{"product_id":"139055","title":"Learn Python and Data Science by Following ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPBvrr7KOZ0wYjFCFBDJqXO6ZoDP.png?v=1765072463\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Learn Python and Data Science by Following \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/123677210\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cdiv\u003e This book is for readers who want to quickly learn the Python language.\u003cbr\u003e It was also written for readers who want to broadly learn about data analysis, machine learning, and deep learning using Python.\u003cbr\u003e Python can do a lot with concise code, and that's the main reason it's enjoyed such acclaim. \u003cbr\u003eIn particular, it is the optimal language for data science, which is the most important field in recent computer science, and it is the language that can most efficiently carry out software development in the fields of machine learning and artificial intelligence.\u003cbr\u003e\u003cbr\u003e The authors wanted to do more than just explain Python's syntax to readers.\u003cbr\u003e For that reason, this book was designed to reveal Python's powerful capabilities and guide readers into a deeper and richer world of programming.\u003cbr\u003e The purpose of this book is to convey the core of the Python language to readers and help them handle numerical, text, and image data like experts.\u003cbr\u003e Additionally, example code and explanations were added here and there to help you understand object-oriented programming techniques, which are a major feature of Python.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003ePART 1 Building Basic Python Skills\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 01: Entering the World of Data Science and Python \u003cbr\u003e1.1 Discovering Hidden Treasures in Data: Entering the World of Data Science\u003cbr\u003e 1.2 Data processing process and program\u003cbr\u003e 1.3 Do I really need to know programming?\u003cbr\u003e 1.4 Installing Python Development Tools\u003cbr\u003e 1.5 Let's print 'Hello World' in Python development tools.\u003cbr\u003e 1.6 Let's start with the calculations first.\u003cbr\u003e 1.7 Let's learn about interactive mode and script mode.\u003cbr\u003e 1.8 Let's create visible results with turtle graphics.\u003cbr\u003e 1.9 Why Python is Really Convenient: Installing Modules\u003cbr\u003e LAB 1-1 Let's practice the print() function, which we will use frequently.\u003cbr\u003e LAB 1-2 Let's draw a triangle with turtle graphics.\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 02 Let's handle values\u003cbr\u003e 2.1 Space to store data: Variables\u003cbr\u003e 2.2 How to name variables\u003cbr\u003e LAB 2-1 Calculating Body Mass Index with Python\u003cbr\u003e LAB 2-2 Let's calculate the area of ​​a pizza.\u003cbr\u003e LAB 2-3 Drawing Pizza with Turtle Graphics\u003cbr\u003e LAB 2-4 Calculating Compound Interest  \u003cbr\u003e2.3 What are the benefits of using variables?\u003cbr\u003e 2.4 To know the data type of a variable: type() function\u003cbr\u003e 2.5 Limitations of computer numerical representation and limitations of computers\u003cbr\u003e 2.6 How to create a string\u003cbr\u003e 2.7 Why an error occurs: Data type conversion\u003cbr\u003e 2.8 Getting integer input from the user\u003cbr\u003e LAB 2-5 Robot Reporter Writes Baseball Articles\u003cbr\u003e 2.9 Objects, Methods, and Functions\u003cbr\u003e LAB 2-6 Let's Challenge Yourself to Create a Real Estate Advertisement\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 03 Let's do calculations with operators\u003cbr\u003e 3.1 Formulas are everywhere\u003cbr\u003e 3.2 How to use formulas and operators\u003cbr\u003e 3.3 Exponentiation Operator: **\u003cbr\u003e LAB 3-1 Converting Fahrenheit to Celsius\u003cbr\u003e LAB 3-2 Calculating BMI by Entering Weight and Height\u003cbr\u003e LAB 3-3 Let's create a vending machine program.\u003cbr\u003e 3.4 A convenient operator called the compound assignment operator\u003cbr\u003e 3.5 AND, OR, NOT can also be used as operators: Logical operators\u003cbr\u003e LAB 3-4 Finding the Mean - Operator Precedence  \u003cbr\u003e3.6 Operators optimized for computers that handle binary numbers well: Bitwise operators\u003cbr\u003e 3.7 There are also things that are processed first between operators.