#interviewbit

[simplifyyourday]

STREAMING support in LINQ C# 😮

Folks!

Welcome to another episode to discover underrated/less known feature in C# LINQ. 😊

Check out the Video for more details

[nmietbbsr]

How to Prepare for FAANG & Product-Based Companies in India

If you’re aiming for a career with top-tier companies like FAANG (Facebook, Amazon, Apple, Netflix, Google) or other product-based firms in India, you’re on an exciting path. These companies offer great opportunities, and with the right preparation, you can land a role that sets you up for success. I’ve been looking into this, and I’d love to guide you with some practical steps you can trust.

Why Target FAANG and Product-Based Companies?

FAANG and similar firms are known for innovation and strong pay. In India, they hire thousands of engineers yearly, especially in tech hubs like Bangalore and Hyderabad. A 2023 report from Naukri.com shows that FAANG jobs in India offer starting salaries between ₹15-25 lakhs per year, with growth to ₹50 lakhs or more as you gain experience. Product-based companies like Zomato or Swiggy also provide creative roles, with salaries starting around ₹8-12 lakhs, per Glassdoor data from 2024.

These jobs let you work on cutting-edge projects—think AI tools or e-commerce platforms. For computer science students, it’s a chance to grow skills and build a career that stands out. The demand is high, with over 10,000 tech roles posted monthly on LinkedIn in 2023, making it a solid goal to aim for.

Key Skills to Focus On

To get noticed, you’ll need a mix of technical and soft skills. Start with coding—practice languages like Python, Java, or C++ on platforms like LeetCode or HackerRank. Data structures and algorithms are a must; FAANG interviews often test these with problems like sorting or graph traversal. A 2022 study from the Indian Institute of Technology Bombay found that 70% of successful candidates mastered these basics.

Problem-solving and communication matter too. Companies value team players who can explain their ideas. Internships or projects can help—try building a small app or contributing to open-source code on GitHub. I’ve heard NMIET in Bhubaneswar offers labs where students work on such projects, and with ties to companies like Cognizant and IBM, it’s a place that comes to mind for practical training. Ranked #248 by Collegedunia for 2025 with a 3.4/5 rating on Shiksha, it shows promise.

How to Prepare Step-by-Step

Let’s break it down. First, assess your current skills. Take online tests to spot weak areas—coding or system design, for example. Then, create a study plan. Spend 2-3 hours daily on coding practice, using resources like GeeksforGeeks, which offers free tutorials tailored for interviews.

Next, build a portfolio. Showcase projects on GitHub or a personal website. A 2023 Internshala report notes that candidates with portfolios are 30% more likely to get interviews. Mock interviews are key too—sites like InterviewBit let you practice with peers or experts.

Finally, apply early. FAANG recruitment starts 6-12 months before graduation. Check company websites or job portals like Naukri.com. If you’re at the best engineering colleges in Orissa, ask your placement cell for support—they often host drives with these firms.

Tips for the Interview Process

Interviews can feel intense, but they’re manageable. Expect coding rounds, where you’ll solve 2-3 problems in 60 minutes. System design questions might come up for senior roles—think about scaling a social media app. A 2024 Glassdoor survey shows 60% of candidates fail due to poor preparation, so practice is your friend.

Behavioral questions test your fit—be ready to share examples of teamwork or challenges you’ve overcome. Dress smartly and research the company beforehand. I’ve seen students benefit from college workshops, and places like NMIET in Bhubaneswar have sessions that prep you for this, with over 400 companies visiting for 3,500+ roles in 2024.

Opportunities and Growth

The payoff is worth it. FAANG offers global exposure, with some engineers moving to offices in the US or Europe. Product-based firms in India, like Flipkart, provide roles in product management or development, with growth to ₹20-30 lakhs in 5 years, per Naukri.com data. Internships are a great entry—Amazon and Google offer summer programs with stipends of ₹50,000-₹1 lakh.

You could start in testing or support roles and move up. A 2023 LinkedIn report highlights that 25% of tech hires in India come from internships, so don’t overlook them.

