Machine learning algorithms for beginners
For Developers

Top 10 Machine Learning Algorithms for Beginners

Top machine learning algorithms: 1. Linear regression 2. Logistic regression 3. Decision tree 4. Support vector machine 5. Naive Bayes 6. K-nearest neighbor

What are machine learning algorithms? How do machine learning algorithms work? What are examples of machine learning algorithms? Which algorithm is best for machine learning? Keep reading this blog post to find out.

Machine Learning is a subset of Artificial Intelligence. ML imitates the human learning process and helps us automate tasks. In addition, ML assists in decision-making, pattern recognition, risk assessment, image classification, predictive analysis, data processing, and a lot more.

What are machine learning algorithms?

Machine learning algorithms are a set of instructions that guide a computing system to process historical data and produce the output within a given range. Prediction, classification, regression, forecasting, and data modeling are some of the major applications of machine learning algorithms. 

Types of machine learning algorithms

  1. Supervised learning
  2. Unsupervised learning
  3. Semi-supervised learning
  4. Reinforcement learning

List of popular machine learning algorithms:

  1. Linear regression (Supervised Learning/Regression)

    Linear regression is a simple and popular machine learning algorithm commonly used for predictive analysis. Logistic regression allows users to study the relationship between dependent and independent variables by defining a line and its equation. This line is the regression line, and its linear equation is y=mx+c.

    Here, y is the dependent variable, x is the independent variable, m is the slope of the line, and c is the intercept. The m and c are calculated by minimizing the sum of the squared distance between the two points and the regression line.

    This linear regression algorithm is commonly used to predict stock market movements.
  2. Logistic regression (Supervised learning – Classification)

    Logistic regression is a supervised machine learning algorithm that uses binary values (0 and 1) from a set of independent variables. Logistic regression predicts categorical and discrete values. This algorithm comes in handy to solve classification problems and predict the probability of an event.

    ML experts also use logistic regression for binary classification of data and fraud detection.

    Logistic regression uses a transformation function. The logistic function h(x) = 1 / (1 + ex) forms an S-shaped curve that lies between 1 and 0. Logistic regression is applied to predict the probability of a yes/no event, such as whether or not a patient has a heart attack, whether or not a debtor will default, and more.
  3. Decision tree (Supervised Learning – Classification/Regression)

    The decision tree is a machine learning algorithm that classifies both categorical and continuous dependent variables. The decision tree divides the data into two or more similar sets based on the attributes and variables.

    A decision tree starts with a root node and ends with a leaf node. The branches show the decision rules/conditions, the internal node represents the dataset features, and the leaf node represents the output.

    The decision tree algorithm has real-world applications like identifying cancerous and non-cancerous cells, suggesting products and services to potential buyers, and more.

    Related Post: What is Machine Learning Ops (MLOps)? How to get started?
  4. Support vector machine (Supervised Learning – Classification)

    Support vector machine is a classification algorithm that allows you to plot raw data as points in an n-dimensional space. N represents the number of features you have defined. The value of each feature is associated with specific coordinates.

    The SVM algorithm creates a hyperplane or a decision boundary that separates data sets into different classes. The support vectors are the data points that define the hyperplane. Classifiers split the data and plot those data points on a graph.

    Support vector machines have real-life applications, such as image classification, face detection and identification, drug discovery, and more.
  5. Naive Bayes (Supervised Learning – Classification)

    The Naive Bayes algorithm is based on the Bayes theorem – calculate the probability that an event may occur. The algorithm is naive because the variables are independent of each other. The Naive Bayes is a supervised machine learning algorithm that is based on conditional probability.

    Here is the equation…

    P(A|B) = P(B|A) * P(A)P(B)

    Here P(A|B) = posterior probability. Posterior probability calculates the probability of event A for data B is calculated.

    P(B|A) is the likelihood, i.e., the probability of data B if event A occurs.  P(A) is class prior probability, and P(B) is predictor prior probability.

    The Naive Bayes algorithm is ideal for large datasets, such as text classification.

    Related Post: Artificial Intelligence vs Machine Learning vs Deep Learning: What’s the Difference?
  6. K-nearest neighbors (Supervised Learning)

    The K-nearest neighbors are a supervised learning algorithm for the classification and regression of data. This algorithm estimates the likelihood of a data point being a member of one or another group. To determine the group of data points, the overall points are analyzed with reference to a single data point.

    The K-nearest neighbors algorithm assumes similarities between new and available data points to classify them. The Euclidean distance between data points on a graph classifies them into separate categories. KNN is applied for text mining, agriculture, finance, medical, facial recognition, and more.