\u003cbr\u003e 3.8 Let's try out various functions with the random and math modules.\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 04 Let's run it by considering the conditions.\u003cbr\u003e 4.1 There are three main control structures in a program.\u003cbr\u003e 4.2 If statement that executes only when a condition is met\u003cbr\u003e 4.3 if-else statement executed according to exclusive condition\u003cbr\u003e 4.4 Various Turtle Graphics Commands\u003cbr\u003e 4.5 Turtle Objects and Screen Objects\u003cbr\u003e LAB 4-1 Let's control turtle graphics based on input numbers.\u003cbr\u003e LAB 4-2 Let's check the age limit for watching the movie.\u003cbr\u003e LAB 4-3 Controlling the Turtle\u003cbr\u003e LAB 4-4 How to determine leap year\u003cbr\u003e LAB 4-5 Let's create a coin tossing game using a random function.\u003cbr\u003e LAB 4-6 Is it a point inside or outside the circle?\u003cbr\u003e 4.6 Checking other conditions consecutively when a condition is false\u003cbr\u003e LAB 4-7 Login Processing  \u003cbr\u003eLAB 4-8 Let's create a penalty shootout game with the computer.\u003cbr\u003e Let's try drawing a shape using LAB 4-9 input.\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 05 Let's do something that is repeated many times\u003cbr\u003e 5.1 Why loops are important\u003cbr\u003e 5.2 Set a number of repetitions\u003cbr\u003e 5.3 The range() function, a perfect match for the for loop\u003cbr\u003e LAB 5-1 Let's draw several circles using turtle graphics.\u003cbr\u003e LAB 5-2 Let's draw shapes using repetition\u003cbr\u003e LAB 5-3 Drawing N-gons\u003cbr\u003e LAB 5-4 Calculating Factorials Using Iteration\u003cbr\u003e 5.4 While statement that executes repeatedly according to conditions\u003cbr\u003e Log in by receiving a password from a LAB 5-5 user\u003cbr\u003e 5.5 Using while for a fixed number of repetitions\u003cbr\u003e LAB 5-6 Outputting the multiplication table using the input numbers\u003cbr\u003e LAB 5-7 Let's draw stars using the while loop.\u003cbr\u003e LAB 5-8 Draw a cool spiral shape with simple code\u003cbr\u003e LAB 5-9 Let's create a number guessing game using an infinite loop.\u003cbr\u003e LAB 5-10 Let's create a mental arithmetic problem  \u003cbr\u003e5.6 Breaking out of an infinite loop with break\u003cbr\u003e 5.7 Formatting to make the output look pretty\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 06: Organizing work with functions\u003cbr\u003e 6.1 A cool feature for creating structured functionality: functions\u003cbr\u003e 6.2 Let's create a function and call it to do some work 158\u003cbr\u003e 6.3 Let's make the function do some work and get the value back.\u003cbr\u003e 6.4 Passing multiple values ​​and receiving multiple values ​​back\u003cbr\u003e LAB 6-1 Creating a function to draw a rectangle\u003cbr\u003e 6.5 What is the scope of a variable?\u003cbr\u003e 6.6 Default arguments that make functions work easier\u003cbr\u003e LAB 6-2 Weekly Wage Calculation Program\u003cbr\u003e LAB 6-3 Creating a function to draw an n-gon\u003cbr\u003e LAB 6-4 Function to find maximum\/minimum values ​​in a list\u003cbr\u003e LAB 6-5 A function that finds the maximum\/minimum value in a list and returns two of them.\u003cbr\u003e 6.7 Recursive functions that call themselves\u003cbr\u003e LAB 6-6 Calculating the Fibonacci Function\u003cbr\u003e 6.8 Let's reuse functions using modules\u003cbr\u003e 6.9 Creating Modules and Aliases\u003cbr\u003e Key Summary  \u003cbr\u003esubjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 07 Let's group data into lists and tuples.\u003cbr\u003e 7.1 What is a list and why is it needed?\u003cbr\u003e 7.2 Let's try list operations\u003cbr\u003e Let's create a list of delicious fruits using LAB 7-1 input.\u003cbr\u003e LAB 7-2 Let's find the prime numbers from 2 to 100.\u003cbr\u003e 7.3 Let's try indexing and slicing.\u003cbr\u003e 7.4 Let's freely manipulate the element values ​​of the list.