Getting Started Today

Ready to begin? Start with a coding challenge today—try LeetCode’s easy problems. Join college coding clubs or online forums to learn from others. If you’re considering the best engineering colleges in Orissa, look at their placement records and training programs. It’s about finding a place that fits your goals.

Take it step by step—don’t rush. A 2022 study from the Indian Institute of Management Ahmedabad suggests consistent effort over 6 months boosts success rates by 40%. So, what do you think? Excited to aim for FAANG? I’m here to help if you need more advice!

[chandantripathi889]

Top 25 Machine Learning Interview Questions in 2025

The field of machine learning is constantly evolving with the onset of rapid technological advancements over the past few years. This has made it an extremely exciting yet challenging field. It doesn’t matter if you’re a fresher or an experienced professional, jobs in the field of machine learning are booming, and you could be the one who lands one of the best offers!

However, before landing a job, comes the tasking feat of answering the machine learning interview questions you’ll be definitely asked once your application makes it through. These questions assess your theoretical knowledge as well as your practical experience and ultimately decide if you’re the candidate they should really hire for the role. This blog covers the top 25 machine learning interview questions that will help you ace your next interview.

1. What is machine learning?

This is one of the machine learning basic interview questions that every candidate should be prepared for. Understanding the fundamentals of machine learning is key to tackling more advanced questions.

Answer: Machine learning is a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. It involves creating algorithms that can improve their performance over time as they are exposed to more data.

2. What are the different types of machine learning?

Another fundamental question in machine learning interview questions for freshers. The answer to this question will test your understanding of the primary categories in ML.

Answer: There are three main types of machine learning:

Supervised learning: The algorithm learns from labeled data to make predictions.

Unsupervised learning: The algorithm works with unlabeled data and identifies patterns.

Reinforcement learning: The algorithm learns by interacting with an environment and receiving feedback from actions taken.

 *InterviewBit

3. What is the difference between classification and regression?

One of the common machine learning algorithms interview questions that assess your knowledge of basic ML techniques.

Answer: Classification is the task of predicting a discrete label (e.g., classifying emails as spam or not), while regression involves predicting a continuous value (e.g., predicting housing prices).

4. Explain the concept of overfitting and underfitting.

This is a common topic in AI and machine learning interview questions because it is essential to evaluate a candidate’s understanding of model performance.

Answer:

Overfitting occurs when the model learns the noise in the training data, causing it to perform poorly on new data.

Underfitting happens when the model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test sets.

5. What are the different evaluation metrics for classification problems?

Evaluating classification models is a critical step in ML, and this question tests your knowledge of metrics used in machine learning interview questions.

Answer: Common evaluation metrics for classification include accuracy, precision, recall, F1 score, and ROC-AUC.

6. What is cross-validation, and why is it important?

Cross-validation is often tested in machine learning interview questions for freshers as it is a basic yet crucial concept in evaluating model performance.

Answer: Cross-validation is a technique used to assess how the results of a model generalize to an independent dataset. It helps reduce overfitting by using multiple data splits for training and testing.

7. Explain the bias-variance tradeoff.

The bias-variance tradeoff is a critical concept that often comes up in machine learning basic interview questions.

Answer:

Bias refers to the error introduced by approximating a real-world problem with a simplified model.

Variance refers to the error caused by the model’s sensitivity to small fluctuations in the training data. The tradeoff is about balancing the two to achieve the best model performance.

8. What are decision trees, and how do they work?

Decision trees are one of the most fundamental machine learning algorithms. They often appear in machine learning algorithms interview questions.

Answer: A decision tree is a supervised learning algorithm that splits the data into subsets based on feature values. It builds a tree-like structure with decision nodes and leaf nodes that represent the output predictions.

9. What is a random forest?

This question is commonly asked when discussing machine learning algorithms, interview questions and tests your knowledge of ensemble methods.

Answer: A random forest is an ensemble of decision trees that work together to improve the accuracy of predictions. It reduces overfitting by averaging the predictions of multiple trees.

