Play It Safe: Manage Security Risks

 

What you'll learn

Identify the primary threats, risks, and vulnerabilities to business operations

Examine how organizations use security frameworks and controls to protect business operations

Define commonly used Security Information and Event Management (SIEM) tools

Use a playbook to respond to threats, risks, and vulnerabilities

Join Free:Play It Safe: Manage Security Risks

There are 4 modules in this course

This is the second course in the Google Cybersecurity Certificate. These courses will equip you with the skills you need to apply for an entry-level cybersecurity job. You’ll build on your understanding of the topics that were introduced in the first Google Cybersecurity Certificate course.

In this course, you will take a deeper dive into concepts introduced in the first course, with an emphasis on how cybersecurity professionals use frameworks and controls to protect business operations. In particular, you'll identify the steps of risk management and explore common threats, risks, and vulnerabilities. Additionally, you'll explore Security Information and Event Management (SIEM) data and use a playbook to respond to identified threats, risks, and vulnerabilities. Finally, you will take an important step towards becoming a cybersecurity professional and practice performing a security audit.

Google employees who currently work in cybersecurity will guide you through videos, provide hands-on activities and examples that simulate common cybersecurity tasks, and help you build your skills to prepare for jobs. 

Learners who complete this certificate will be equipped to apply for entry-level cybersecurity roles. No previous experience is necessary.

By the end of this course, you will: 

- Identify the common threats, risks, and vulnerabilities to business operations.

- Understand the threats, risks, and vulnerabilities that entry-level cybersecurity analysts are most focused on.

- Comprehend the purpose of security frameworks and controls.

- Describe the confidentiality, integrity, and availability (CIA) triad.

- Explain the National Institute of Standards and Technology (NIST) framework.

- Explore and practice conducting a security audit.

- Use a playbook to respond to threats, risks, and vulnerabilities.

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Generative AI Fundamentals Specialization

What you'll learn

Explain the fundamental concepts, capabilities, models, tools, applications, and platforms of generative AI foundation models.

Apply powerful prompt engineering techniques to write effective prompts and generate desired outcomes from AI models.

Discuss the limitations of generative AI and explain the ethical concerns and considerations for the responsible use of generative AI.

Recognize the ability of generative AI to enhance your career and help implement improvements at your workplace.

Join Free: Generative AI Fundamentals Specialization

Specialization - 5 course series

Generative AI is revolutionizing our lives.

This specialization provides a comprehensive understanding of the fundamental concepts, models, tools, and applications of generative AI to enable you to leverage the potential of generative AI toward a better workplace, career, and life.

The specialization consists of five short, self-paced courses, each requiring 3–5 hours to complete.

Understand powerful prompt engineering techniques and learn how to write effective prompts to produce desired outcomes using generative AI tools.

Learn about the building blocks and foundation models of generative AI, such as the GPT, DALL-E, and IBM Granite models. Gain an understanding of the ethical implications, considerations, and issues of generative AI.

Listen to experts share insights and tips for being successful with generative AI. Learn to leverage Generative AI to boost your career and become more productive.

Practice what you learn using hands-on labs and projects, which are suitable for everyone and can be completed using a web browser. These labs will give you an opportunity to explore the use cases of generative AI through popular tools and platforms like IBM watsonx.ai, OpenAI ChatGPT, Stable Diffusion, and Hugging Face.

This specialization is for anyone passionate about discovering the power of generative AI and requires no prior technical knowledge or a background in AI. It will benefit professionals from all walks of life.

Applied Learning Project

Throughout this specialization, you will complete hands-on labs and projects to help you gain practical experience with text, image, and code generation, prompt engineering tools, foundation models, AI applications, and IBM watsonx.ai.

Some examples of the labs included are:

Text generation using ChatGPT and Bard

Image generation using GPT and Stable Diffusion

Code generation in action

Getting to know prompting tools

Experimenting with prompts

Different approaches in prompt engineering

Generative AI foundation models

Exploring IBM watsonx.ai and Hugging Face

 

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Generative AI for Everyone

 

What you'll learn

What generative AI is and how it works, its common use cases, and what this technology can and cannot do.