\u003cbr\u003e LAB 7-3 Let's slice the city's population data.\u003cbr\u003e 7.5 List methods and various functions\u003cbr\u003e 7.6 Let's sort the list by size.\u003cbr\u003e LAB 7-4 Let's create a feature that selects today's famous quote.\u003cbr\u003e LAB 7-5 Let's group the city name and population into a tuple.\u003cbr\u003e 7.7 Reinventing the wheel?\u003cbr\u003e 7.8 In-depth concept of creating and referencing list objects\u003cbr\u003e 7.9 List comprehensions are used to make code shorter and more concise.\u003cbr\u003e 7.10 Data types whose values ​​cannot be changed once created: Tuples\u003cbr\u003e 7.11 Integration using the zip() function\u003cbr\u003e 7.12 What are classes and objects?  \u003cbr\u003eKey Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 08 Pairing Related Data into a Dictionary\u003cbr\u003e 8.1 Let's store data as a dictionary with keys and values.\u003cbr\u003e 8.2 Comparison of Dictionaries and Lists\u003cbr\u003e 8.3 Various methods of dictionaries\u003cbr\u003e 8.4 Lambda function = function without a name\u003cbr\u003e LAB 8-1 Let's Create a Convenience Store Inventory Management Program\u003cbr\u003e LAB 8-2 Let's Make an English-Korean Dictionary\u003cbr\u003e 8.5 When objects whose order is not important are gathered together: a set\u003cbr\u003e 8.6 Let's look at various operations that can be applied to sets.\u003cbr\u003e 8.7 Compare lists, tuples, sets, and dictionaries\u003cbr\u003e Find out who attended the LAB 8-3 party at the same time\u003cbr\u003e Let's read and save data from file 8.8.\u003cbr\u003e Finding words used in LAB 8-4 files\u003cbr\u003e 8.9 Divisors and greatest common divisor of two numbers and programming thinking\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003ePART 2 DATA SCIENCE AND ARTIFICIAL INTELLIGENCE\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 09 Let's Process Text\u003cbr\u003e 9.1 The Impact of ChatGPT\u003cbr\u003e 9.2 Basic Text Processing  \u003cbr\u003e9.3 How to change and format text\u003cbr\u003e LAB 9-1 Character Count, Word Count, Average Word Length\u003cbr\u003e LAB 9-2 Removing Stop Words\u003cbr\u003e 9.4 Let's handle stop words easily\u003cbr\u003e LAB 9-3 Twitter Message Processing\u003cbr\u003e LAB 9-4 Word Frequency Calculation\u003cbr\u003e LAB 9-5 Movie Review Analysis\u003cbr\u003e 9.5 Word Cloud\u003cbr\u003e 9.6 Creating Korean word clouds and images\u003cbr\u003e LAB 9-6 Wikipedia Word Cloud\u003cbr\u003e 9.7 Let's learn regular expressions\u003cbr\u003e 9.8 Finding Specific Patterns Using Regular Expressions\u003cbr\u003e 9.9 Replacing Patterns Using Regular Expressions\u003cbr\u003e LAB 9-7 Let's remove HTML tags\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 10: Processing Numeric Data with NumPy\u003cbr\u003e 10.1 NumPy arrays are much faster than lists.\u003cbr\u003e 10.2 Creating NumPy Aliases and Performing Simple Array Operations\u003cbr\u003e 10.3 Let's learn about powerful NumPy array operations.\u003cbr\u003e LAB 10-1 Let's create an ndarray object and learn about its properties.\u003cbr\u003e LAB 10-2 Calculating BMI for Multiple People Quickly and Easily  \u003cbr\u003e10.4 Indexing and slicing can also be done in NumPy.\u003cbr\u003e 10.5 Indexing Two-Dimensional Arrays\u003cbr\u003e 10.6 Two-dimensional array slicing in NumPy style\u003cbr\u003e LAB 10-3 Two-Dimensional Array Practice\u003cbr\u003e LAB 10-4 Finding the shape of a NumPy array and performing operations by slicing\u003cbr\u003e 10.7 Comparison of the arange() and range() functions\u003cbr\u003e LAB 10-5 Extracting only rows that satisfy a specific condition from a two-dimensional array\u003cbr\u003e 10.8 linspace() and logspace() functions\u003cbr\u003e 10.9 Let's generate random numbers\u003cbr\u003e 10.10 Generating Normally Distributed Random Numbers\u003cbr\u003e Let's change the shape of the LAB 10-6 array.\u003cbr\u003e LAB 10-7 Practice Calculating Mean and Median\u003cbr\u003e 10.11 Calculating Correlations\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 11 Let's Draw a Cool Chart\u003cbr\u003e 11.1 Data Visualization\u003cbr\u003e 11.2 Try using matplotlib blindly\u003cbr\u003e 11.3 Various techniques to help decorate charts\u003cbr\u003e LAB 11-1 Let's draw math functions easily.