This is the code repository for Python Deep Learning Projects, published by Packt.
Top 7 algorithms for scientific computing, data analysis, and machine learning
Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
This book covers the following exciting features:
- Understand how to identify a data science problem correctly
- Implement well-known machine learning algorithms efficiently using Python
- Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy
- Devise an appropriate prediction solution using regression
- Work with time series data to identify relevant data events and trends
- Cluster your data using the k-means algorithm
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
def dic_key_count(dic, key):
if key is None:
return 0
if dic.get(key, None) is None:
return 0
else:
return int(dic[key])
Following is what you need for this book: This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1-7 | Python 2.7.13 or higher,Rscript 3.4.1 or higher | Windows, OS X, Linux, BSD or other |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Dávid Natingga graduated with a master's in engineering in 2014 from Imperial College London, specializing in artificial intelligence. In 2011, he worked at Infosys Labs in Bangalore, India, undertaking research into the optimization of machine learning algorithms. In 2012 and 2013, while at Palantir Technologies in USA, he developed algorithms for big data. In 2014, while working as a data scientist at Pact Coffee, London, he created an algorithm suggesting products based on the taste preferences of customers and the structures of the coffees. In order to use pure mathematics to advance the field of AI, he is a PhD candidate in Computability Theory at the University of Leeds, UK. In 2016, he spent 8 months at Japan's Advanced Institute of Science and Technology as a research visitor.
Data Science Algorithms in a Week
Click here if you have any feedback or suggestions.