Computer Programming: 2 Books in 1: Machine Learning for Beginners + Python for Beginners
- Length: 330 pages
- Edition: 1
- Language: English
- Publication Date: 2019-12-27
- ISBN-10: B08383L5L9
- Sales Rank: #1851992 (See Top 100 Books)
Are you a newbie to Computer Programming? Are you curious to learn why Python is the best way to begin?
Probably you want to start by learning practical techniques that could easily make you become a programmer, developer or simply make you learn how it goes. You probablywould like to learn with examples and images of how the code was written, and afterward an explanation of what is the task and job of each element on the code.
This 2 books in 1 is exactly what you’re looking for !
“Computer Programming” is a complete guide to start learning Python and Machine Learning.
Machine Learning is one of the most exciting developments to come up of computer science since its founding and Python is one of the most used programming language all around the world. The knowledge of Machine Learning and Python is going to give you lots of advantages on your programming technique.
Here’s what you’ll find inside:
- Introduction to Data Science, AI, Machine Learning and uses of Python;
- See how Machine Learning is being used by companies like Amazon, Netflix and Google;
- Benefits of Python and differences between it and other languages;
- Python basic concepts, as well as Conditional Statements, Loops, Functions, Modules, OOP etc..;
- And much more…
Introduction Chapter 1: What is Machine Learning – and the evolution of machines A Quick History of Computer Science The Evolution of Machines The Evolution of Artificial Intelligence Machine Learning Training and Test Data Machine Learning vs. Artificial Intelligence and Deep Learning What Sectors and Industries Use Machine Learning Government Financial Services Transportation and Shipment Mining, Oil and Gas Retail and online marketing Healthcare Chapter 2: Introduction to Data Science What is Data Science? Is Data Science Really a Thing? The Role of Machine Learning in Data Science The Tasks of Data Science Predictive Analysis Prescriptive Analytics Pattern Recognition Classification and Anomaly Detection Examples of Data Science in Use Data Science as a Career Chapter 3: Supervised Machine Learning Supervised Learning Selecting an Algorithm Bias Errors Variance Errors Noisy Data Classification vs. Regression Variations on Supervised Learning Chapter 4:Unsupervised Learning Clustering K-Means Clustering Mean Shift Clustering Neural Networks Markov Algorithm Chapter 5: Reinforcement Learning Types of Reinforcement Learning The State Model of Reinforcement Learning Short Term Rewards When are Rewards Given Episodes Continuous Tasks Q Tables Chapter 6: Algorithms for Supervised Learning Linear Regression Logistic Regression Decision Trees Random Forest Nearest Neighbor Chapter 7: Tips for Your Work in Machine Learning Understand the Difference Between Prediction and Classification Knowing Which Type of Learning to Use Data Selection Tools to Use Practice Utilize Mixed Learning Models Have Realistic Expectations Chapter 8: The Future of Machine Learning Introduction Chapter 1: Introductory Chapter What Is Python? Which Are the Advantages of Using Python? Differences Between Python and Other Programming Languages Introduction to Data Science Machine Learning What Is Machine Learning? How Is ML Classified? What Are Some of the Current Applications of ML? Artificial Intelligence (AI) Fundamentals of Artificial Intelligence Why Is Artificial Intelligence Used? Artificial Intelligence and Programming Languages Python and Artificial Intelligence What Are the Features and Advantages of Python with AI? Characteristics of Artificial Intelligence In Which Areas Is AI Applied? Why Python? Why It Is Highly Recommended in the Above Topics? How to Start Installing Python and Its Interpreter? Using Python with Shell and IDLE Chapter 2: Basic Concepts How Can We Declare a Variable? Data Types Operators Interactivity and Its Importance Basic Functions for Interactivity Standard Output with print() Standard Input with input() Chapter 3: Conditionals If Statement Else Statement Elif Statement Chapter 4: Loops While Loop Statements Used in the While Loop For Loops Chapter 5: Functions Some Python Functions How Can I Create My Own Function? Parameters Return Statement Lambda Function Filter() Function Map() Function What Is the Difference Between Def and Lambda? Variables Global Variables Local Variables Chapter 6: Modules Why Should You Use Modules? How Do We Create a Module on Python? Import Statement How to Import a Module? Chapter 7: OOP (Object-Oriented Programming) What Is a Class? Chapter 8: File management How to Access a File? Open() Function Read([ ]) Method Readlines() Method Close() Function What Is a Buffer? Errors Encoding Newline Handling files with the "OS" module xlsx Files: Handling PDF files Handling of BIN Type Files Chapter 9: Exceptions What Is the Possible Solution to This Problem? Chapter 10: Other Topics Python and Serial Communication in Electronic Devices Python and the Databases Python and Graphical Interfaces Conclusion
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