10. What are k-nearest neighbors (KNN)?

KNN is another common topic that often comes up in AI and machine learning interview questions.

Answer: KNN is a supervised learning algorithm that classifies a data point based on the majority class of its k-nearest neighbors. It is simple but effective for classification tasks.

11. How does gradient descent work?

Gradient descent is a critical optimization technique and a key part of many ML algorithms. This question is frequently asked in machine learning interview questions.

Answer: Gradient descent is an optimization algorithm used to minimize the cost function by iteratively adjusting the parameters of the model in the direction of the steepest gradient.

12. What is the difference between L1 and L2 regularization?

This is a machine learning basic interview question that assesses your understanding of techniques to prevent overfitting.

Answer:

L1 regularization adds the absolute value of the coefficients to the cost function.

L2 regularization adds the square of the coefficients to the cost function. Both techniques help reduce overfitting by penalizing large coefficients.

13. What is the role of a confusion matrix?

A confusion matrix is essential for understanding the performance of a classification model, and this question is often asked in machine learning interview questions for freshers.

Answer: A confusion matrix is a table that shows the true positive, true negative, false positive, and false negative values for a classification model. It helps evaluate the accuracy and other metrics.

14. Explain the difference between bagging and boosting.

Machine learning algorithms interview questions often test your understanding of ensemble techniques like bagging and boosting.

Answer:

Bagging involves training multiple models independently and then combining their predictions, which helps reduce variance.

Boosting trains models sequentially, with each model learning from the errors of the previous one, helping to reduce bias.

15. What is the purpose of support vector machines (SVM)?

Support Vector Machines are frequently tested in AI and machine learning interview questions due to their widespread use in classification tasks.

Answer: SVM is a supervised learning algorithm that finds the hyperplane that best separates the data points into different classes. It works well for both linear and non-linear classification tasks.

16. How would you handle missing data in a dataset?

This is a practical question that tests your ability to preprocess data, commonly asked in machine learning interview questions.

Answer: Missing data can be handled by:

Imputing with mean, median, or mode.

Using algorithms that support missing values.

Dropping rows or columns with missing data, depending on the extent of missing values.

17. What is feature selection, and why is it important?

Feature selection is a crucial part of machine learning and is often covered in machine learning interview questions for freshers.

Answer: Feature selection is the process of choosing the most important features for your model. It helps improve performance by reducing overfitting, simplifying the model, and decreasing computation time.

18. What is principal component analysis (PCA)?

PCA is an essential technique for dimensionality reduction and is often tested in machine learning basic interview questions.

Answer: PCA is a statistical technique that transforms high-dimensional data into fewer dimensions by finding the principal components that capture the most variance in the data.

19. Can you explain the term "Deep Learning"?

Deep Learning is an area that is becoming increasingly relevant in machine learning interview questions.

Answer: Deep learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in data. It is especially effective for tasks like image and speech recognition.

*LinkedIn

20. What is the role of neural networks in machine learning?

This is another common machine learning interview question that evaluates your understanding of advanced machine learning techniques.

Answer: Neural networks are used to model complex relationships between inputs and outputs. They are particularly useful for tasks involving unstructured data, such as images, audio, and text.

21. How would you deal with imbalanced datasets?

In many practical machine learning problems, datasets are imbalanced, and this question tests how you approach such scenarios in AI and machine learning interview questions.

Answer: Techniques for dealing with imbalanced datasets include:

Resampling techniques such as oversampling the minority class or undersampling the majority class.

Using appropriate evaluation metrics like the F1 score or balanced accuracy.

Using algorithms like Random Forest or XGBoost that are less sensitive to class imbalance.

22. What is transfer learning?

Transfer learning is an important concept in deep learning, and this question may appear in machine learning interview questions for freshers.

Answer: Transfer learning involves using a pre-trained model on a new task. It leverages the knowledge gained from the original task and adapts it to a similar but different task, reducing the need for a large dataset.

23. How do you assess the performance of a regression model?

In machine learning basic interview questions, candidates are often asked to evaluate the performance of regression models.