    KNN is also a lazy-learner algorithm, as it uses the entire dataset as its training set.
  7. K-means clustering (Unsupervised Learning – Clustering)

    The K-means is an unsupervised machine learning algorithm that can solve clustering problems. Datasets are classified into K-number clusters based on their similarities and differences between other data points. The process is repeated continuously until data points are assigned to each cluster.

    Centroids are the center point of each cluster, and the distance from each data point to the centroids is calculated. The data point is assigned to a cluster that is nearest to the centroid. The algorithm then creates new centroids and repeats the process until centroids are not interchanged.

    The k-means clustering algorithm is applied for market segmentation, document clustering, image segmentation, image compression, and more.
  8. Random forest (Supervised Learning – Classification/Regression

    The random forest algorithm follows ensemble learning techniques to combine multiple algorithms and achieve better results. The random forest is a collection of decision trees that classifies new objects according to their attributes. The trees vote for a class, and the forest selects the classification that has the majority votes.

    The decision trees represent individual subsets of data and calculate the average to improve the accuracy of the prediction/classification model. The random forest ideally contains 64-128 trees. The input is entered at the top of the decision tree. Then, the input travels down to the subsets based on the attributes/variables.

    Random forest algorithm is applied for predicting customer behavior, consumer demand, market fluctuations, fraud identification, diagnosis, and more.
  9. Apriori algorithm (Unsupervised Learning)

    The apriori algorithm is an unsupervised learning algorithm that can solve association problems. The association problems are aimed at finding interesting associations and relationships among large sets of data items.

    The apriori algorithm uses frequent item sets to generate association rules that determine how strongly two objects are connected to each other. This algorithm works on databases, which contain transactions or similar forms of comparable information.

    R. Agrawal and Srikant wrote the apriori algorithm in 1994. The algorithm uses a breadth-first search process and a hash tree to calculate item sets. To find frequent item sets from the larger dataset, the process is repeated continuously.

    Some of the common applications Apriori algorithm include market basket analysis to find products that can be bundled together, drug reactions in patients, and more.
  10. Principal component analysis (Unsupervised Learning)

    Principal component analysis is an unsupervised learning technique for dimensionality reduction. This algorithm reduces dataset dimensionality, i.e., it reduces the number of correlated features. The PCA follows a statistical process to convert observations of correlated features into linearly uncorrelated features.

    PCA considers the variance of each attribute to check for low variance. A high variance displays better splits between the classes and reduces dimensionality.

    The principal component analysis comes in handy for exploratory data analysis and predictive modeling. The applications of PCA include a movie recommendation system, image processing, power allocation optimization for electrical grids, and more. 

Bottom line

A sound knowledge of machine learning algorithms can help you excel as an ML engineer. Knowing when to use a specific algorithm is very important for a machine learning engineer. Using the machine-learning algorithms mentioned above, ML engineers can start implementing ML systems for classification, regression, data analysis, modeling, and more.

Do you wish to work at a top US company as an ML engineer? Are you a machine learning enthusiast looking for high-paying remote jobs? Try Turing.

Turing offers high-paying remote machine learning jobs for developers across the globe. For more information, visit the Apply for Jobs page.


  1. How do machine learning algorithms work?

    Machine learning algorithms rely on computational techniques to gather information from the data instead of using a predetermined equation as a reference model. The ML algorithms adapt and improve their performance as the sample data increases. The algorithms consider the input variables during training and find the best solution to a given problem.
  2. What are examples of machine learning algorithms?

    Some of the common machine learning algorithms are linear regression, logistic regression, Naive Bayes, K-nearest neighbor, principal component analysis, random forest, support vector machine, and more.
  3. Which algorithm is best for machine learning?

    Selecting the best algorithm for machine learning depends on your exact requirements, your sample/learning dataset, your expected format of output, classification/regression calculations, and a few other factors. 

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By November 30, 2022
Top programming languages in 2022
For Developers

Programming Languages Overview: These Languages Dominated 2022

Is Java losing its popularity? Which is the most used programming language? Which are the fastest-growing languages? All of these questions have been answered in the Octoverse by GitHub. Octoverse is a yearly report by GitHub that reveals the state of programming languages and their trends. Also, read GitHub vs GitLab Let’s take a look… View Article

Is Java losing its popularity? Which is the most used programming language? Which are the fastest-growing languages? All of these questions have been answered in the Octoverse by GitHub. Octoverse is a yearly report by GitHub that reveals the state of programming languages and their trends.