How to think through the lifecycle of a generative AI project, from conception to launch, including how to build effective prompts.

The potential opportunities and risks that generative AI technologies present to individuals, businesses, and society.

Join Free: Generative AI for Everyone

There are 3 modules in this course

Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. It includes hands-on exercises where you'll learn to use generative AI to help in day-to-day work and receive tips on effective prompt engineering, as well as learning how to go beyond prompting for more advanced uses of AI.

You’ll get insights into what generative AI can do, its potential, and its limitations. You’ll delve into real-world applications and learn common use cases. You’ll get hands-on time with generative AI projects to put your knowledge into action and gain insight into its impact on both business and society. 

This course was created to ensure everyone can be a participant in our AI-powered future.

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Algorithms for Decision Making (Free PDF)

 

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.

Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.

The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Buy : Algorithms for Decision Making by Mykel J. Kochenderfer (Author), Tim A. Wheeler (Author), Kyle H. Wray (Author)

PDF Download : Algorithms for Decision Making

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Computer Graphics [CGR]

 Basic Concepts:

a. Define computer graphics and explain its significance.

b. Differentiate between raster and vector graphics.

Graphics Primitives:

a. Discuss the difference between points, lines, and polygons as graphics primitives.

b. Explain the concept of anti-aliasing in the context of computer graphics.

2D Transformations:

a. Describe the translation, rotation, and scaling transformations in 2D graphics.

b. Provide examples of homogeneous coordinates in 2D transformations.

Clipping and Windowing:

a. Explain the need for clipping in computer graphics.

b. Discuss the Cohen-Sutherland line-clipping algorithm.

3D Transformations:

a. Describe the translation, rotation, and scaling transformations in 3D graphics.

b. Explain the concept of perspective projection.

Hidden Surface Removal:

a. Discuss the challenges of hidden surface removal in 3D graphics.

b. Explain the Z-buffer algorithm.

Color Models:

a. Describe the RGB and CMY color models.

b. Explain the concept of color depth.

Rasterization:

a. Discuss the process of scan conversion in computer graphics.

b. Explain the Bresenham's line-drawing algorithm.

Computer Animation:

a. Define keyframes and in-betweening in computer animation.

b. Discuss the principles of skeletal animation.

Ray Tracing:

a. Explain the concept of ray tracing in computer graphics.

b. Discuss the advantages and disadvantages of ray tracing.

OpenGL:

a. Describe the OpenGL graphics pipeline.

b. Explain the purpose of the Model-View-Projection (MVP) matrix in OpenGL.

Virtual Reality (VR):

a. Define virtual reality and its applications in computer graphics.

b. Discuss the challenges of achieving realism in virtual reality.

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Digital Techniques [DTE]

 Number Systems:

a. Convert the binary number 101010 to its decimal equivalent.

b. Explain the concept of two's complement in binary representation.

Logic Gates:

a. Implement the XOR gate using only NAND gates.

b. Explain the truth table for a half adder circuit.

Combinational Circuits:

a. Design a 4-to-1 multiplexer using basic logic gates.

b. Implement a full subtractor circuit.

Sequential Circuits:

a. Describe the operation of a D flip-flop.

b. Design a 3-bit binary counter using JK flip-flops.

Registers and Counters:

a. Explain the difference between a shift register and a parallel-in/serial-out register.

b. Design a 4-bit synchronous up-counter using T flip-flops.

Memory Units:

a. Define the terms RAM and ROM.

b. Explain the concept of memory decoding in digital systems.

Digital-to-Analog Conversion:

a. Describe the operation of a weighted resistor digital-to-analog converter.

b. Explain the purpose of a sample-and-hold circuit in digital-to-analog conversion.

Analog-to-Digital Conversion:

a. Discuss the successive approximation method for analog-to-digital conversion.

b. Explain the concept of quantization error in the context of analog-to-digital conversion.