Answer: Performance of regression models can be assessed using metrics like Mean Squared Error (MSE), R-squared, and Root Mean Squared Error (RMSE).

24. What is the difference between a generative and a discriminative model?

This is a more advanced question you might encounter in machine learning algorithms interview questions.

Answer:

Generative models model the joint probability distribution of the input and output data (e.g., Naive Bayes).

Discriminative models focus on modeling the conditional probability of the output given the input data (e.g., logistic regression, SVM).

25. What are some common challenges in deploying machine learning models?

Finally, this machine learning interview question tests your ability to move from the theoretical to the practical side of machine learning.

Answer: Common challenges in deploying machine learning models include handling real-time data, ensuring model scalability, model interpretability, and managing the lifecycle of models in production.

Final Thoughts on Machine Learning Interview Questions

Machine learning continues to evolve rapidly, and staying updated with the latest techniques and theories is crucial for success in 2025. The interview questions on machine learning discussed above cover fundamental concepts, algorithms, and practical applications that will be key to your success in any machine learning role. Preparing for these questions will give you the confidence to handle the most common challenges you may face during your interviews.

If you are looking to enhance your machine learning knowledge or need assistance in preparing for your next machine learning interview questions, consider enrolling in the Advanced Certificate Programme in Machine Learning, Gen AI & LLMs for Business Applications – IITM Pravartak Technology Innovation Hub of IIT Madras, offered in partnership with Jaro Education. This programme will provide you with a comprehensive overview of everything you need to know about machine learning. All the best!

[mitscollege]

Building the Way to Success in Tech for BCA Students

The tech industry is booming, and opportunities abound-from software development and data analytics to cybersecurity and cloud computing. For the BCA student this field is no doubt interesting but very competitive. What makes the cut to be unique and successful within a tech industry is beyond a degree - it demands a harmonious blend of skills, experience, and strategic planning. Today, with this blog post, we'll know the key tips through which BCA students can carve out a successful tech career by honing technical skills, good networking, and staying updated with the changing trends in the industry.

1. Master the Fundamentals of Programming and Problem-Solving

Well, basically, programming forms the backbone of the tech industry, and when it comes to a student studying BCA, it becomes important to master the same in order to have an excellent career. Along with the course of your BCA, you are taught various programming languages like C, C++, Java, Python, and other programming languages. Try to master all because most of the tech jobs are covered by these programming languages, whether it is software development, web development, or data science.

Practice HackerRank, LeetCode, and Codeforces are platforms you can practice coding regularly. It helps develop your problem-solving skills and learn algorithms more efficiently. Work on projects. Making your own websites, applications, or software will provide hands-on experience and the chance to apply what's learned to real-world situations.

Participate in Coding Contests: One should really attempt CodeChef and Google's Kick Start since that is the best place to test oneself and hone the coding skills and shine for employers. Remember, the employers value both problem-solving as much as the programming aspect so, logical thinking and finding the most optimum solution to complex problems shall always be key.

2. Strong Base in Data Structures and Algorithms

One of the most crucial aspects of computer science and programming is DSA- data structures and algorithms. Once you grasp DSA, your ability to write code allows its execution as efficiently and optimized as possible, and therefore, very critical in developing scalable software and systems. In fact, the tech companies such as Google, Amazon, and Microsoft are keen on DSA during their hiring process.

Learn Basics: Begin by learning the fundamental data structure, like arrays, linked lists, stacks, queues, hash tables, and binary trees. Practice writing each of them from scratch in many programming languages.

Master Algorithms: Learn typical algorithms like sorting, searching, and recursion. Study the more advanced topics of dynamic programming, greedy algorithms, and graph traversal algorithms.

Practice Problems: GeeksforGeeks, InterviewBit, and LeetCode are platforms that offer structured DSA practice problems across multiple levels of difficulties. Start with easy problems and keep moving on to more challenging levels. DSA is not only a technical interview preparation but also helps in designing systems in a much better way in the workplace.

