COMPUTER PROGRAMMING: 4 Books in 1: Data Science, Hacking with Kali Linux, Computer Networking for Beginners, Python Programming. Coding Language for Machine Learning and Artificial Intelligence

BOOK 1: Python for Data Science 9 Chapter 1: Introduction to Data Analysis 13 Python for Data Science 13 Why Select Python 13 Python vs. R 14 Widespread Application of Data Analysis 14 Clarity 15 Types of Data Analysis 15 Descriptive Analysis 15 Predictive Analysis 15 Prescriptive Analysis 15 Why Data Analysis Is on the Rise? 16 Summary of the Data Science Process 16 Prerequisite and Reminders 18 Do You Need Some Expertise in Mathematics? 18 Chapter 2: Python Review 20 Properties 20 Getting help 20 Data Analysis vs. Data Science vs. Machine Learning 20 Possibilities 21 Drawbacks of Data Analysis & Machine Learning 21 Accuracy & Performance 22 Chapter 3: Important Python Libraries 23 Install the Software and Setting Up 23 For Windows 23 Numpy Arrays 24 Using IPython as a Shell 24 Web Scraping Using Python Libraries 25 Web Scraping 25 Matplotlib 28 2nd generation 28 Chapter 4: Data Manipulation 29 6 Key Things You Need to Know About Numpy and Pandas 29 Getting Started with Numpy 30 Array Indexing 30 Array Slicing 31 Array Concatenation 32 Getting Started with Pandas 33 Importing Data 37 Missing Data 38 Visualize the Data 38 Transformation of Feature 40 Chapter 5: Data Aggregation 42 Definition of Data Frame 42 Split-Apply-Combine 42 Implementation 42 How to Group Data Frames? 44 Chapter 6: Data Visualization 46 Data Visualization to the End-User 46 Matplotlib 47 Line Chart 48 Histogram 48 Bar Chart 48 Visualization Using Pandas 48 The Objective of Visualization 48 The Simplest Method to Complex Visualization of Data 49 Overview of Plotly 50 Building Attractive Plots Using Plotly 50 Scatter Plots 50 Box Plots 51 Heat Maps 52 Chapter 7: Machine Learning 54 Machine Learning Algorithms Classifications 54 Supervised Learning 54 Unsupervised Learning 54 Reinforcement Learning 55 How to Approach a Problem 55 What is Deep Learning 56 Neural Networks with Scikit-Learn 56 The Structure of Neuron 57 Back Propagation 57 Scikit-Learn 58 The Neural Networks Using TensorFlow 58 TensorFlow 58 Preparing the Environment 59 Installing Scikit-Learn 59 Import Scikitlearn 60 Installing TensorFlow 60 Chapter 8: Artificial Neural Networks 62 How the Brain Works 63 Constraints and Opportunities 64 Let’s See an Example 64 Chapter 9: How to use Scikit-Learn 68 Loading Datasets 68 Simple Linear Regression 68 Import Libraries 68 Data Preparation 70 Training the Algorithm 71 Predicting 71 Evaluating the Accuracy 72 Multiple Linear Regression 72 Data Preparation 73 Training the Algorithm 74 Predicting 74 Evaluating the Accuracy 75 Chapter 10: K-Nearest Neighbors Algorithm 76 Splitting the Dataset 76 Feature Scaling 76 Training the Algorithm 77 Evaluating the Accuracy 77 K Means Clustering 78 Data Preparation 79 Visualizing the Data 79 Creating Clusters 80 Chapter 11: Classification 83 Logistics Regression 83 K-Nearest Neighbors 84 The Decision Tree Classification 85 Random Forest Classification 87 Clustering 88 Objectives and Function of Clustering 89 K-Means Clustering 89 Anomaly Detection 92 Chapter 12: Association Rule Learning 94 Explanation 94 Apriori 95 Chapter 13: Reinforcement Learning 98 What is Reinforcement Learning? 98 Comparison with Supervised & Unsupervised Learning 99 Applying Reinforcement Learning 99 Mastering the Bagging Method 102 How to Do It 102 Conclusion 104 BOOK 2: Hacking With Kali Linux 106 Chapter 1: Basics of hacking 110 Chapter 2: What is Ethical Hacking? 