Multiplexers and Demultiplexers:

a. Design an 8-to-1 multiplexer using 4-to-1 multiplexers.

b. Explain the function of a demultiplexer.

Digital Logic Families:

a. Compare and contrast TTL and CMOS logic families.

b. Discuss the advantages and disadvantages of ECL logic.

Programmable Logic Devices (PLDs):

a. Describe the types of PLDs and their applications.

b. Explain the concept of a look-up table (LUT) in programmable logic.

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Object Oriented Programming [OOP]

 Basic Concepts:

a. Define what an object is in the context of OOP.

b. Explain the difference between a class and an object.

Encapsulation:

a. Describe the concept of encapsulation and its benefits.

b. Provide an example of encapsulation in a programming language of your choice.

Inheritance:

a. Explain the concept of inheritance and its purpose.

b. Differentiate between single inheritance and multiple inheritance.

Polymorphism:

a. Define polymorphism and explain its types.

b. Provide an example of compile-time and runtime polymorphism.

Abstraction:

a. Discuss the importance of abstraction in OOP.

b. Provide an example of abstraction in a real-world scenario.

Class and Object Relationships:

a. Explain the difference between a class method and an instance method.

b. Describe the concept of composition in OOP.

Interfaces and Abstract Classes:

a. Define an interface and explain its role in OOP.

b. Differentiate between an interface and an abstract class.

Design Patterns:

a. Discuss the singleton design pattern and its use cases.

b. Explain the observer design pattern.

Exception Handling:

a. Describe how exception handling is implemented in an object-oriented language.

b. Discuss the importance of try-catch blocks in OOP.

Object-Oriented Analysis and Design (OOAD):

a. Explain the phases of Object-Oriented Analysis and Design.

b. Discuss the importance of UML (Unified Modeling Language) in OOAD.

Generic Programming:

a. Define generic programming and its advantages.

b. Provide an example of using generics in a programming language.

Reflection:

a. Explain the concept of reflection in OOP.

b. Discuss situations where reflection can be useful.

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Database Management System [DBMS]

 Basics of DBMS:

a. Define what a database is and explain its advantages.

b. Differentiate between DBMS and RDBMS.

Relational Database Concepts:

a. Define the terms: table, tuple, attribute, and primary key.

b. Explain the concept of normalization and its importance in a relational database.

SQL Queries:

a. Write an SQL query to retrieve all records from a table named "Employees."

b. Explain the differences between the WHERE and HAVING clauses in SQL.

Database Design:

a. What is the purpose of a foreign key in a relational database?

b. Describe the steps involved in the normalization process.

Transaction Management:

a. Define the ACID properties in the context of database transactions.

b. Explain the concepts of commit and rollback in a database transaction.

Indexing and Query Optimization:

a. Discuss the importance of indexing in a database.

b. Explain how the query optimizer works in a relational database system.

Concurrency Control:

a. What is a deadlock in the context of database concurrency?

b. Discuss the various methods of handling concurrent transactions.

Data Integrity and Constraints:

a. Explain the concept of referential integrity in a relational database.

b. Define the CHECK constraint in SQL.

NoSQL Databases:

a. Compare and contrast SQL and NoSQL databases.

b. Provide examples of NoSQL databases and their use cases.

Database Security:

a. Discuss the importance of database security.

b. Describe techniques for securing a database, including access control and encryption.

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Data Structures Using C

 Arrays and Strings:

a. Write a C program to find the sum of elements in an array.

b. Explain how you can reverse a string in C.

Linked Lists:

a. Implement a function to insert a node at the beginning of a linked list.

b. Write a program to detect a loop in a linked list.

Stacks:

a. Implement a stack using an array.

b. Write a C program to check for balanced parentheses using a stack.

Queues:

a. Implement a queue using two stacks.

b. Write a C program to perform enqueue and dequeue operations on a queue.

Trees:

a. Implement a binary search tree and perform an inorder traversal.

b. Write a function to find the height of a binary tree.

Graphs:

a. Implement a depth-first search (DFS) algorithm for a graph.

b. Write a program to find the shortest path in a weighted graph using Dijkstra's algorithm.