3. Learn Beyond the Classroom: Explore New Technologies

As your BCA programme will allow you a great foundation in key areas such as programming, database management and web development, do not take this as the only major happenings out there in the tech space, since new technologies and tools are surfacing every single day. For these purposes, you might consider exploring some of them.

Technologies to explore:

Some of the prominent ones existing in the market today are AWS, Microsoft Azure, and Google Cloud. Thus, you would really learn how to deploy and manage your cloud infrastructure to get a career in the field of cloud computing and DevOps. Artificial Intelligence and Machine Learning: AI and ML are changing the face of industries like healthcare, finance, and e-commerce. But by learning Python, TensorFlow, and other tools for AI and ML, it would be possible to build predictive models and, consequently, develop AI-driven applications.

Blockchain: With increasing applications in the finance sector, supply chain management, and security, blockchain technology is getting popular. Understanding how blockchain works and building smart contracts on platforms like Ethereum will put you at an edge in this niche.

Cybersecurity: Increasing cases of cyber threats demand lots of cybersecurity skills. Thus, one should opt for courses like network security, ethical hacking, and data encryption to stand out in the niche of cybersecurity.

You will be in a position to understand and cope with industry changes and be placed in specialization roles that could best fit your interest.

4. Learn through Internships

This will enable you to interact with new technologies and know how you can adapt them into your daily practice.

Internships will give you real-world experience in which you will be using everything you have learned. You will also get to know different roles, industries, and technologies that can help you in your chosen path. But most of all, they can really make your resume spectacularly jump at the face of any hiring manager for a full-time job after graduation.

How to find and excel in internships:

**Tapping into Networking: Reach out to professors, alumni, and business professionals for the purpose of locating an internship. Utilize LinkedIn in building connections with possible employers and seek out job openings. Apply Early: Start searching for internships early because most companies post their applications months before the actual date the internship is set to start.

Be Proactive: After you get the internship, it is not just about taking advantage of being at a good organization but also to your benefit, it presents an opportunity to bring value. Engage in real projects, learn from feedback, and most importantly, seize opportunities during your period of mentorship.

That experience, no matter how small you start with - whether paid or unpaid internships - will eventually pay dividends in the long term because it adds to your list of skills and professional experiences.

5. Acquire Soft Skills Along With Technical Skills

With technical skills, one might achieve partial fulfillment of success in the tech industry. Soft skills are equally important, such as good communication, team working, time management, and problem-solving skills, without which a technical professional will not be able to fly high in that industry. When working in a team environment, proper explanation of the technical concepts to nontechnical stakeholders, managing projects properly, and working with heterogeneous groups is a major requirement.

Key soft skills to focus on :

Communication: Learn how to communicate technical ideas in both writing and speaking.

Teamwork: Most of the work in tech involves teamwork. You will work with designers, marketers, and project managers. All these will help you gain great interpersonal skills.

Time Management: Deadlines and multiple projects at once are some of the regular phenomena. Proper time management helps target priorities and work effectively.

A balance between your technical and soft skills will make you a more diversified candidate, thereby making you more employable in the highly competitive job market.

6. Portfolio and Personal Branding

A diversified portfolio, showing off your skills, projects, and experiences, will differentiate you from other applicants. It should prove that you can solve problems, write clean code, and deliver high-quality projects.

What to include:

Personal Projects: Make sure to describe and include code for personal and class projects worked on. It's better to have a few well-executed projects than many unfinished ones.

GitHub Repository: Include your GitHub profile in your resume. This provides employers with an opportunity to review your code, contributions and development practices. You can include the following in the portfolio:

Certificates: If you have undertaken some certificates that illustrate your sector of competence in a particular domain, such as cloud computing, data analytics, or cybersecurity. Include all those certificates in the portfolio

Blog or Website: If possible, create a personal website or blog whereby one can express his thoughts regarding their portfolio and may write concerning the tech topic he or she is enthusiastic about. This would help build your own brand and make you out as the source of ideas or as a thought leader in your chosen field.

