DataSpoof

Best book on machine learning by Abhishek thakur World first kaggle Grandmaster Paperback version https://amzn.to/3olDb9h

Some good books for data science are

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130 Machine Learning Projects Solved and Explained https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392

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https://www.instagram.com/p/CKac5Yihxtx/?igshid=13t66p3zki6k7 In the second part, we talk about 1- What are the different types of data 2- What is probability distribution 3- Types of Probability distribution 4- Definition of correlation and covariance Like ❤ and share

Harvard University has this Free course on Data Science : Machine Learning! Link to enroll : http://bit.ly/2WtDPFZ

Don't have enough data to train your model? Fret not! Use the synthetic one! 👉🏼 Synthetic data is artificially generated data that is not collected from real world events! It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. 🧠 Synthetic data can be used for many applications: - Privacy - Removing Bias - Balancing Datasets - Augment Datasets 👉🏼 Where to generate it from and how? Open Source Project YData Synthetic: This repository contains material on GANs for synthetic data generation, especially regular tabular data and time-series. It consists a set of different GAN architectures developed using Tensorflow 2.0. An example Jupyter Notebook is included, to show how to use the different architectures. Link: https://github.com/ydataai/ydata-synthetic 🌟 Star the repository to save it for future use or reference!

Interview Question When would you use standard Gradient Descent over Stochastic Gradient Descent, and vice-versa? Standard Gradient Descent theoretically minimizes the error function better than Stochastic Gradient Descent. However, Stochastic Gradient Descent converges much faster once the dataset becomes large. Thus standard Gradient Descent is preferable for small datasets while Stochastic Gradient Descent is preferable for the larger ones. In practice, however, Stochastic Gradient Descent is used for most of the applications because it minimizes the error function well enough while being much faster and more memory efficient for large datasets.

Interview Question What is the difference between Data Mining and Machine Learning? Machine Learning is a branch of Artificial Intelligence which aims at making systems learn automatically from the data provided and improve their learning over time without being explicitly programmed. Data Mining, on the other hand, focuses on analyzing the data and extracting knowledge and/or unknown interesting patterns from it. The goal is to understand the patterns in the data in order to explain some phenomenon and not to develop a sophisticated model which can predict the outcomes for the unknown/new data. For instance, you can use Data Mining on the existing data to understand your company’s sales trends and then build a Machine Learning Model to learn from that data, find the correlations and adapt for the new data.

Interview Question Define Sampling. Why do we need it? Sampling is a process of choosing a subset from a target population which would serve as its representative. We use the data from the sample to understand the pattern in the population as a whole. Sampling is necessary because often we can not gather or process the complete data within a reasonable time. There are many ways to perform sampling. Some of the most commonly used techniques are Random Sampling, Stratified Sampling, and Clustering Sampling.

Interview Question Q. How is k-Nearest Neighbors (k-NN) different from k-Means algorithm? A. The fundamental difference between these algorithms is that k-NN is a Supervised algorithm whereas k-means is Unsupervised in nature. B. k-NN is a Classification (or Regression) algorithm and k-means is a Clustering algorithm. C. k-NN tries to classify an observation based on its "k" surrounding neighbors. It is also known as a lazy learner because it does absolutely nothing at the training stage. On the other hand, k-means algorithm partitions the training data set into different clusters such that all the data points in a cluster are closer to each other than the data points from other clusters. The algorithm tries to maintain enough separability between these clusters.

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Why to study machine learning Why Study Algorithms? Computer scientists learn by experience. We learn by seeing others solve problems and by solving problems by ourselves. Being exposed to different problem-solving techniques and seeing how different algorithms are designed helps us to take on the next challenging problem that we are given. By considering a number of different algorithms, we can begin to develop pattern recognition so that the next time a similar problem arises, we are better able to solve it. Algorithms are often quite different from one another. Consider the example of sqrt seen earlier. It is entirely possible that there are many different ways to implement the details to compute the square root function. One algorithm may use many fewer resources than another. One algorithm might take 10 times as long to return the result as the other. We would like to have some way to compare these two solutions. Even though they both work, one is perhaps “better” than the other. We might suggest that one is more efficient or that one simply works faster or uses less memory. As we study algorithms, we can learn analysis techniques that allow us to compare and contrast solutions based solely on their own characteristics, not the characteristics of the program or computer used to implement them.

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There is no direct way to become data scientist. You have to be some experience first