Also, read GitHub vs GitLab

Let’s take a look at the key takeaways from the Octoverse.

Trends in programming languages 2014-2022 (1)

Trends in programming languages 2014-2022

  • JavaScript remains the most used programming language. 
  • Python remained in the second position, while Java remained steady in the third. It is interesting to note that Java has been around for more than 30 years but still is one of the most used programming languages.
  • Typescript jumped from the 10th position to the 4th in 2020. In 2022, it remained steady as the 4th most used programming language.
  • PHP dropped down from its rank in 2022. The language stood in the 3rd position in 2014 but dropped to 6th last year. In 2022, PHP dropped to the 7th position, losing the 6th position to C++.
  • Shell, C, and Ruby remain in the 8th, 9th, and 10th positions, respectively.

Java shows no signs of wear! Why is Java still popular in 2023?

Java has been around for more than 30 years. But what makes it the most used language with no signs of slowing down in terms of popularity? Let’s take a look. 

  • Java is an open-source programming language with extensive documentation. 
  • Java is one of the most user-friendly languages. It offers a simple learning curve for new developers. Java is easier to learn if you’re already familiar with C and C++.
  • Java is a WORA programming language. WORA stands for ‘write once, run anywhere. Java can run on various operating systems, such as Windows, Linux, Unix, and macOS. 
  • Java has an extensive and rich library of APIs. These APIs can cater to various applications such as networking, parsing XML, etc. 
  • In its 30 years of existence, Java has acquired one of the largest and most robust communities. 
  • Java offers a suite of extensive automation tools, editors, and debuggers in the Integrated Development Environment (IDE). Some of the most popular IDEs are Netbeans, Eclipse, IntelliJ, etc. 

Also, read JavaScript Trends in 2022: React, Elm, TypeScript.

Which were the fastest-growing programming languages in 2022?

  • Hashicorp Configuration Language (HCL) showed a staggering 56.1 percent growth in 2022. 
  • Following closely, Rust showed a growth of 50 percent in 2022 as compared to 2021. 
  • Typescript and Lua grew by over 37 and 34 percent, respectively.
  • Other programming languages which showed more than 20 percent growth this year are Go, Shell, Makefile, C, Kotlin, and Python. 

Other key takeaways from the Octoverse:

  • Programming languages are most used for application development. In 2022, Lua was heavily used for game development.
  • For cloud-based applications and scripting, developers preferred faster languages such as Go. 
  • For mobile application development, developers used languages like Kotlin, Dart, Flutter, etc., in 2022. 

So, these were some of the top programming languages statistics for 2022. Stay tuned to learn more about the programming languages and their usage, popularity, etc., in 2023. 

Are you a software developer well-versed in the top programming languages such as Java, JS, Python, C++, etc.? If yes, top US companies are looking for you. Sign up on Turing and get hired by top US companies. 

More on Octoverse by Github

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By November 30, 2022
5 Key Trends in Cloud Computing in 2023
For Employers

5 Key Trends in Cloud Computing in 2023

Here are the top 5 trends in cloud computing in 2023: 1. AI and ML 2. Docker and Kubernetes adoption 3. Data security on cloud computing 4. Hybrid cloud solutions…

Cloud computing has become one of the fastest-emerging technologies in the IT industry. Though the technology has been around for over a decade, its usage has increased exponentially in recent years. Considering this upward trend, cloud computing is bound to become the most talked about technology in 2023. 

Before we take a look at the major trends, here are some interesting stats about the cloud computing market:

  • The cloud computing market is expected to reach over $1 trillion by 2028. 
  • Within a span of 10 years, i.e., between 2010 to 2020, the market grew by a massive 635 percent.
  • As per stats, an organization can save up to 40 percent by migrating to cloud computing. 
  • A survey by Accenture revealed that companies could save up to 64 percent in their energy consumption costs and reduce their carbon emissions by up to 64 percent by migrating to cloud computing!

What are the trends in cloud computing in 2023? Let’s find out.

  1. AI and ML integration into cloud computing

    With the increase in demand for cloud computing, the integration of AI and ML in cloud computing will rise in 2023. A recent survey stated that the cloud AI market will be valued at $13.1 billion by 2026, registering a CAGR of 20.3 percent for 2021-26.  This is a massive jump from the value of $5.2 billion in 2020. Companies can use AI and ML with cloud computing for various applications such as digital asset management, virtual assistants, reality-as-a-service, cloud-based security for applications, and much more. AI and ML will…

  2. Kubernetes and Docker to manage cloud deployment

    Docker and Kubernetes offer a platform to deliver software in packages called containers. Using Docker and Kubernetes in managing cloud deployment will help in large-scale deployments. Both platforms offer the following:

    • High scalability
    • High efficiency
    • Cloud deployment automation
    • Flexible sharing of resources
    • Quick and low-cost deployment

      The later versions of Docker have Kubernetes built-in to provide the ability to automate and manage all the containerized applications that the developers build using Docker.

