Data Science & Machine Learning

“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford https://datasimplifier.com/best-data-analyst-projects-for-freshers/ https://toolbox.google.com/datasetsearch https://www.kaggle.com/datasets http://mlr.cs.umass.edu/ml/ https://www.visualdata.io/ https://guides.library.cmu.edu/machine-learning/datasets https://www.data.gov/ https://nces.ed.gov/ https://www.ukdataservice.ac.uk/ https://datausa.io/ https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html https://www.kaggle.com/xiuchengwang/python-dataset-download https://www.quandl.com/ https://data.worldbank.org/ https://www.imf.org/en/Data https://markets.ft.com/data/ https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0 https://www.aeaweb.org/resources/data/us-macro-regional http://xviewdataset.org/#dataset http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php http://image-net.org/ http://cocodataset.org/ http://visualgenome.org/ https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1 http://vis-www.cs.umass.edu/lfw/ http://vision.stanford.edu/aditya86/ImageNetDogs/ http://web.mit.edu/torralba/www/indoor.html http://www.cs.jhu.edu/~mdredze/datasets/sentiment/ http://ai.stanford.edu/~amaas/data/sentiment/ http://nlp.stanford.edu/sentiment/code.html http://help.sentiment140.com/for-students/ https://www.kaggle.com/crowdflower/twitter-airline-sentiment https://hotpotqa.github.io/ https://www.cs.cmu.edu/~./enron/ https://snap.stanford.edu/data/web-Amazon.html https://aws.amazon.com/datasets/google-books-ngrams/ http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm https://code.google.com/archive/p/wiki-links/downloads http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/ https://www.yelp.com/dataset https://t.me/DataPortfolio/2 https://archive.ics.uci.edu/ml/datasets/Spambase https://bdd-data.berkeley.edu/ http://apolloscape.auto/ https://archive.org/details/comma-dataset https://www.cityscapes-dataset.com/ http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset http://www.vision.ee.ethz.ch/~timofter/traffic_signs/ http://cvrr.ucsd.edu/LISA/datasets.html https://hci.iwr.uni-heidelberg.de/node/6132 http://www.lara.prd.fr/benchmarks/trafficlightsrecognition http://computing.wpi.edu/dataset.html https://mimic.physionet.org/ ✅ Best Telegram channels to get free coding & data science resources https://t.me/addlist/4q2PYC0pH_VjZDk5 ✅ Free Courses with Certificate: https://t.me/free4unow_backup

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What do we do with categorical variables? Categorical variables must be encoded before they can be used as features to train a machine learning model. There are various encoding techniques, including: One-hot encoding Label encoding Ordinal encoding Target encoding

What is the area under the PR curve? Is it a useful metric? The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.

What is the PR (precision-recall) curve? A precision-recall curve (or PR Curve) is a plot of the precision (y-axis) and the recall (x-axis) for different probability thresholds. Precision-recall curves (PR curves) are recommended for highly skewed domains where ROC curves may provide an excessively optimistic view of the performance.

What is AUC (AU ROC)? When to use it? AUC stands for Area Under the ROC Curve. ROC is a probability curve and AUC represents degree or measure of separability. It's used when we need to value how much model is capable of distinguishing between classes. The value is between 0 and 1, the higher the better.

What kind of problems neural nets can solve? Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.

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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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What is PCA PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R: 🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently. 🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective. 🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more. 🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components. 🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data. 🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter. 🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful. Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.

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10 commonly asked data science interview questions along with their answers 1️⃣ What is the difference between supervised and unsupervised learning? Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data. 2️⃣ Explain the bias-variance tradeoff in machine learning. The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance. 3️⃣ What is the Central Limit Theorem and why is it important in statistics? The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes. 4️⃣ Describe the process of feature selection and why it is important in machine learning. Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy. 5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them? Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data. 6️⃣ What is regularization and why is it used in machine learning? Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features. 7️⃣ How do you handle missing data in a dataset? Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly. 8️⃣ What is the difference between classification and regression in machine learning? Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome. 9️⃣ Explain the concept of cross-validation and why it is used. Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting. 🔟 What evaluation metrics would you use to evaluate a binary classification model? Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.

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Machine Learning Project Ideas 👆

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