115 Chapter 3: Cyber Security 119 Chapter 4: Linux Architecture 123 Chapter 5: Basics of Linux Operating System 128 Chapter 6: Basic Linux Commands 132 Chapter 7: Characteristics of Kali Linux and Why It Is So Important In The Hacking World 136 Chapter 8: Installation of Kali Linux 140 Chapter 9: Applications and Use of Kali Linux 143 Chapter 10: Different Tools of Kali Linux 148 Chapter 11: How can Kali Linux be Used For Hacking? 152 Chapter 12: Techniques of Port Scanning using Kali Linux 157 Chapter 13: Penetration Testing 161 Chapter 14: VPN 164 Chapter 15: Firewall 168 Chapter 16: Cryptography 171 Conclusion 173 BOOK 3: COMPUTER NETWORKING FOR BEGINNERS 174 Introduction 177 Chapter 1: Computer networking: An Introduction 178 Networking Essentials 179 Networks Types 185 The OSI Model 189 Computer Network Components 194 Basic Network Troubleshooting 197 Chapter 2: Network Management 199 Hardware Management and Maintenance 200 Virtualization in Cloud Computing 203 The Concept behind Virtualization 206 Chapter 3: Computer Network Communication Technologies 208 How computers communicate in a network 209 Understanding Ethernet 210 Peer-to-Peer Communication 214 Chapter 4: The Internet 216 Internet basics 217 Sub-net Mask 218 Private Networks 221 Chapter 5: Router and Server Basics 225 Routing Types 227 Network Servers 229 Understanding VLAN 231 Chapter 6: IP addressing and IP sub-netting 233 What is an IP address? 234 IP Sub-netting 235 IPv4 vs. IPv6 236 Chapter 7: Introduction to Cisco System and CCNA Certification 237 Chapter 8: Fundamentals of Network Security 240 Network Intruders 242 What can be done about these threats? 245 Network security best practices 248 Chapter 9: Wireless Technology and Security 249 Chapter 10: Introduction to Machine Learning: A Computer Networking Perspective 257 What is Machine Learning? 258 Machine Learning in Analytics 261 Machine Learning in Management 262 Machine Learning in Security 263 Conclusion 264 BOOK 4: Python Programming 265 Introduction 266 Chapter 1: What is the Python Language, and Why Should I Use It? 268 How to Use Python 269 The Benefits of Python 271 Chapter 2: How Can I Install Python on My Computer? 273 Installing Python on Mac OS X 275 Python – V 275 Python3 – V 275 Installing Python on a Windows System 275 Installing Python on a Linux Operating System 277 Chapter 3: The Basics of the Python Code 279 The Keywords 279 Looking at the Comments 279 The Importance of Variables 280 Bringing in Python Strings 281 The Python Functions 282 The Operators 283 Chapter 4: The Different Data Types in Python 284 Chapter 5: The Python Functions 289 The Different Types of Functions 289 The Advantages of Python Functions 289 The Syntax of the Function 290 Chapter 6: How to Write Your Own Conditional Statements 292 Starting with the If Statement 292 Moving to the If Else Statements 293 Finishing Off with the Elif Statements 294 Chapter 7: The Python Classes and How to Write Your Own 296 Chapter 8: Handling Files in Python 300 Creating Our Own New Files 300 Can I Create a Binary File? 302 How to Open a File 302 How to Seek One of the Files 303 Chapter 9: Tips and Tricks to Get the Most Out of Python 305 Comment Out the Code 305 Print Things Out 306 Work with the Code You Know Will Behave 306 Read All of the Error Messages 308 Run the Code Often 308 Take a Break When Needed 308 Ask for Help 309 Chapter 10: A Quick Introduction to Data Analysis 311 Define the Question 313 Pick Out the Important Measuring Options 313 Collect the Data 315 Look Through and Analyze the Data 316 Interpret the Results 317 Chapter 11: Some of the Best Python Algorithms for Data Analysis 319 Neural Networks 319 Clustering 320 Support Vector Machines 320 Naïve Bayes 321 Decision Trees 321 Conclusion 323