Conceptualizing Python in Google COLAB

This book can serve as textbook for post graduates and reference for any computer graduate. It will also provide easy reference for Computer Professionals who wants to begin their career in Machine Learning using Python. Colaboratory, or ‘Colab’ for short, is a product from Google Research. Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to Machine Learning (ML), data analysis and education. If you are exploring Machine Learning but struggling to conduct simulations on enormous datasets, or an expert playing with ML desperate for extra computational power, Google Colab is the perfect solution for you. Google Colab or ‘the Colaboratory’ is a free cloud service hosted by Google to encourage Machine Learning and Artificial Intelligence research, where often the barrier to learning and success is the requirement of tremendous computational power. If you want to create a Machine Learning model but you don’t have a computer that can take the workload, Google Colab is the platform for you. Even if you have a GPU or a good computer creating a local environment with anaconda and installing packages and resolving installation issues are a hassle. Colaboratory is a free Jupyter notebook environment provided by Google where you can use free GPUs and TPUs which can solve all these issues. It contains almost all the modules you need for Data Science analysis. These tools include but are not limited to Numpy, Scipy, Pandas, etc. Even Deep Learning frameworks, such as Tensorflow, Keras and Pytorch are also included. This book is precisely organized into twelve chapters. Each chapter has been carefully developed with the help of several implemented concepts. Dedicated efforts have been put in to ensure that every concept of Python discussed in this book is explained with help of relevant commands and screenshots of the outputs have been included. Chapter 1 focuses on development environment offered by Google COLAB. Chapters 2 through 4 cover the Python language fundamentals focusing on control and iterative statements, operators along with their applications in basic programs. Python employs blended programming paradigm in which it is procedural, object-oriented and functional. The best part of all programming languages reside in a single platform. Chapter 5 focuses on functions in Python with a special emphasis on Lambda functions. Advanced Python programming concepts such as iterators, closures, decorators, generators are covered at depth in Chapter 6 and 7. A good and in-depth knowledge of exception handling enables in writing a reliable and robust code. To cater to this need Chapter 8 unleashes the salient features of exception handling in Python. Data persistence through file handling is covered in Chapter 9. Due to the wide application of Regular expressions in pattern matching, Chapter 10 is fully devoted to understanding of regular expression in Python. Different types of common errors that might creep in during the execution of a Python program are summarized in Chapter 11. Final Chapter 12 is devoted to implementation of object oriented concepts in Python. The case study based on object oriented concept is discussed at depth and implemented in Appendix A.