Artificial Intelligence

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How to start learning Data Science? There are many resources available to help you start learning data science, depending on your background and goals. Here are a few steps you can take: Develop a strong understanding of the basics of statistics and programming. Learn Python or R programming languages, both are popular among data scientists. Learn the basics of data manipulation and visualization with tools such as pandas and matplotlib. Learn the basics of machine learning, such as linear regression and k-nearest neighbors, and practice applying them to real-world datasets. Take online courses and tutorials, such as those offered by Coursera, edX, and DataCamp. Practice by working on projects and participating in online data science competitions. Get familiar with popular data science libraries such as numpy, scikit-learn, tensorflow, keras and pytorch. It's a good idea to start with a solid foundation in statistics and programming, and then build on that foundation by learning the specific tools and techniques used in data science. As you gain experience, you can start working on more complex projects and exploring specialized areas of the field.

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Data Science Interview Q&A 1.What are the different types of Pooling? Explain their characteristics. Max pooling: Once we obtain the feature map of the input, we will apply a filter of determined shapes across the feature map to get the maximum value from that portion of the feature map. It is also known as subsampling because from the entire portion of the feature map covered by filter or kernel we are sampling one single maximum value. Average pooling: Computes the average value of the feature map covered by kernel or filter, and takes the floor value of the result. Sum pooling: Computes the sum of all elements in that window. 2. What is a Moving Average Process in Time series? In time-series analysis, moving-average process, is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic term. 3. What is the difference between SQL having vs where? The WHERE clause specifies the criteria which individual records must meet to be selected by a query. It can be used without the GROUP by clause. The HAVING clause cannot be used without the GROUP BY clause . The WHERE clause selects rows before grouping. The HAVING clause selects rows after grouping. The WHERE clause cannot contain aggregate functions. The HAVING clause can contain aggregate functions 4. What is Relative cell referencing in excel? In Relative referencing, there is a change when copying a formula from one cell to another cell with respect to the destination. cells’ address Meanwhile, there is no change in Absolute cell referencing when a formula is copied, irrespective of the cell’s destination. This type of referencing is there by default. Relative cell referencing doesn’t require a dollar sign in the formula. ENJOY LEARNING 👍👍

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Master the art of solving complex real-world problems using the power of machine learning and data science. 󰞪Are you one who knows the basics of Python/Machine Learning/Deep Learning/ Data Science/ Computer Vision or NLP but has not practiced on Real World Advanced Projects? 󰞪Are you in your last year of college but haven't worked on a real-world project? Or are you a professional who wants to switch to a Data-Oriented job but has no real-world projects to showcase on your resume? Then, we have something amazing for you. We have designed a project bundle having more than 550+ real-world solved projects in various domains like Machine learning, Data Science, Deep Learning, Computer vision, and NLP. You can download and learn how the experts have solved real-world problems. Each project is provided with complete source code(python3), dataset, and beginner-friendly explanation so that it doesn’t get your head screwed up. You can get a complete list of projects from our website. ⚫But Why? ➡️ Learn by working like a professional developer. ➡️ Build a GitHub portfolio that impresses recruiters. ➡️ Stand out with real proof of work. ➡️ You will find over 550+ real-world projects covering almost every aspect of Machine Learning. For more details, visit our website. www.tensorcode.io. Link: https://tensorcode.io/dragonbundle/

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