There is a lifelong learning curve for data scientists. You will learn more quickly by reading the right books and focusing on developing the right skills. The good news is that Packt has published a number of books that can help you out.
Get these free Packt eBooks for beginners and advanced learners of Python, Data Analysis and Machine Learning.
By Fabrizio Romano
What you will learn:
- Get Python up and running on Windows, Mac, and Linux in no time
- Grasp the fundamental concepts of coding, along with the basics of data structures and control flow.
- Write elegant, reusable, and efficient code in any situation
- Understand when to use the functional or the object oriented programming approach
- Create bulletproof, reliable software by writing tests to support your code
- Explore examples of GUIs, scripting, data science and web applications
- Learn to be independent, capable of fetching any resource you need, as well as dig deeper
Click here to get the eBook.
Author – Fabrizio Romano
- Fabrizio Romano holds a Master’s Degree in Computer Science Engineering from the University of Padova. He is also a certified Scrum Master and Reiki Master and Teacher, member of CNHC.
- He moved to London in 2011 to work for companies such as Glasses Direct and TBG/Sprinklr. He now works at Sohonet as a Principal Engineer/Tech Lead.
- He has given talks on Teaching Python and TDD in two editions of EuroPython, and at Skillsmatter and ProgSCon, in London.
Building Machine Learning Systems with Python
By Willi Richert, Luis Pedro Coelho
What you will learn:
- Build a classification system that can be applied to text, images, or sounds
- Use scikit-learn, a Python open-source library for machine learning
- Explore the mahotas library for image processing and computer vision
- Build a topic model of the whole of Wikipedia
- Get to grips with recommendations using the basket analysis
- Use the Jug package for data analysis
- Employ Amazon Web Services to run analyses on the cloud
- Recommend products to users based on past purchases
Click here to get the eBook.
Authors –
Willi Richert
- Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation
- Currently, he works for Microsoft in the Core Relevance Team of Bing, where he is involved in a variety of ML areas such as active learning, statistical machine translation, and growing decision trees
Luis Pedro Coelho
- Luis Pedro Coelho is a computational biologist: someone who uses computers as a tool to understand biological systems. In particular, Luis analyzes DNA from microbial communities to characterize their behavior
- Luis has a PhD from Carnegie Mellon University, one of the leading universities in the world in the area of machine learning
- In 2004, he started developing in Python and has contributed to several open source libraries in this language. He is the lead developer on the popular computer vision package for Python and mahotas, as well as the contributor of several machine learning codes
Practical Data Analysis
By Hector Cuesta
What you will learn:
- Work with data to get meaningful results from your data analysis projects
- Visualize your data to find trends and correlations
- Build your own image similarity search engine
- Learn how to forecast numerical values from time series data
- Create an interactive visualization for your social media graph
- Explore the MapReduce framework in MongoDB
- Create interactive simulations with D3js
Click here to get the eBook.
Author – Hector Cuesta
- Hector Cuesta is founder and Chief Data Scientist at Dataxios, a machine intelligence research company. Holds a BA in Informatics and a M.Sc. in Computer Science
- He provides consulting services for data-driven product design with experience in a variety of industries including financial services, retail, fintech, e-learning and Human Resources. He is an enthusiast of Robotics in his spare time
- You can follow him on Twitter at https://twitter.com/hmCuesta
Machine Learning with R
By Brent Lantz
What you will learn:
- Get to grips with the basics of machine learning
- Find out how to classify data using nearest neighbor methods
- Learn Bayesian methods to classify data
- Predict values using decision trees, rules, and support vector machines
- Model data using neural networks
- Join a global community of statisticians and data scientists putting R to work to produce stunning insights
Click here to get the eBook.
Author – Brett Lantz
- Brett Lantz has spent more than 10 years using innovative data methods to understand human behavior. A trained sociologist, he was first enchanted by machine learning while studying a large database of teenagers’ social networking website profiles
- Since then, Brett has worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. He maintains http://dataspelunking.com/, a website dedicated to sharing knowledge about the search for insight in data
Advanced Machine Learning With Python
By John Hearty
What you will learn:
- Push your Python algorithms to maximum potential
- Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning with real world applications
- Learn most relevant and powerful machine learning techniques
- Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications
Click here to get the eBook.
Author – John Hearty
- John Hearty is a consultant in digital industries with substantial expertise in data science and infrastructure engineering
- His favourite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network
- In his own time, he routinely builds ML solutions in Python to fulfil a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation. He currently lives in the UK
Building your skill sets as a data scientist takes time. Simply getting your degree or certificates isn’t enough. You will need to continue learning over the years, which requires you to read some of the best books. These books should help you on your journey.