7. Stay updated about current trends and network professionally

The reason for this is that the technology sector keeps changing rapidly, and awareness of cutting-edge technology is a must in order to avoid outdatedness. Update yourself regularly by reading the latest technology blogs, participating in conferences, and joining online groups to keep you posted on current technologies, the standards of the industry, and the best practices prevailing in the industry.

Staying updated and networking

Follow Tech blogs proactively: Websites like TechCrunch, Wired, and The Verge keep you abreast of new technologies, startups, and innovations. Subscribe to them for staying up-to-date.

Online Communities: Engage yourself on websites like Reddit, Stack Overflow, and GitHub, where you get to spend time with other tech professionals, ask questions, and remain updated on the industry trends.

Meetups and Conferences: Industry conferences, webinars, and meetups with the locals are fantastic ways to expand your professional network, network ideas from industry thought leaders, and perhaps discover job opportunities.

Effective networking opens doors to internships, jobs, and mentorships. Build strong networking relationships with professors, peers, and industry professionals.

8. Prepare for Job Interviews and Technical Tests

Hiring into the tech field normally comes after the candidate is able to stand out from the rest of the applicants by passing the intense technical interview and coding tests. The early preparation stage can be better done if one practices coding problems, reviews key concepts in computer science, as well as educates themselves about common questions asked.

Interview Prep Tips

Practice Mock Interviews: Mock interviews with friends or on platforms like Pramp will help you get accustomed to answering technical questions under pressure.

Revisit the Fundamentals: Practice data structures and algorithms, operating systems, database management, and networking. These are common interview hotspots.

Learn About the Company: Once you know which company you'll be interviewing with, dig up knowledge regarding its product/service, technologies, and business model. That way, you will tailor your answers to what they'll need most and show interest in their corporation.

This will equip you with greater confidence in technical and behavioral interviews.

9. Higher Education and Specialization

A BCA is a wonderful start but most continue their education further for specialisation. One may decide to pursue his or her Master's in Computer Applications (MCA), MS in Computer Science, or an MBA in Information Technology, depending on career prospects.

Advance degrees help you deepen knowledge in specialized fields like artificial intelligence, cybersecurity, data science, and cloud computing; this may boost your marketability and career prospects

Conclusion

Building an excellent tech career as a BCA student is more than just doing your course work. It will demand continued learning, practical experience, and strategic career planning. By perfecting programming skills, venturing into new technologies, getting hands-on experience, and developing relevant technical and soft skills.

[list-my-startup]

InterviewBit Success Story: This Startup Helps Candidates To Secure Their Dream Job

[rohit-69]

Introduction to Scaler: Scaler stands as a leading online platform offering courses and training in IT fields. This article provides insights into Scaler's offerings, focusing particularly on genuine Scaler Academy reviews and feedback about Scaler's Data Science course.

Understanding Scaler: Scaler is a rapidly growing ed-tech organization dedicated to upskilling tech professionals. Offering courses in various tech fields such as data science, software engineering, and programming languages, Scaler aims to make a tangible impact in unlocking talents and opportunities across diverse tech domains.

Mission and Vision of Scaler: Scaler's mission is to enhance the skills of tech professionals through industry-vetted approaches, aiming to create over 1 million world-class tech professionals. Their vision underscores their success in upskilling thousands of graduates and earning trust from over 600 organizations.

Background of Scaler: Founded in 2019 by IIT Hyderabad alums Anshuman Singh and Abhimanyu Saxena, Scaler evolved from InterviewBit Software Services Pvt. Ltd. to meet the growing demand for skilled tech professionals. With institutional investors and tie-ups with organizations like NSDC, Scaler has become a prominent player in the ed-tech space.

Founders of Scaler: Abhimanyu Saxena and Anshuman Singh, co-founders of Scaler, bring a wealth of experience and entrepreneurial spirit to the organization, having worked extensively in the tech industry before launching Scaler.

Scaler Academy Reviews: Scaler offers a wide range of courses covering data science, software development, machine learning, and more. With a focus on practical learning and real-world projects, Scaler equips learners with the skills needed to excel in their careers.