Sorting and Searching:

a. Implement the quicksort algorithm in C.

b. Write a program to perform binary search on a sorted array.

Hashing:

a. Implement a hash table in C.

b. Write a program to handle collisions in a hash table using chaining.

Dynamic Programming:

a. Solve the Fibonacci sequence using dynamic programming.

b. Implement the knapsack problem using dynamic programming.

Miscellaneous:

a. Explain the difference between a stack and a queue.

b. Describe the advantages and disadvantages of arrays and linked lists.

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Linear Regression with Python

 

What you'll learn

Create a linear model, and implement gradient descent.

Train the linear model to fit given data using gradient descent.

Join Free:Linear Regression with Python

About this Guided Project

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.

Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Data Science as a Field

 

What you'll learn

By taking this course, you will be able explain what data science is and identify the key disciplines involved.

You will be able to use the steps of the data science process to create a reproducible data analysis and identify personal biases.

You will be able to identify interesting data science applications, locate jobs in Data Science, and begin developing a professional network.

Join Free:Data Science as a Field

There are 4 modules in this course

This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline.

Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

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Excel/VBA for Creative Problem Solving, Part 3 (Projects)

 

What you'll learn

Create a VBA user form that will implement or solve a real world scenario or problem

Join Free:Excel/VBA for Creative Problem Solving, Part 3 (Projects)

There are 5 modules in this course

In this course, learners will complete several VBA projects.  It is highly recommended that learners first take "Excel/VBA for Creative Problem Solving, Part 1" and "Excel/VBA for Creative Problem Solving, Part 2".  This course builds off of skills learned in those two courses.  This is a project-based course.  Therefore, the projects are quite open-ended and there are multiple ways to solve the problems.  Through the use of Peer Review, other learners will grade learners' projects based on a grading rubric.

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Introduction to Data Analysis Using Excel

 

Build your subject-matter expertise

This course is part of the Business Statistics and Analysis Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free:Introduction to Data Analysis Using Excel

There are 4 modules in this course

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners -  those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them.

To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. 

________________________________________

WEEK 1

Module 1: Introduction to Spreadsheets

In this module, you will be introduced to the use of Excel spreadsheets and various basic data functions of Excel.

Topics covered include:

• Reading data into Excel using various formats

• Basic functions in Excel, arithmetic as well as various logical functions

• Formatting rows and columns

• Using formulas in Excel and their copy and paste using absolute and relative referencing

________________________________________

WEEK 2

Module 2: Spreadsheet Functions to Organize Data

This module introduces various Excel functions to organize and query data. Learners are introduced to the IF, nested IF, VLOOKUP and the HLOOKUP functions of Excel. 

Topics covered include:

• IF and the nested IF functions

• VLOOKUP and HLOOKUP

• The RANDBETWEEN function

________________________________________

WEEK 3

Module 3: Introduction to Filtering, Pivot Tables, and Charts

This module introduces various data filtering capabilities of Excel. You’ll learn how to set filters in data to selectively access data. A very powerful data summarizing tool, the Pivot Table, is also explained and we begin to introduce the charting feature of Excel.

Topics covered include:

• VLOOKUP across worksheets

• Data filtering in Excel

• Use of Pivot tables with categorical as well as numerical data

• Introduction to the charting capability of Excel

________________________________________

WEEK 4

Module 4: Advanced Graphing and Charting

This module explores various advanced graphing and charting techniques available in Excel. Starting with various line, bar and pie charts we introduce pivot charts, scatter plots and histograms. You will get to understand these various charts and get to build them on your own.

Topics covered include

• Line, Bar and Pie charts

• Pivot charts

• Scatter plots

• Histograms

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Hadoop Platform and Application Framework

 

There are 5 modules in this course

This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment.   In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data.  You'll feel empowered to have conversations about big data and the data analysis process.