      Related post: Here’s How You Can Use Docker to Test and Deploy Web Applications
  3. Data security on cloud computing

    Data security is a major concern hampering the adoption of cloud computing on a large scale. Several reasons, such as software bugs, human errors, etc., can lead to a security breach. Some of the major data security challenges while using cloud computing are:
    • Exposed APIs can result in non-authorized personnel gaining access to the cloud data.
    • Data breaches can occur as a result of misconfigurations or insecure cloud infrastructure. 
    • Loss of data is yet another threat associated with cloud computing. This loss can happen for various reasons like insufficient data backups, software corruption, natural disasters, unintentional data deletion, etc.

      Developers can use encryption, access control, and activity monitoring on the cloud to prevent such data breaches in 2023.
  4. Hybrid cloud solutions

    A private cloud ensures high processing power. And so this solution is quite popular among users.

    We can expect businesses to choose a hybrid cloud in 2023 for the following reasons:
    • They get the best of both worlds (private and public cloud)
    • More flexibility
    • Highly cost-effective
    • Able to perform critical data processing
    • Highly scalable

      The hybrid cloud market is projected to reach a value of over $260 billion by 2027. Over 70 percent of organizations around the globe have a strategy or a set infrastructure to successfully adopt hybrid cloud technology.
  5. Cloud cost optimization

    Several companies are trying to better optimize their expenditure on IT services. Therefore, cloud service providers will create cost-effective cloud solutions and ensure high levels of performance and availability. The cloud service providers will have to use the resources at their disposal efficiently and offer optimization of cloud workloads to ensure the cost-effectiveness of cloud services.

So, these are the five major trends in cloud computing that you’ll witness in 2023. In addition to these, we may also see other trends like serverless computing, cloud monitoring, and disaster recovery in the cloud computing space. 

Are you looking to hire software developers well-versed in cloud computing? Turing can help. Hire the top 1 percent of 2M+ software developers from across the globe, pre-vetted by Turing’s AI-driven matching system. Head to the Hire Developers page now!


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By November 30, 2022
Effective manager habits
For Employers

Develop These 25 Habits to Become An Effective Manager

How to be an effective manager? 1. Don’t avoid debates. 2. Think big and trust your team to perform big. 3. Don’t hesitate to open up 4. Build connections

How to be a great manager? What successful habits do high-impact managers follow? High performance, identifying and clearing blockers, streamlining processes, and setting up efficient feedback systems are just a few of a manager’s responsibilities. But effective management goes beyond these tasks. Like an art form, effective management requires managers to focus on intricate details and optimize them to improve their team’s performance.

If you are a newly appointed manager, a veteran, or planning to accept a managerial role in the future, here is a list of daily habits that can help you become a highly effective manager.

  1. Don’t avoid debates

    Good managers allow their employees to own their decisions and explore ideas, even if they disagree with them initially. Heated debates may challenge existing assumptions but can often lead to better alternative outcomes. In fact, discussions that encourage team members to express their opinions enhance transparency and help them understand their roles better.

    So ask questions that allow employees to brainstorm and tackle problems with an open mindset. All successful managers offer their teammates a safe space to learn, grow, and fail.

    Related Post: 15 Questions Technical Hiring Managers Ask During Interviews
  2. Think big and trust your team to perform big

    Big ideas can transform productivity, performance, and process management in an organization. Great managers are often gifted with the ability to develop big ideas. And so, every manager must focus on the bigger picture, and how a specific change or improvement can affect other areas of the business.

    But that’s not all. The true strength of a manager lies in the way they communicate these ideas to their teams, and delegate tasks to implement them. To become a highly effective manager, you must show confidence in your team. In addition, you must guide them to shape your ideas into visible outcomes.

    Related Post: 6 Tips to Scale a Design Team: A Guide for Product Managers

  3. Managers must first be a part of their own team

    The waterfall model of hierarchy takes a linear and sequential approach to the development process. This structure is often overrated. Good managers must see themselves as coordinators of talent, priorities, and resources.

    They must move beyond the conventional approach, and consider the team’s strengths when delegating tasks. All effective managers think of their role in the company as enablers of growth, and avoid acting from a place of authority. They tap into the broader concept of teamwork, where each member is equally responsible for success. This way, each team member feels equally empowered and valued.