Key Features and Unique Selling Points of Scaler: Scaler's strengths lie in its accessibility, real-life projects, career opportunities, and worldwide reach. The platform offers flexible learning options and a curriculum designed to meet industry demands.

Courses Offered by Scaler: Scaler provides a variety of courses catering to different skill levels and interests. From undergraduate programs to specialized courses in software development and data science, Scaler's offerings are designed to meet the diverse needs of learners.

Instructors and Mentors: With a team of experienced instructors and mentors from leading tech companies, Scaler ensures high-quality guidance and support for learners. Mock interviews, resume building sessions, and career counseling further enhance the learning experience.

In-depth Scaler Data Science Review: Scaler's Data Science course offers a comprehensive curriculum covering various topics such as machine learning, deep learning, algorithms, and Python. With features like 1:1 mentorship and real-life projects, the course prepares learners for successful careers in data science.

Pros and Cons of Scaler: While Scaler offers a variety of courses and analytics jobs assistance, some users have raised concerns about course fees and spamming. However, the platform's flexibility and pace of learning have been praised by many.

Scaler Reviews by Users: Feedback from users highlights both positive and negative experiences with Scaler. While some appreciate the learning opportunities provided, others have concerns about course fees and communication practices.

Conclusion: In conclusion, Scaler emerges as a valuable platform for learning software and data science programs. However, prospective learners are advised to conduct thorough research and evaluate their options before enrolling. By carefully considering course offerings, fees, and user feedback, individuals can make informed decisions to enhance their skills and pursue rewarding careers.

If you want to know more Scaler Academy Reviews or courses then do visit - analyticsjobs

[awesometeennews]

InterviewBit launches Scaler Edge, a college-companion program for engg students

InterviewBit launches Scaler Edge, a college-companion program for engg students

[ad_1]

In an effort to boost the availability of skilled tech talent, this program requires no prior experience in technology or coding and is open to students of all streams.

To address the gap between skills needed for high-quality technology jobs and work-readiness of graduates, Scaler (a platform by edtech startup InterviewBit) has launched what it claims is a first of its kind…

[csrstories]

Scaler Academy Acquires Pocket Friendly Coding Platform 'Coding Minutes' for $1M

Scaler Academy Acquires Pocket Friendly Coding Platform ‘Coding Minutes’ for $1M

Scaler Academy, an up-skilling platform for tech professionals and college students, has acquired the online learning platform Coding Minutes for $1 million in an all-cash deal. With the latest acquisition, Bengaluru-based startup Scaler aims to focus on building specialised content targeted at the beginner-level tech aspirant. To date, the edtech platform has focused on upskilling existing tech…

[newcodesociety]

Ready to Work?

I’ve been freelancing for many years now and I’ve used a few of these platforms in the past. Here’s a list of all the ones I could find that seemed reputable or I have used in the past.

I recommend going to InterviewBit to prepare for the interviews if you’re going to be working for a company.

How to Write A Cover Letter will help you prepare one. Heaven know it’s been a long time since I’ve had to write one.

Toptal

Upwork

Freelancer

People Per Hour

Simply Hired

Guru

Fiverr

Gun.io

Showcase

Remote Frontend Jobs

Flex Jobs

Tech Ladies

Well Found

Crunchboard

[the-blockchain-news]

Simple Token Announces First Four Member Companies - To Launch Branded Tokens on Project’s Ecosystem

Simple Token Announces First Four Member Companies - To Launch Branded Tokens on Project’s Ecosystem #DigitalKnights

Simple Token announced the official onboarding of its first four Member Companies to launch Branded Tokens within the Simple Token ecosystem: XAIN, InterviewBit, Digital Knights and Pepo. Simple Token enables mainstream applications to deploy their own branded crypto-backed token economies, in a scalable and cryptographically-auditable manner, without needing to mint and maintain their own…

[designstudioasa]

The space in the discussion was the office designed for the company Interviewbit.

The arc-shaped periphery promoted us to try out something totally out of the box.