Join Free:Hadoop Platform and Application Framework

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Using Python to calculate standard deviation and variance

 

import numpy as np

data = np.array([1, 3, 5, 7, 9])

sd = np.std(data)

var = np.var(data)

print(f"Standard deviation: {sd:.2f}")

print(f"Variance: {var:.2f}")

#For free code visit clcoding.com

import statistics

data = [1, 3, 5, 7, 9]

sd = statistics.stdev(data)

psd = statistics.pstdev(data)

print(f"Standard deviation: {sd:.2f}")

print(f"Population standard deviation: {psd:.2f}")

#For free code visit clcoding.com

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Python Coding challenge - Day 93 | What is the output of the following Python Code?

 

Solution and Explanation :

The code print(round(1 / 3, 2)) will output the result of dividing 1 by 3 and rounding the result to 2 decimal places. Let's calculate:

1/3≈0.333333...31​≈0.333333...

Rounding this to 2 decimal places, the result will be:

round(13,2)≈0.33round(31​,2)≈0.33

So, the output of the code will be: 0.33

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Applied Machine Learning in Python

 

What you'll learn

Describe how machine learning is different than descriptive statistics

Create and evaluate data clusters

Explain different approaches for creating predictive models

Build features that meet analysis needs

Join Free:Applied Machine Learning in Python

There are 4 modules in this course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. 

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

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Neural Networks and Deep Learning

 

Build your subject-matter expertise

This course is part of the Deep Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free:Neural Networks and Deep Learning

There are 4 modules in this course

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. 

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

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Machine Learning Introduction for Everyone

 

What you'll learn

Compare and contrast artificial intelligence, machine learning, and deep learning 

Explain the machine learning models development lifecycle    

Differentiate between supervised and unsupervised machine learning   

Evaluate classification models using metrics such as accuracy, confusion matrices, precision, and recall

Join Free:Machine Learning Introduction for Everyone

There are 3 modules in this course

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts. You will develop concrete machine learning skills as well as create a final project demonstrating your proficiency. 

After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices. 

This Course Is Part of Multiple Programs 

You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.

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Machine Learning for All

 

What you'll learn

You will understand the basic of how modern machine learning technologies work

You will be able to explain and predict how data affects the results of machine learning

You will be able to use a non-programming based platform train a machine learning module using a dataset

You will be able to form an informed opinion on the benefits and dangers of machine learning to society

Join Free: Machine Learning for All

There are 4 modules in this course

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it. While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming, we believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should. The big AI breakthroughs sound like science fiction, but they come down to a simple idea: the use of data to train statistical algorithms. In this course you will learn to understand the basic idea of machine learning, even if you don't have any background in math or programming. Not only that, you will get hands on and use user friendly tools developed at Goldsmiths, University of London to actually do a machine learning project: training a computer to recognise images. This course is for a lot of different people. It could be a good first step into a technical career in Machine Learning, after all it is always better to start with the high level concepts before the technical details, but it is also great if your role is non-technical. You might be a manager or other non-technical role in a company that is considering using Machine Learning. You really need to understand this technology, and this course is a great place to get that understanding. Or you might just be following the news reports about AI and interested in finding out more about the hottest new technology of the moment. Whoever you are, we are looking forward to guiding you through you first machine learning project.

NB this course is designed to introduce you to Machine Learning without needing any programming. That means that we don't cover the programming based machine learning tools like python and TensorFlow.

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Supervised Machine Learning: Regression and Classification

 

What you'll learn

Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn

Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

Join Free: Supervised Machine Learning: Regression and Classification

There are 3 modules in this course

In the first course of the Machine Learning Specialization, you will:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

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What is the output of following Python code?

 

What is the output of following Python code?

import math

print(math.floor(-2.8))

print(math.trunc(-2.8))

print(math.ceil(-2.8))

Solution and Explanation: 

The output of the provided Python code is:

-3

-2

-2

Here's a breakdown of each line:

math.floor(-2.8): This line uses the floor function from the math module to round down -2.8 to the nearest integer. Since the largest integer less than or equal to -2.8 is -3, the output is -3.

math.trunc(-2.8): Similar to floor, trunc also rounds down towards zero. However, unlike floor, it truncates the decimal part of the number instead of rounding it. Therefore, math.trunc(-2.8) also outputs -2.

math.ceil(-2.8): This line uses the ceil function, which rounds numbers up to the nearest integer. The smallest integer greater than or equal to -2.8 is -2, so math.ceil(-2.8) outputs -2.

