Python for Beginners: Comprehensive Guide to the Basics of Programming, Machine Learning, Data Science and Analysis with Python
- Length: 435 pages
- Edition: 1
- Language: English
- Publication Date: 2021-08-28
- ISBN-10: B09DXKFMVZ
- Sales Rank: #0 (See Top 100 Books)
Python is one of the most powerful computer programming languages of all time, for several reasons we’ll discuss in the first section. The syntax is simple to learn and use and, compared to other programming languages, you often don’t need to write so much code. The sheer simplicity of the language helps programmers write more and develop programs that are more complex in less time.
This guide provides all you need to master the basics of programming with Python. I have kept it deliberately simple – it is a quick-start guide, after all. I have provided plenty of coding examples to show you how the syntax works, too, along with a guide on installing Python on Windows, Mac, and Linux systems. I finish with some useful tips on helping you to code better.
By the end of the guide, you will have a deeper understanding of the Python language, a stepping stone from which to take your learning further.
Please note that we are using Python 3 in this guide, not Python 2, as many similar guides do.
Are you ready to become a computer programmer? Let’s get started on this wonderful journey.
Introduction What is Python? Installing Python 3 - Windows Installing Python - Mac OSX Installing Python - Linux Running Python Programs Data Types and Variables Assign a Variable With a Value Using Comments Simultaneous Assignment Data Types Receiving An Input from The Console Importing a Module Python Numbers Determining Types Python Operators Operator Precedence Augmented Assignment Operator Python Strings Creating Strings String Operations Slicing Strings ord() and chr() Functions Python String Functions In and Not In Operators String Comparison String Iteration Using a For Loop Testing Strings Searching for Substrings Converting Strings Python Lists Creating Lists Accessing List Elements Common List Operations List Slicing + and * Operators in List in or not in Operator Using a For Loop to Traverse a List List Comprehension Python Dictionaries Creating a Dictionary Retrieving, Modifying And Adding Elements Deleting Items Looping Items Find the Dictionary Length in or not in Operators Equality Tests Dictionary Methods Python Tuples Creating A Tuple Tuples Functions Iterating Through Tuples Slicing Tuples In And Not In Operator Datatype Conversion Converting an int to a Float Converting a Float to an int Converting a String to an int Converting a Number to a String Rounding Numbers Python Control Statements Nested if Statements Python Functions Creating Functions Function With A Return Value Global Variables Vs. Local Variables Arguments With Default Values Keyword Arguments Combining Keyword and Positional Arguments Multiple Values Returned From Function Python Loops The for Loop range(a, b) Function The while Loop The break Statement The continue Statement Mathematical Functions Generating Random Numbers File Handling Open a File Close a File Append Data Using a For Loop Reading and Writing - Binary Objects and Classes Define a Class Self Objects Created from Classes How to Hide Data fields Operator Overloading Inheritance Multiple Inheritance Method Overriding isinstance() Function Exception Handling Raising Exceptions Exception Objects Create A Custom Exception Class Using A Custom Exception Class Modules Creating a Module Using the From Statement With Import Using the dir() Method Beginner Tips for Learning Python Conclusion References Introduction Chapter O n e : A n O v e r v i e w o f M a c h i n e Learning Machine Learning Categories Examples of Machine Learning Applications Classification: Predicting Discrete Labels Regression: Predicting Continuous Labels Clustering: Inferring the Labels on Unlabeled Data Dimensionality Reduction: Inferring the Structure of Unlabeled Data Chapter Two: Regression Machine Learning Models When is Regression Required? Different Types of Regression Techniques Linear Regression Logistic Regression Polynomial Regression Stepwise Regression Ridge Regression Lasso Regression ElasticNet Regression C h a p t e r T h r e e: C l a s s i f i c a t i o n M a c h i n e L e a r n i n g M o d e l s Different Classification Algorithms for Python Logistic Regression Naïve Bayes Stochastic Gradient Descent K-Nearest Neighbors Decision Tree Random Forest Support Vector Machine Accuracy C h a p t e r F o u r : U n s u p e r v i s e d M a c h i n e L e a r n i n g A l g o r i t h m s Why Choose Unsupervised Learning? Different Types of Unsupervised Machine Learning Clustering Types of Clustering Supervised Machine Learning vs. Unsupervised Machine Learning Unsupervised Machine Learning Applications The Disadvantages of Using Unsupervised Learning Chapter Five: Your First Machine Learning Project The Hello World of Python Machine Learning Chapter Six: An Introduction to Data Science What is Data Science? Using Pandas for Exploratory Analysis Using Pandas for Data Wrangling Building the Model How to Learn Python For Data Science Step 1: Learn The Fundamentals of the Python Language Step 2: Do Some Small Python Projects Step 3: Learn the Python Data Science Libraries Step 4: As You Learn Python, Build Up a Data Science Portfolio Step 5: Apply Some Advanced Techniques in Data Science Chapter Seven – Ten Things You Should Know About Machine Learning Conclusion Introduction Chapter 1: Basics of Data Science What is Data Science? Career Scope and Impact of Data Science Using Python Chapter 2: Various Aspects of Data Science Steps Involved in a Data Science Project Defining the Problem Collecting Data from Sources Data Processing Feature Engineering Algorithm Selection Hyperparameter Tuning Data Visualization Interpretation of Results How to Solve the Problems with Python Chapter 3: Python Exploratory Analysis with Pandas Understanding DataFrames and Series Data Set For Practice – A Loan Prediction Problem Beginning with Exploration How to Import the Data Set and Libraries Quick Data Exploration Distribution Analysis Categorical Variable Analysis Using Pandas for Data Munging in Python Checking for Missing Values Treating Extreme Values in a Distribution Chapter 4: Basics of Python for Data Analysis Why Do We Use Python v.3 And Not V.2|? Data Structures of Python Data Analysis in Python Using Pandas Data Science Using Python: Start Instantly Read the Tutorial Carefully Anaconda Jupyter Notebook Open New Notebook Math Calculations Data Importing Importing Dataset Exploration Clean the Dataset Features Develop an Easy Model Using Matplotlib Chapter 5: Metrics in Python along with Demo Changes when a System shows unusual Behavior What Are Metrics And How Many Types Of Metrics Are There? Counters Gauges Histograms or Timers Demo 1 Mean Median Percentile Histogram and Cumulative Histogram Demo 2 Network Applications Long Processes How to Monitor in a Python Application Chapter 6: How to Build a Predictive Model in Python Logistic Regression Decision Tree Data Prediction and Analysis C h a p t e r 7 : I n c o m e I n c r e m e n t u s i n g D a t a S c i e n c e w i t h P y t h o n Search Options Churn Prediction Churn Categories Data Science and Python: The Essential Relationship Learning Python for Data Science Conclusion Introduction Chapter 1: Data Analysis? Data Science? Or Machine Learning? Machine Learning and Data Analysis Limitations The Potential and the Implications Chapter 2: Get and Process Your Data CSV Files Internal Data Chapter 3: Data Visualization Importing and Using Matplotlib Supervised and Unsupervised Learning Chapter 4: A Deeper Look at Regression Multiple Linear Regression Decision Tree Regression Random Forest Regression Chapter 5: Digging into Classification Logistic Regression K-Nearest Neighbors Decision Tree Classification Random Forest Classification Chapter 6: A Look at Clustering Clustering Goals and Uses K-Means Clustering Anomaly Detection Chapter 7: What Is Reinforcement Learning? Reinforcement Learning Compared with Supervised and Unsupervised Learning How to Apply Reinforcement Learning Chapter 8: The Artificial Neural Network Imitating the Human Brain Conclusion References
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