\u003cbr\u003e 11.4 Let's plot multiple data on one chart.\u003cbr\u003e LAB 11-2 Drawing the graph of sine, the basic trigonometric function  \u003cbr\u003e11.5 Let's draw a bar chart easily.\u003cbr\u003e 11.6 Drawing a Scatter Graph Representing Data as Points\u003cbr\u003e 11.7 Let's take a quick look at the data distribution with a histogram.\u003cbr\u003e LAB 11-3 Visually verifying random numbers generated from a normal distribution\u003cbr\u003e LAB 11-4 Let's display sales by vehicle type in a pie chart.\u003cbr\u003e 11.8 Let's learn about box charts, which effectively represent data.\u003cbr\u003e 11.9 Drawing multiple graphs on one screen: subplots( )\u003cbr\u003e Using the LAB 11-5 subplot\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 12: Analyzing Data with Pandas\u003cbr\u003e 12.1 Pandas for faster processing than Excel\u003cbr\u003e Have you heard of 12.2 CSV?\u003cbr\u003e 12.3 Let's extract the desired data from CSV.\u003cbr\u003e LAB 12-1 In which month is the wind strongest on Ulleungdo?\u003cbr\u003e 12.4 Pandas Data Structures: Series and DataFrames\u003cbr\u003e 12.5 Reading Data Files with Pandas\u003cbr\u003e 12.6 Selecting data by column\u003cbr\u003e 12.7 Selecting rows by slicing  \u003cbr\u003e12.8 There is a function to easily analyze data.\u003cbr\u003e 12.9 DatetimeIndex and Grouping for Year, Month, and Day\u003cbr\u003e 12.10 Grouping data based on specific values: Grouping\u003cbr\u003e LAB 12-2 In which month is the windiest on Ulleungdo? - Using groupby()\u003cbr\u003e 12.11 Let's select according to the conditions: Filtering\u003cbr\u003e 12.12 Let's fill in the missing data.\u003cbr\u003e 12.13 Let's change the data structure.\u003cbr\u003e LAB 12-3 Applying concat in various ways\u003cbr\u003e 12.14 Merging data using database join method - merge\u003cbr\u003e LAB 12-4 Applying Merge in Various Ways\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 13: A Sample for Understanding the Nature of Data\u003cbr\u003e 13.1 Let's find out the relationship between the data.\u003cbr\u003e 13.2 Introduction to the Google Colab Environment\u003cbr\u003e 13.3 Correlation Coefficients and Visualization\u003cbr\u003e 13.4 Visualizing and Interpreting Correlation Coefficients\u003cbr\u003e 13.5 Getting Started with a Simple Seaborn Tutorial\u003cbr\u003e 13.6 Tips Data Structure\u003cbr\u003e 13.7 Let's show the relationship in detail with a scatter plot graph.  \u003cbr\u003e13.8 Paired graphs are useful for exploring relationships between variables.\u003cbr\u003e 13.9 Anscombe's quartet data set\u003cbr\u003e 13.10 Describing Data Using Nonlinear Functions\u003cbr\u003e 13.11 Airline Passenger Data Set\u003cbr\u003e 13.12 Let's fix the airline passenger data set.\u003cbr\u003e 13.13 Let's take a look at the heatmap.\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 14: Building Smart Computers with Machine Learning\u003cbr\u003e 14.1 The Computer Program That Beat Lee Sedol: What's the Secret?\u003cbr\u003e 14.2 Let's dive deeper into machine learning.\u003cbr\u003e 14.3 Let's learn about regression problems.\u003cbr\u003e 14.4 The Simplest Regression: Linear Regression Analysis\u003cbr\u003e 14.5 Predicting with Linear Regression: Are Height and Weight Correlated?\u003cbr\u003e LAB 14-1 Men and women will have different weights even if their heights are similar: Multidimensional linear regression\u003cbr\u003e LAB 14-2 Housing's actual area, public transportation accessibility, and price\u003cbr\u003e 14.6 Creating a Diabetes Example and Training Data in Scikit-Learn\u003cbr\u003e 14.7 What is the correlation between body mass index and blood sugar levels?  \u003cbr\u003e14.8 Let's divide the diabetes example into training and test data.\u003cbr\u003e LAB 14-3: Comparison of predicted results and actual data using 80% of the data\u003cbr\u003e 14.9 Errors in the Algorithm\u003cbr\u003e 14.10 Problem of Classifying Dachshunds and Samoyeds\u003cbr\u003e 14.11 Classification using the k-NN algorithm\u003cbr\u003e 14.12 Let's look at the data to which the k-NN algorithm will be applied.