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Machine Learning for Trading Specialization

 

What you'll learn

Understand the structure and techniques used in machine learning, deep learning, and reinforcement learning (RL) strategies.

Describe the steps required to develop and test an ML-driven trading strategy.

Describe the methods used to optimize an ML-driven trading strategy.

Use Keras and Tensorflow to build machine learning models.

Join Free:Machine Learning for Trading Specialization

Specialization - 3 course series

This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies.  By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading.  This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.

Applied Learning Project

The three courses will show you how to create various quantitative and algorithmic trading strategies using Python. By the end of the specialization, you will be able to create and enhance quantitative trading strategies with machine learning that you can train, test, and implement in capital markets. You will also learn how to use deep learning and reinforcement learning strategies to create algorithms that can update and train themselves.

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IBM AI Engineering Professional Certificate

 

What you'll learn

Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction 

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn 

Deploy machine learning algorithms and pipelines on Apache Spark 

Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow 

Join Free:IBM AI Engineering Professional Certificate

Professional Certificate - 6 course series

Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.  

You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.

Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.

In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering. 

Applied Learning Project

Throughout the program, you will build a portfolio of projects demonstrating your mastery of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your ability to communicate project outcomes. 

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Machine Learning Engineering for Production (MLOps) Specialization

 

What you'll learn

Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.

Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.

Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.

Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

Join Free:Machine Learning Engineering for Production (MLOps) Specialization

Specialization - 4 course series

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. 

Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. 

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Applied Learning Project

By the end, you'll be ready to

• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements

• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application

• Build data pipelines by gathering, cleaning, and validating datasets

• Implement feature engineering, transformation, and selection with TensorFlow Extended

• Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas

• Apply techniques to manage modeling resources and best serve offline/online inference requests

• Use analytics to address model fairness, explainability issues, and mitigate bottlenecks

• Deliver deployment pipelines for model serving that require different infrastructures

• Apply best practices and progressive delivery techniques to maintain a continuously operating production system.

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AI For Everyone

 

There are 4 modules in this course

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. 

Join Free:AI For Everyone

In this course, you will learn:

- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science

- What AI realistically can--and cannot--do

- How to spot opportunities to apply AI to problems in your own organization

- What it feels like to build machine learning and data science projects

- How to work with an AI team and build an AI strategy in your company

- How to navigate ethical and societal discussions surrounding AI

Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

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Machine Learning Specialization

 

What you'll learn

Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)

Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods

Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection

Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

Join Free : Machine Learning Specialization

Specialization - 3 course series

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Applied Learning Project

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.

• Build and train a neural network with TensorFlow to perform multi-class classification.

• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.

• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.

• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.

• Build a deep reinforcement learning model.

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Python Coding challenge - Day 92 | What is the output of the following Python Code?

 

The above code is a Python list comprehension that adds corresponding elements of sublists in the nested list lis. Let's break it down:

lis = [[8, 7], [6, 5]]

result = [p + q for p, q in lis]

print(result)

lis is a nested list with two sublists: [8, 7] and [6, 5].

The list comprehension [p + q for p, q in lis] iterates over each sublist, and for each sublist, it takes elements p and q and calculates their sum p + q.

The result is a new list (result) containing the sums of corresponding elements from the sublists.

When you print the result, you'll get:

[15, 11]

This is because:

For the first sublist [8, 7], the sum of corresponding elements is 8 + 7 = 15.

For the second sublist [6, 5], the sum of corresponding elements is 6 + 5 = 11.

So, the final output is [15, 11].

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IBM Cybersecurity Analyst Professional Certificate

 

What you'll learn

Develop knowledge of cybersecurity analyst tools including data protection; endpoint protection; SIEM; and systems and network fundamentals.   