    Related Post: Hiring Managers Must Avoid Making These Mistakes
  4. Prioritize your team over other managers

    On several occasions, managers from other teams will approach you to drive an initiative. Instead of responding immediately, managers should first discuss the initiative with their direct reporters                                                       . Even better if you can ask your team for suggestions.

    This practice displays that as a manager, you trust and prioritize the opinions of your team members during discussions. Collaborating with other departments and their managers might be necessary, but doing so should not disturb the harmony of your own team.

    Remember, great managers, allow their team members the freedom to operate as owners and shape their vision accordingly.

    Related Post: Here’s How You Can Judge the Diversity of Your Team
  5. Share an onboarding guide with recruits

    Sharing an onboarding guide is a great way of showing new recruits that you care about their role in the organization as a manager. Ensure that this guide shares the team’s preferred working styles, day-to-day processes, and other vital information. The guide will help team members understand their daily tasks at an early stage and improve productivity.

    If you’re looking to scale your team, you can go a step further and share this onboarding user manual with the top candidates during the interview. Doing so will build psychological safety, trust, and transparency among candidates.
  6. Discuss failures, shortcomings, and declines

    Managers develop a habit of getting the team together to stand on a steady footing, even amidst stumbles. It is okay to show some vulnerability through personal challenges, work struggles, and professional expectations during these meetings. Similarly, it is okay to express burnout, exhaustion, or any other feelings you experience.

    Managers should also share their achievements and wins at these weekly meetings. This practice will build team spirit, encourage team members to talk about their experiences, and reflect upon the areas that need more attention.

    Related Post: What Is Chaos Engineering and What Are Its Benefits?
  7. Don’t hesitate to open up

    Managers should not hesitate to open up about the fun stuff, especially when during periods of stress. Sharing personal updates and anecdotes, big or small, helps team members feel associated with you on a personal level.

    This holds especially true for remote teams. Remote setups can make employees feel isolated and disconnected from each other. So if you’re a remote manager, ensure to open up to your team every once in a while. Build that warmth and rapport by encouraging your team to do the same.
  8. Monthly reflections are always helpful

    As responsibilities increase, targets shift, and monthly reflections allow managers to track progress, identify shortcomings, and devise strategies to overcome them.

    Set up monthly 1:1 meetings with each team member where the report reflects on three things that were successful last month, and three things that could be done differently next month. These meetings will drastically improve the team’s performance. To keep things light, you can end these conversations on an optimistic note. For example, ask them what they’re looking forward to in the next month.

    Related Post: 4 Qualities of High-Performing Teams You Must Know!
  9. Allocate time for out-of-the-box thinking

    Regular weekly meetings are crammed with basic status updates. This leaves no room for thinking big. So, effective managers must take out some time off their schedules to get creative. They must set up special meetings to promote innovation, evaluate and implement never-tried-before ideas, and improve the overall process.

    In other words, great managers must leverage every opportunity to brainstorm and build with their team. This practice helps bring non-urgent matters to light and start off new projects. Last but not least, this out-of-the-box thinking can improve your problem-solving and decision-making skills.
  10. Focus on building connections between people and teams

    As an organization starts to scale, it gets difficult for managers to keep their teams connected. A high-impact manager spots opportunities to build connections within their teams. When employees follow their leader to think beyond individual growth, they can excel at their tasks.

    Great managers emphasize offering context, and valuable insights through feedback. They brainstorm, collaborate, and conceptualize, to clear the doubts of their employees. Thus, great managers highlight how the individual contribution of their employees moves the needle for the team and the company as a whole.

    Related Post: 6 Strategies for Hiring Gen Z in Software Development
  11. Avoid multitasking

    Contrary to popular belief, multi-tasking kills productivity rather than improving it. Multitasking reduces the quality of work being done and increases the amount of time required to complete it.

    While delegating tasks, ensure that your employees are not juggling multiple things at the same time. For example, in a fast-scaling startup, regular 1:1 meetings may seem like rote rituals. However, the best managers approach such meetings with great care. They put emphasis on the most important task: listening. Most importantly, they ensure that they don’t sidetrack these 1:1 meetings by performing other tasks simultaneously.

    Successful managers stay present and attentive to respond to any doubts or concerns their employees have. Even if a question pops up at an inconvenient time, they take a couple of minutes to address it. These actions go a long way to show the employee that they are important to the team and their issues are a priority to the manager.

