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Step-by-step guide to practising data science techniques with Jupyter notebooks Key Features Acquire Python skills to do independent data science projects Learn the basics of linear algebra and statistical science in Python way Understand how and when they're used in data science Build predictive models, tune their parameters and analyze performance in few steps Cluster, transform, visualize, and extract insights from unlabelled datasets Learn how to use matplotlib and seaborn for data visualization Implement and save machine learning models for real-world business scenarios Description Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, you’ll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques. Audience The book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience. Table of Contents Data Science Fundamentals Installing Software and Setting up Lists and Dictionaries Function and Packages NumPy Foundation Pandas and Dataframe Interacting with Databases Thinking Statistically in Data Science How to import data in Python? Cleaning of imported data Data Visualization Data Pre-processing Supervised Machine Learning Unsupervised Machine Learning Handling Time-Series Data Time-Series Methods Case Study – 1 Case Study – 2 Case Study – 3 Case Study – 4 About the Author Prateek is a Data Enthusiast and loves the data driven technologies. Prateek has total 7 years of experience and currently he is working as a Data Scientist in an MNC. He has worked with finance and retail clients and has developed Machine Learning and Deep Learning solutions for their business. His keen area of interest is in natural language processing and in computer vision. In leisure he writes posts about Data Science with Python in his blog.
Learn to identify the implementation of Discrete Structure and Theory of Automata in a myriad of applications used in day to day life Key Features Learn how to write an argument using logical notation and decide if the argument is valid or not valid. Learn how to use the concept of different data structures (stacks, queues, sorting concept, etc.) in the computer science field. Learn how to use Automata Machines like FSM, Pushdown automata, Turing machine, etc. in various applications related to computer science through suitable practical illustration. Learn how to implement the finite state machine using JFLAP (Java Formal Languages and Automata Package). Description This book's purpose is to provide a modern and comprehensive introduction to the subject of Discrete Structures and Automata Theory. Discrete structures, also called Discrete Mathematics, are an exciting and active subject, particularly due to its extreme relevance to both Mathematics and Computer Science and Algorithms. This subject forms a common foundation for rigorous Mathematical, Logical Reasoning and Proofs, as well as a formal introduction to abstract objects that are essential tools in an assortment of applications and effective computer implementations. Computing skills are now an integral part of almost all the Scientific fields, and students are very enthusiastic about being able to harness the full computing power of these tools. Further, this book also deep dives into the Automata Theory with various examples that illustrate the basic concepts and is substantiated with multiple diagrams. The book's vital feature is that it contains the practical implementation of the Automata Machine example through the JFLAP Tool. Courses on Discrete Structures and Automata theory are offered at most universities and colleges. What will you learn Understand the basic concepts of Sets and operations in Sets. Demonstrate different traversal techniques for Trees and Graphs. Deep dive into the concept of Mathematical Induction, Sets, Relations, Functions, Recursion, Graphs, Trees, Boolean Algebra, and Proof techniques. Understand the concept of Automata Machines in day to day life like the Elevator, Turnstile, Genetic Algorithms, Traffic lights, etc. Use the JFLAP tool to solve the various exercise problems related to automata theory. Who this book is for This book is a must-read to everyone interested in improving their concepts regarding Discrete Structure and Automata Theory. Table of Contents 1. Set Theory 2. Relations and Functions 3. Graph Theory 4. Trees 5. Algebraic Structure 6. Recursion and Recurrence Relations 7. Sorting 8. Queues 9. Introduction 10. Finite Automata Theory 11. Theory of Machines 12. Regular Language 13. Grammar 14. Pushdown Automata 15. Cellular Automata 16. Turning Machine 17. Problems Solving Using JFLAP Tool 18. Revision Questions About The Authors Dr. UMESH SEHGAL completed his Ph.D.,M.Phil. Computer Science and MCA. He held academic positions at the GNA University as an A.P in FCS Department. He has achieved the Best Educationist Award in 2017.He has achieved the Indira Gandhi Education Excellence Award in 2017.He has achieved the Best Researcher Award in 2018-19. SUKHPREET KAUR GILL received the M.Tech. degree in Computer Science and Engineering from Guru Nanak Dev Engineering College, Ludhiana. She is currently working as Assistant Professor at GNA University Phagwara. She has achieved the Bright Educator Award 2019. She has published several articles in leading International and National Computer science journals.