\u003cbr\u003e 14.13 Let's apply the model to new flowers and classify them.\u003cbr\u003e 14.14 Case Study - Linear Regression: Predicting Life Expectancy\u003cbr\u003e 14.15 Let's look at the correlation between each feature.\u003cbr\u003e 14.16 Let's create a simple regression model.\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003cbr\u003e\u003cbr\u003e Chapter 15: A Taste of Deep Learning\u003cbr\u003e 15.1 Perceptrons that mimic human neurons\u003cbr\u003e 15.2 Let's learn about the structure of deep learning.\u003cbr\u003e 15.3 Let's learn about teachable machines.\u003cbr\u003e 15.4 Recognizing Images Without Coding with Teachable Machines\u003cbr\u003e 15.5 The Most Popular Machine Learning and Deep Learning Platform: TensorFlow\u003cbr\u003e 15.6 Let's look at an example data called MNIST.  \u003cbr\u003e15.7 Structure of MNIST Data and Images\u003cbr\u003e 15.8 Steps to Create a Deep Learning Model\u003cbr\u003e 15.9 Let's train a deep learning model.\u003cbr\u003e 15.10 Let's predict the image\u003cbr\u003e 15.11 Explore the example data called Fashion MNIST\u003cbr\u003e 15.12 Let's build an artificial neural network again.\u003cbr\u003e 15.13 Let's apply the trained neural network to new image recognition.\u003cbr\u003e 15.14 Let's see with our own eyes how much better it is to build a 15.14 floor.\u003cbr\u003e Key Summary\u003cbr\u003e subjective questions\u003cbr\u003e Advanced problems\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eFeatures of the revised edition\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e - Includes friendly, detailed explanations and many example illustrations for readers who are new to Python.\u003cbr\u003e - Each chapter is divided into short sections that fit the core topic, allowing you to grasp the main points concisely.\u003cbr\u003e - A chapter on learning the Seaborn library for data visualization, which was lacking in the first edition, has been added.\u003cbr\u003e - Includes labs and challenge problems that allow readers to practice and check their understanding. \u003cbr\u003e- It starts with the basics of Python, but covers data processing and visualization techniques that can be applied in practice.\u003cbr\u003e - We have made it easy for readers to understand the basics of machine learning and artificial intelligence.\u003cbr\u003e - We used sckit-learn, a major machine learning library, and the latest version of TensorFlow 2.0.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eStructure of this book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.\u003cbr\u003e 'What You'll Learn in This Chapter' begins each chapter with a summary of the key points the reader should know from that chapter.\u003cbr\u003e 2.\u003cbr\u003e Each chapter is divided into small sections of 1-2 pages, and the section titles indicate the main topic.\u003cbr\u003e 3.\u003cbr\u003e Through 'challenge problems', readers can review what they have learned and enjoy the pleasure of solving problems.\u003cbr\u003e 4.\u003cbr\u003e The \"Just a Minute\" column is a place to take a break, filled with a wealth of current events and common sense information related to data science that readers might find useful.\u003cbr\u003e 5. \u003cbr\u003e'LAB' contains problems that allow you to practice what you have learned in each section.\u003cbr\u003e Not only problems, but also hints and solution codes are provided, so you can improve your coding skills by comparing your solutions with the solution codes.\u003cbr\u003e 6.\u003cbr\u003e You can review the key points of each chapter through 'Key Summary'.\u003cbr\u003e 7.\u003cbr\u003e You can review the important contents of each chapter through 'subjective questions'.\u003cbr\u003e 8.\u003cbr\u003e 'Advanced Problems' contains a variety of problems that will help you further review the content learned in each chapter. \u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e November 20, 2023\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 484 pages | 215*275*30mm\u003c\/div\u003e\n\n\u003cdiv 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