    Related Post: 7 Ways Managers Can Boost Remote Workers’ Productivity
  12. Feedbacks are vital

    Following through on deliverables is vital for succeeding in your role. However, as you move towards a managerial role and your schedule is filled with meetings, these good habits may start to slip.

    Following up and following through consistently, is a seemingly small habit that has a huge impact on the team. Managers should ask their employees how a certain task is progressing, and keep in touch to ensure that it’s completed smoothly.

    Feedbacks are critical, irrespective of the role you perform at the organization. And so, good managers offer regular feedback to the employees, on their quality of work, areas of improvement, and more.
  13. Treat your team members humanely

    Managers are tasked with improving and maintaining performance, with targets that must be met. However, through all of this, managers must remember that their employees are human beings, beyond numbers and performance metrics. Effective managers ensure that their team members are not overworked.

    Great managers look at the situations of their team members with compassion. They prioritize employees over deadlines and create an environment of growth and improvement, beyond the workplace pressure. This practice helps employees feel acknowledged as human beings and has a positive impact on their work.

    Related Post: 5 Signs of Toxic Work Culture and How to Deal With Them
  14. Follow an employee-first approach

    Some employees are expressive about their problems, while others tend to be quiet. And so, managers should ask employees if they are struggling on any personal or professional front. As a solution, managers can offer some time off, financial aid, or any other form of help.

    This way managers can encourage employees to take some time off, by actively enquiring about their employee’s well-being. These actions help the employees feel cared for and supported. The employee-first approach of great managers also makes the employee feel valued and secure

    Related Post: Want to Hire the Best Talent? Built the Best Culture First
  15. Soften the hits

    Making mistakes is natural. If your team messes up, this only means that you are being ambitious enough to try new approaches.

    When your employees goof up, explain what went wrong, and work together to devise a plan to keep it from happening again. Ensure you do this as calmly as possible. Even something as small as praising a quality of an employee before offering criticism can soften the hits. Telling your employees that you trust their decisions, and are willing to help them through potential failures is very impactful.

    Related Post: Top 6 Ways to Boost Your Hiring Process in 2023
  16. Monthly performance reviews are essential

    An effective performance review system can be very impactful when executed correctly. The ideal approach should be to follow up on the annual reviews with monthly touchpoints. After that, each feedback item should be turned into a tactical action-plan element that can be tracked and executed. This practice will help you track the progress of individual employees, and bring out the best in them.

    And so, to be a high-impact manager, you must check on your team from time to time and see how they are progressing. Ensure to share advice and guidance along the way to improve performance and boost the morale of the team.
  17. Reinforce positive habits

    When an employee seeks feedback, try to be transparent and thorough as possible. Listen to their expectations before sharing feedback. Understand what they want from you. This practice will streamline communication and prevent misunderstandings.

    To become a great manager, you must reinforce good habits like the clarity of thought, punctuality, and presence of mind. Observing and appreciating the positive habits of your team member encourages other employees to follow the same.

    Related Post: 3 Things Every Engineering Manager Must Focus On.
  18. Take a moment before giving feedback

    Managers may feel the urge to impart the feedback right away, but there is a risk that the employee may not be in the right mental space to hear it. So, to set the right tone, ask your employees how they prefer to receive feedback before getting started.

    High-impact managers first stop to ask if their teammates are open to hearing feedback and then proceed. Doing so helps employees process the feedback effectively, and be more receptive to it.

    Related Post: 6 Challenges to Remote Hiring and Ways to Overcome Them
  19. Try to praise at the moment

    A leader who recognizes and celebrates smaller milestones on the way to a larger goal helps to maintain their team’s momentum. Sharing milestones, accomplishments, small wins, questions, encouragement, and appreciation of team members have a positive impact on the team’s morale. An employee is motivated to excel at their job when they feel valued, appreciated, and acknowledged.

    For example, sending heartfelt messages of compliments, or using props to get your team fired up works wonders. Great managers who engage in such activities, enable their teams to have fun and take the work seriously, without taking themselves too seriously.

    Related Post: Why Should You Include Games in Candidate Assessment?
  20. Try to celebrate up the chain

    If a team member performs exceptionally well at a job, ensure to compliment them and share this with your senior. You can send them an email giving kudos and cc it to your senior. Small gestures like these go a long way. The moments when a manager goes out of their way to give an employee credit for their achievements are always memorable for them.

    A small email or a mention of the amazing things that the team members have accomplished can significantly boost morale and help the team perform better.

    Related Post: 7 Ways to Build and Scale a Remote Engineering Team
  21. Try to celebrate outside the office

    It’s nice to send thoughtful gifts to your team members out of the blue, be it on holidays or important life events. These could even be unrelated to work. Such gestures are wonderful reminders that you as a manager, care enough to remember the important occasions in your teams’ lives.