Learn about key compliance and threat intelligence topics important in today’s cybersecurity landscape. 

Gain skills for incident responses and forensics with real-world cybersecurity case studies.

Get hands-on experience to develop skills via industry specific and open source Security tools.

Professional Certificate - 8 course series

A growing number of exciting, well-paying jobs in today’s security industry do not require a college degree. This Professional Certificate will give you the technical skills to become job-ready for a Cybersecurity Analyst role. Instructional content and labs will introduce you to concepts including network security, endpoint protection, incident response, threat intelligence, penetration testing, and vulnerability assessment.

Join Free : IBM Cybersecurity Analyst Professional Certificate

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Earn a data science certificate

 

• Earn a data science certificate

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Meta Database Engineer Professional Certificate

 

What you'll learn

Demonstrate proficiency of SQL syntax and explain how it’s used to interact with a database.

Create databases from scratch and learn how to add, manage and optimize your database.

Write database driven applications in Python to connect clients to MySQL databases.

Develop a working knowledge of advanced data modeling concepts.

Join Free : Meta Database Engineer Professional Certificate

Professional Certificate - 9 course series

Want to get started in the world of database engineering? This program is taught by industry-recognized experts at Meta. You’ll learn the key skills required to create, manage and manipulate databases, as well as industry-standard programming languages and software such as SQL, Python, and Django used for supporting outstanding websites and apps like Facebook, Instagram and more.

In this program, you’ll learn:

Core techniques and methods to structure and manage databases. 

Advanced techniques to write database driven applications and advanced data modeling concepts. 

MySQL database management system (DBMS) and data creation, querying and manipulation.

How to code and use Python Syntax

How to prepare for technical interviews for database engineer roles.

Any third-party trademarks and other intellectual property (including logos and icons) referenced in the learning experience remain the property of their respective owners. Unless specifically identified as such, Coursera’s use of third-party intellectual property does not indicate any relationship, sponsorship, or endorsement between Coursera and the owners of these trademarks or other intellectual property.

Applied Learning Project

You’ll complete a series of 5 projects in which you will demonstrate your proficiency in different aspects of database engineering. 

You’ll demonstrate your skills with database normalization by structuring your own relational database by defining relationships between entities and developing relational schema. 

This is followed by a stored procedure project in which you’ll demonstrate your competency in SQL automation by writing a stored procedure to solve real world problems. After developing your skills in Python, you’ll create a Python application to administer a MySQL database and program its interactions with clients. 

In the next project, you are required to apply data modeling to a real-world project by enacting advanced data modeling concepts such as automation, storage and optimization. 

Finally, you’ll be tasked with creating a MySQL database solution for an app by drawing on the knowledge and skills that they have gained throughout the program.

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Capstone: Retrieving, Processing, and Visualizing Data with Python

 

What you'll learn

Make use of unicode characters and strings

Understand the basics of building a search engine

Select and process the data of your choice

Create email data visualizations

There are 7 modules in this course

In the capstone, students will build a series of applications to retrieve, process and visualize data using Python.   The projects will involve all the elements of the specialization.  In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find.  Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.

Join Free : Capstone: Retrieving, Processing, and Visualizing Data with Python

 

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Using Python to Access Web Data

 

What you'll learn

Use regular expressions to extract data from strings

Understand the protocols web browsers use to retrieve documents and web apps

Retrieve data from websites and APIs using Python

Work with XML (eXtensible Markup Language) data

There are 6 modules in this course

This course will show how one can treat the Internet as a source of data.  We will scrape, parse, and read web data as well as access data using web APIs.  We will work with HTML, XML, and JSON data formats in Python.  This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization.  These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files.  This course covers Python 3.

Join Free : Using Python to Access Web Data

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Python Data Structures by drchuck

 

What you'll learn

Explain the principles of data structures & how they are used

Create programs that are able to read and write data from files

Store data as key/value pairs using Python dictionaries

Accomplish multi-step tasks like sorting or looping using tuples

There are 7 modules in this course

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”.  This course covers Python 3.