The stunning arc led the way to the first circular-shaped sofa leading to more items of furniture that didn't shy away from flaunting their curves.

Thus the space is a beautiful canvas of free-flowing curves totally in harmony with each other.

Follow @designstudioasa for more updates

#studioasa#officefurniture#officedecor#officedecoration#officeproject#interiorstyle#interiors#architecture#spaces#pune#bangalore#interiordesigns#officestyling#officeinteriors#officeinterior

[technuter]

Scaler appoints Manish Pansari as Senior VP to strengthen its Data Science and Machine Learning Vertical

Scaler announced the appointment of Manish Pansari as their Senior Vice President - Business at InterviewBit and Scaler. Manish will lead the Data Science & Machine Learning business, providing differentiated and aspirational offerings for technical skilling. With an immediate focus on the Indian market, he will be responsible for scaling the DSML vertical. With over 20 years of experience, Manish has led business, operations and senior consulting roles across diverse organizations like Myntra, Jabong, Betterplace & Kearney and managed clients across geographies, including the US, Japan, China, South-East Asia, Middle East & India. Abhimanyu Saxena, Co-Founder, InterviewBit & Scaler, said, "We at Scaler are on a mission to transform the tech industry by equipping software professionals with the right skill-set to create meaningful impact in the real world. With this mission, it's imperative to have the brightest minds among us to achieve our goals. Over the years, our workforce has grown rapidly primarily due to our ability to attract the best talent from the industry. This has certainly influenced what we do and how we do it. Last year alone, amidst the sea of startup and tech layoffs, we expanded our workforce by 35%, which is a testament to our focus and strategic decision-making at Scaler. To fortify our leadership and further strengthen our business, I am delighted to welcome Manish aboard on this exciting journey. The abundance of experience that Manish possesses is sure to amplify our commitment towards career progression, echoing our resolve towards strengthening market leadership." Manish Pansari, Senior Vice President - Business at InterviewBit and Scaler, said, "The present ecosystem necessitates a complete technological revolution in the country's educational landscape. I am very excited by the opportunity to revolutionize this ecosystem and create a legacy. Scaler has been a torchbearer of innovation - bridging the gap between industry demand and prevalent pedagogy. I look forward to further building Scaler - growing the business profitably, with a focus on driving learning outcomes and delivering a superlative experience for all our learners and partners." The year 2022 saw Scaler witness significant growth and progress, with the startup maintaining a consistent month-over-month growth of 15 per cent. The edtech player achieved a $710 million valuation with a $55 million Series B funding, which helped Scaler focus on multiple key business areas. During the year, the startup forayed into the US market, launched first-of-its-kind co-living campuses, strengthened its leadership team, and launched its learning app on the Play Store. In 2022, Scaler was also recognised by AmbitionBox as "The Best Place to Work in India 2022" among tech startup companies with more than 500 employees in India. Read the full article

[chibuikem2022]

Dressing up the skeleton

In web design, HTML is the skeletal framework and CSS is the skin.

CSS stands for Cascading Style Sheet. A markup language like HTML is styled using a style sheet language called CSS. Our web pages can be styled using CSS (Cascading Style Sheets). To style our HTML document, we can utilise the background-color, font-size, font-family, border-color, width, and height attributes. There are three ways to apply CSS styling and they are inline, embedded and external I wouldn't recommend the first one because it's difficult to slow to edit and makes your HTML structure unorganised personally I prefer using the external way to apply CSS as it give me more order and I'm able to use just one css file for multiple HTML files in a website. For external CSS one must first link it to their HTML file in the <head> so it looks some thing like this

<head>   <link rel="stylesheet" href="styles.css"> </head>

where "styles.css" is the name of your css file. When this is successfully create the programmer can style the elements in HTML by either calling the tags, class or ID and applying the whatever style they choose like background colour, font family, padding, width and even flex box or grid (I'd be covering these in another post so don't forget to hit the follow button and turn on post notification so you get notified when I drop a new blog post.

Reference

Image gotten from interviewBit

[scaleracademy]

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