Step-by-step guide to learn and solve complex computational problems with Nature Inspired algorithms. Key Features Artificial Neural Networks Deep Learning models using Keras Quantum Computers and Programming Genetic Algorithms, CNN and RNNs Swarm Intelligence Systems Reinforcement Learning using OpenAI Artificial Life DNA computing Fractals Description Natural Computing is the field of research inspired by nature, that allows the development of new algorithms to solve complex problems, leads to the synthesis of natural models, and may result in the design of new computing systems. This book exactly aims to educate you with practical examples on topics of importance associated with research field of Natural computing. The initial few chapters will quickly walk you through Neural Networks while describing deep learning architectures such as CNN, RNN and AutoEncoders using Keras. As you progress further, you’ll gain understanding to develop genetic algorithm to solve traveling saleman problem, implement swarm intelligence techniques using the SwarmPackagePy and Cellular Automata techniques such as Game of Life, Langton's ant, etc. The latter half of the book will introduce you to the world of Fractals such as such as the Cantor Set and the Mandelbrot Set, develop a quantum program with the QiSkit tool that runs on a real quantum computing platform, namely the IBM Q Machine and a Python simulation of the Adleman experiment that showed for the first time the possibility of performing computations at the molecular level. What You Will Learn Mastering Artificial Neural Networks Developing Artificial Intelligence systems Resolving complex problems with Genetic Programming and Swarm intelligence algorithms Programming Quantum Computers Exploring the mathematical world of fractals Simulating complex systems by Cellular Automata Understanding the basics of DNA computation Who This Book Is For This book is for all science enthusiasts, in particular who want to understand what are the links between computer sciences and natural systems. Interested readers should have good skills in math and python programming along with some basic knowledge of physics and biology. . Although, some knowledge of the topics covered in the book will be helpful, it is not essential to have worked with the tools covered in the book. Table of Contents Neural Networks Deep Learning Genetic Programming Swarm Intelligence Cellular Automata Fractals Quantum Computing DNA Computing About the Author Giancarlo Zaccone has over ten years of experience in managing research projects in scientific and industrial areas. He is a Software and Systems Engineer Consultant at European Space Agency (ESTEC). Giancarlo holds a master’s degree in Physics and an advanced master’s degree in Scientific Computing at La Sapienza of Rome. His LinkedIn Profile: https://www.linkedin.com/in/giancarlozaccone/
Quick solutions to frequently asked algorithm and data structure questions. Description Python is the most popular programming language, and hence, there is a huge demand for Python programmers. Even if you have learnt Python or have done projects on AI, you cannot enter the top companies unless you have cleared the Algorithms and data Structure coding test. This book presents 75 most frequently asked coding questions by top companies of the world. It not only focuses on the solution strategy, but also provides you with the working code. This book will equip you with the skills required for developing and analyzing algorithms for various situations. This book teaches you how to measure Time Complexity, it then provides solutions to questions on the Linked list, Stack, Hash table, and Math. Then you can review questions and solutions based on graph theory and application techniques. Towards the end, you will come across coding questions on advanced topics such as Backtracking, Greedy, Divide and Conquer, and Dynamic Programming. After reading this book, you will successfully pass the python interview with high confidence and passion for exploring python in future. What you will learn -Design an efficient algorithm to solve the problem. -Learn to use python tricks to make your program competitive. -Learn to understand and measure time and space complexity. -Get solutions to questions based on Searching, Sorting, Graphs, DFS, BFS, Backtracking, Dynamic programming. Who this book is for This book will help professionals and beginners clear the Data structures and Algorithms coding test. Basic knowledge of Python and Data Structures is a must. Table of Contents 1. Lists, binary search and strings 2. Linked lists and stacks 3. Hash table and maths 4. Trees and graphs 5. Depth first search 6. Breadth first search 7. Backtracking 8. Greedy and divide and conquer algorithms 9. Dynamic programming About the Authors Professor Shyamkant Limaye spent 18 years in the computer industry and 30 years in teaching electronics engineering students. His experience includes a two-year stint as a system analyst in the USA. In 1971, he graduated from Visvesvaraya National Institute of Technology in Electrical Engineering with a gold medal. He did masters from IIT Kanpur and Doctorate in electronics from RTM Nagpur University. He has guided ten students for PhD. He published a text book on VHDL programming in 2007. He has also published a thriller novel titled “Dual reality” in 2011. Currently, he is a Professor in the Electronics and Telecomm Department at St. Vincent Pallotti College of Engineering and Technology, Nagpur. LinkedIn profile: https://www.linkedin.com/in/shyam-limaye-35816ba/ Blog links: limayesir.wordpress.com