Data Science & Machine Learning

Free Datasets to practice data science projects 1. Enron Email Dataset Data Link: https://www.cs.cmu.edu/~enron/ 2. Chatbot Intents Dataset Data Link: https://github.com/katanaml/katana-assistant/blob/master/mlbackend/intents.json 3. Flickr 30k Dataset Data Link: https://www.kaggle.com/hsankesara/flickr-image-dataset 4. Parkinson Dataset Data Link: https://archive.ics.uci.edu/ml/datasets/parkinsons 5. Iris Dataset Data Link: https://archive.ics.uci.edu/ml/datasets/Iris 6. ImageNet dataset Data Link: http://www.image-net.org/ 7. Mall Customers Dataset Data Link: https://www.kaggle.com/shwetabh123/mall-customers 8. Google Trends Data Portal Data Link: https://trends.google.com/trends/ 9. The Boston Housing Dataset Data Link: https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html 10. Uber Pickups Dataset Data Link: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city 11. Recommender Systems Dataset Data Link: https://cseweb.ucsd.edu/~jmcauley/datasets.html Source Code: https://bit.ly/37iBDEp 12. UCI Spambase Dataset Data Link: https://archive.ics.uci.edu/ml/datasets/Spambase 13. GTSRB (German traffic sign recognition benchmark) Dataset Data Link: http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset Source Code: https://bit.ly/39taSyH 14. Cityscapes Dataset Data Link: https://www.cityscapes-dataset.com/ 15. Kinetics Dataset Data Link: https://deepmind.com/research/open-source/kinetics 16. IMDB-Wiki dataset Data Link: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/ 17. Color Detection Dataset Data Link: https://github.com/codebrainz/color-names/blob/master/output/colors.csv 18. Urban Sound 8K dataset Data Link: https://urbansounddataset.weebly.com/urbansound8k.html 19. Librispeech Dataset Data Link: http://www.openslr.org/12 20. Breast Histopathology Images Dataset Data Link: https://www.kaggle.com/paultimothymooney/breast-histopathology-images 21. Youtube 8M Dataset Data Link: https://research.google.com/youtube8m/

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1. What is RDBMS? How is it different from DBMS? RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables. 2.What is ETL in SQL? ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase.Finally, we would have to load this tidy data into tools which would help us to find insights. 3. What is a kernel function in SVM? In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable. 4. What do you understand by the F1 score? The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.

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Q. Explain the data preprocessing steps in data analysis. Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks. 1. Data profiling. 2. Data cleansing. 3. Data reduction. 4. Data transformation. 5. Data enrichment. 6. Data validation. Q. What Are the Three Stages of Building a Model in Machine Learning? Ans. The three stages of building a machine learning model are: Model Building: Choosing a suitable algorithm for the model and train it according to the requirement Model Testing: Checking the accuracy of the model through the test data Applying the Model: Making the required changes after testing and use the final model for real-time projects Q. What are the subsets of SQL? Ans. The following are the four significant subsets of the SQL: Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc. Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc. Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE. Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc. Q. What is a Parameter in Tableau? Give an Example. Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines. For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.

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ML algorithms cheat sheet

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🚦Top 10 Data Science Tools🚦 Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science. 🛰What is Data Science ? Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data . 🗽Top Data Science Tools that are normally utilized : 1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text . 2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability. Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization. 3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning. 4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning. 5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively. 6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly. 7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts. 8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets. 9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem. 10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.

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