    Moreover, these gestures will bring you closer to your employees and build meaningful connections. With that in mind, it is important to ensure that these gestures are professional, and do not violate any social/emotional boundaries.

    Related Post: 11 Ways to Celebrate Pride at Your Workplace
  22. Invest in the careers of your team members

    Great managers delegate new projects to the team members that fit their strengths and are in line with their personal goals.

    This step displays that the managers are attentive to the way their employees are looking to grow and offer opportunities accordingly. The best managers aren’t just cheerleaders, they help their employees shape their careers.

    Related Post: Here’s How Google’s Project Starline Is Connecting People Working Remotely
  23. It is okay to rely on mentors and skip-level reports

    Mentoring your employees to achieve their career goals is a huge task. And so, asking professionals to do that on your behalf helps to share that responsibility. Simply put, high-impact managers connect employees with mentors to enhance their professional life and work toward their ambitions.

    If you are a top-level manager, you should demonstrate trust in your skip-level reports. A skip-level report is a person that reports to your subordinate or vice versa. A great skip-level manager can depend on their skip-level reports for productive feedback, insights, and grassroots-level information.

    Skip-level reports can display exceptional caliber and capability if given the opportunity. As a skip-level manager, you should be in close contact with your skip-level report.
  24. Nurture a flexible environment for growth

    Great managers care about the well-being of their employees. They go beyond asking, “How are things going?”, and ask deeper, meaningful questions to understand their team’s goals and aspirations. They try to normalize and make space for change, so that nobody feels stuck, or boxed in.

    Managers can achieve better growth when the personal and professional goals of their team members are well aligned. This is why managers must reserve time to discuss career opportunities with their team.

    Related Post: 4 Ways to Create the Best Workplace Culture for Women in Tech

  25. Help your team members improve their craft skills

    High-impact managers frequently sharpen their arrows. To become one, be sure to regularly check in on the craft skills of your employees that help their profiles stand out from the crowd.

    Letting your subordinates take time to attend a conference, setting up team-wide coaching sessions, or scheduling personal development workshops is a great way to approach this. Great managers always prioritize up-skilling over outsourcing.

    Wish to read more about how you can improve yourself as a manager? Read this article about the micro habits of high-impact managers.

Bottom line

So, how can you be an effective manager? There’s no one-size-fits-all formula that catapults you from being an ordinary manager to an extraordinary one. But making small yet consistent tweaks to your managing style based on the suggestions shared above can help you build a stronger, faster, high-performing team. Remember, a manager is only as good as their team. And so, building a solid team is the first step to becoming a great manager. 

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  1. How to become a successful manager? What habits make a successful manager?

    To become a successful manager, you must be a good listener, set clear expectations for your team, and delegate tasks in a way that allows your team to grow. In addition, you must offer constructive criticism where necessary and have a clear vision for your team.

  2. What are the 3 skills of a manager?

    Technical skills, conceptual/theoretical skills, and interpersonal skills are the three main skills a manager must have. Technical skills improve productivity, theoretical skills improve understanding, and interpersonal skills improve communication.

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By November 29, 2022
Black box testing vs white box testing
For Developers

Black Box Testing vs White Box Testing: Understanding Key Differences

Black Box Testing vs White Box Testing: Black box testing is the process of checking how software behaves, whereas white box testing checks how different ……

What is the difference between black box testing vs white box testing? What are common black box testing techniques? What are common white box testing techniques? What are the steps to perform black box testing? What are the different types of white box testing?

Let’s answer all these questions one by one.

What is black box testing?

Black box testing is a testing method wherein the testing team does not know about the internal structure and design of an application. This testing happens by comparing the input value and output value. 

Black box testing focuses on understanding user experience and happens post-completion of software applications or products. 

What are the steps to perform black box testing?

Steps to perform black box testing include…

  1. Examine system requirements and specifications
  2. Create a positive test scenario to check if the system processes requests correctly
  3. Create a negative test scenario to check the system’s capability to identify issues
  4. Determine expected outputs for all inputs
  5. Construct and execute test cases
  6. Compare outputs with inputs
  7. Validate the right results and fix persisting issues

What is white box testing?

White box testing is a testing method wherein the testing team examines the internal structure and design of an application. With white box testing, the team checks the data flow from input to output

White box testing focuses on finding hidden errors and optimizing code for a more efficient software application or product. 

What are the steps to perform white box testing?

Steps to perform white box testing include….

