Author: Naveen
Naveen PandeyExplanation for AI and Data Science by ChatGPT AI
Question to ChatGPT: Explain AI Artificial intelligence (AI) is the ability of a computer program or a machine to simulate human intelligence, including the ability to reason, learn, and solve problems. AI can be applied to a wide range of field, including robotics, natural language processing, computer vision, and machine learning. The goal of AI…
Read MoreImportant Machine Learning Concepts Part – 2
Ensemble Learning Training multiple models with different parameters to solve the same problem. A/B Testing Statistical way of comparing 2+ techniques to determine which technique performs better and also if difference in statistically significant. Baseline Model Simple model/heuristic used as reference point for comparing how well a model is performing. Bias Prejudice or favourite towards…
Read MoreImportant Machine Learning Concepts Part – 1
Features Input data/variables used by the ML model. Feature Engineering Transforming input features to be more useful for the models. e.g., mapping categories to buckets, normalizing between -1 and 1, removing null. Train/Eval/Test Training is data used to optimize the model, evaluation is used to asses the model on new data during training, test is…
Read MoreWhat is selection Bias?
Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples.…
Read MoreWhat is a confusion matrix?
The confusion matrix is a 2×2 table that contains 4 outputs provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Confusion matrix. A dataset used for performance evaluation is called a test data set. It should contains the correct labels and predicted labels. The…
Read MoreWhat is the ROC curve?
The ROC curve is a graph between False positive rate on the x axis and True positive rate on the y axis. True positive rate is the ratio of True positives to the total number of positive samples. False positive rate is the ratio of False positives to the total number of negative samples. The…
Read MoreWhat do you understand by true positive rate and false-positive rate?
True Positive rate (TRP) is the ratio of True Positives to True Positives and False Negatives. It is the probability that an actual positive will test as positive. TPR = TP / TP + FN The False Positive Rate (FPR) is the ratio of the False Positives to all the positives (True positives and false…
Read More3 Concepts Every Data Scientist Must Know Part – 3
1. What is the significance of sampling? Name some techniques for sampling? For analyzing the data, we cannot proceed with the whole volume at once for large datasets. We need to take some samples from the data which can represent the whole population. While making a sample out of complete data, we should take the…
Read More3 Important Neural Network Architectures Explained
1. Perceptron The perceptron is the most basic of all neural networks, being a fundamental building block of more complex neural network. If simple connects an input cell and an output cell. 2. Feed-Forward Network The feed-forward network is a collection of perceptions’. In which there are three fundamental types of layers – input layers,…
Read More3 Concepts Every Data Scientist Must Know Part – 2
1. Bagging and Boosting Bagging and Boosting are two different ways used in combining base estimators for ensemble learning (Like random forest combining decision trees). Bagging means aggregating the predictions of several weak learners. We can think of it combining weak learners is used in parallel. The average of the predictions of several weak learners…
Read MoreWhat is the Normal Distribution?
Probability distribution is the function that shows the probabilities of the outcome of an event or experiment. Consider a feature (i.e., column) in a dataframe. This feature is a variable and its probability distribution function shows the likelihood of the values it can take. Probability distribution function are quite useful in predictive analytics or machine…
Read MoreImportant Deep learning Concept Explained Part – 2
Converge Algorithm that converges will eventually reach an optimal answer, even if very slowly. An algorithm that doesn’t converge may never reach an optimal answer. Learning Rate Rate at which optimizers change weights and biases. High learning rate generally trains faster but risks not converging whereas a lower rate trains slower. Numerical instability Issues with…
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