Join Free : Python Data Structures

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clcoding = '786' *coding_list, = clcoding print(coding_list)

 

Code :

clcoding = '786'

*coding_list, = clcoding

print(coding_list)

Solution and Explanation: 

In the above code, the expression *coding_list, = clcoding is used to unpack the characters from the string clcoding and assign them to the list coding_list. Here's a breakdown:

clcoding = '786'

# Unpack the characters from the string 'clcoding' and assign them to the list 'coding_list'

*coding_list, = clcoding

# Print the resulting list

print(coding_list)

When you run this code, it will output:

['7', '8', '6']

This is essentially doing the same thing as the previous example using list(clcoding), but in a more concise way using the unpacking syntax *. The result is a list containing individual characters from the string '786'.

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Python Coding challenge - Day 91 | What is the output of the following Python Code?

 

The code you provided creates a list of individual characters from the string clcoding and then prints the list. Here's a breakdown:

clcoding = '786'

# Convert the string 'clcoding' into a list of individual characters

char_list = list(clcoding)

# Print the resulting list

print(char_list)

When you run this code, it will output:

['7', '8', '6']

This is because the list() function is used to convert the string '786' into a list of its individual characters.

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Python Coding challenge - Day 90 | What is the output of the following Python Code?

 

Code :

num = [10, 20, 30, 40, 50]

num[1:4] = [15, 25, 35]

print(num)

Solution and Explanation : 

The given code creates a list called num with elements [10, 20, 30, 40, 50]. Then, it uses slicing to replace elements at indices 1 to 3 (excluding 4) with the values [15, 25, 35]. Finally, it prints the modified list.

Let's break it down:

num = [10, 20, 30, 40, 50]

This creates a list with the elements 10, 20, 30, 40, and 50.

num[1:4] = [15, 25, 35]

This line uses slicing to replace the elements at indices 1 to 3 with the values 15, 25, and 35. So, the list becomes [10, 15, 25, 35, 50].

print(num)

This prints the modified list:

[10, 15, 25, 35, 50]

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HarvardX: Introduction to Data Science with Python

 

About this course

Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

Join Free : HarvardX: Introduction to Data Science with Python

What you'll learn

Gain hands-on experience and practice using Python to solve real data science challenges

Practice Python programming and coding for modeling, statistics, and storytelling

Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn

Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems

Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study

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HarvardX: CS50's Introduction to Game Development

 

About this course

In a quest to understand how video games themselves are implemented, you'll explore the design of such childhood games as:

Super Mario Bros.

Pong

Flappy Bird

Breakout

Match 3

Legend of Zelda

Angry Birds

Pokémon

3D Helicopter Game

Dreadhalls

Portal

Join Free : HarvardX: CS50's Introduction to Game Development

What you'll learn

The basics of machine learning

How to perform cross-validation to avoid overtraining

Several popular machine learning algorithms

How to build a recommendation system

What is regularization and why it is useful?

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HarvardX: CS50's Introduction to Artificial Intelligence with Python

 About this course

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Join Free : HarvardX: CS50's Introduction to Artificial Intelligence with Python

What you'll learn

graph search algorithms

adversarial search

knowledge representation

logical inference

probability theory

Bayesian networks

Markov models

constraint satisfaction

machine learning

reinforcement learning

neural networks

natural language processing

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CS50's Introduction to Computer Science

 

About this course

This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course.

Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form. 

Join Free : CS50's Introduction to Computer Science

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Python Coding challenge - Day 89 | What is the output of the following Python Code?

 

Code : 

print(1+True)

Solution and Explanation: 

In Python, True is treated as 1 when used in numeric operations. Therefore, 1 + True is equivalent to 1 + 1, and the result is 2.

So, the output of print(1 + True) will be:

2

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Match the following:

 

Answer : 

Default value of sep in print( ) - ' '

Default value of end in print( ) - \n

Easiest way to print output - Using fstring

Return type of split( ) - str

print('{num:>5}') - Right justify num in 5 columns

print('{num:<5}') - Left justify num in 5 columns

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