  1. Identification of component, feature, or program for testing
  2. Mapping all possible paths in a flow chart
  3. Integrating user journey, use cases, and technical information in a flow chart
  4. Writing test cases to cover each path in the flow chart
  5. Testing and checking whether you get the desired outcomes

    Related Post: 10 Popular Software Testing Trends in 2023

Black box testing vs white box testing: Key differences 

Black box testing vs white box testing

Key differences between black box testing and white box testing

What are some common black box testing techniques?

Common black box testing techniques include …

  1. Equivalence partitioning

    Equivalence partitioning divides data into partitions of valid and invalid values wherein the partitions exhibit the same behavior. If a condition of one partition is false, then the condition of the other partition should also be false, and vice-versa.

    For example, if you create a software function that accepts 6 digits to verify an OTP, then each partition of that function with six digits should be able to check the value. Also, if you enter more or less than six digits, the function should direct the user to the error page.
  2. Error guessing

    Yet another popular black box testing technique is error guessing. This technique involves identifying problematic areas of software with common testing questions. There is no specific method of error guessing. The tester must rely on their experience and test cases for other applications.

    Some of the common error-guessing questions include…

    Null pointer exceptions
    Entering invalid parameters
    Dividing by zero
    Uploading maximum files to test file limit
    Checking buttons without entering values
    Blank space in text fields
  3. Boundary value analysis

    Boundary value analysis is among the necessary black box testing techniques that check boundary values that are prone to error. The tester checks whether the software produces the correct output upon entering the boundary value.
  4. State transition testing

    State transition testing uses inputs to check for the desired output. The objective of state transition testing is to see if there is a change in the system during the transition from one state to another.

    The tester must analyze transition data and design test cases to probe what happens when the system transitions from one state to another and does not give desired outputs.
  5. Decision table testing

    Decision table testing tests the software system for outcomes produced by different input combinations. By using decision table testing, you can check all the possible conditions necessary for generating the desired output.

    Black box testing techniques

    Decision table testing is a popular black box testing technique.

What are some common white box testing techniques?

Common white box testing techniques include …

  1. Statement coverage testing

    Statement coverage is code inclusion testing that calculates the number of explanations implemented in the source code of an application. Also, it ensures that all statements in the code run at least once.

    By using statement coverage as one of your white box testing techniques, you can eliminate dead code and uncover unused statements.

    The formula of statement coverage is as follows:

    Number of statements implemented / Total number of statements
  2. Branch coverage testing

    The next white box testing technique is the branch coverage testing method. In this technique, the tester identifies all conditional and unconditional branches. Also, the tester ensures that each branch has conditional logic and undergoes unit tests.

    For example, A, B, and C are conditional branches if the system implements them upon meeting a condition, whereas D can be an unconditional branch that the system executes every time after executing A.

    The formula of branch coverage is as follows:

    Number of branches implemented / Total number of branches
  3. Path coverage testing

    Path coverage testing is a structured process that allows the tester to check each line of code. Path testing helps focus on program logic as you can check vital parts of code through cyclomatic complexity and unit tests.

    For example, in this code below,

    If a > b
    // do something
    // do nothing

    In the above code, the tester will first create a flow diagram by breaking the code into separate statements. After that, the tester will create different paths for outcomes. Finally, the tester will calculate cyclomatic complexity and run a unit test to validate codes.

    In the example, the cyclomatic complexity is 2, as a minimum of 2 test cases are necessary to cover all code execution paths.

    Related Post: Unit Testing vs Integration Testing: 4 Key Differences Explained


Black box testing focuses on software behavior, whereas white box testing focuses on the internal functioning of the software. Testers require both testing methods at different stages of product development. 

So, organizations must carefully understand the differences between black box testing and white box testing to choose the best testing method for their applications and software.

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1. What are the different types of white box testing?

Different types of white box testing include…

White box penetration testing – In this method, an ethical hacker attacks an application based on knowledge of its code and design.
Unit testing – Unit testing tests all written code to see if it works as expected.
Mutation testing – Mutation testing checks code resilience by making small changes to the code structure.
Integration testing – This method checks integration points in the internal software and external systems.
Static code analysis – This method uses machine learning and predefined patterns to identify issues in static codes.

2. What are the different types of black box testing?

Different types of black box testing include….

Functional testing – Functional testing validates software against functional requirements and specifications.
Non-functional testing – Non-functional testing checks how the system operates rather than the specific functions of the system.
Regression testing – Regression testing checks whether a system behaves differently after updates or code improvements. 


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By November 29, 2022
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