. Whether you are learning from OReilly, Manning, Packt, Leanpub, Pragprog, or other platforms now there is a wealth of knowledge for everyone to learn data science.