Eigen Decomposition in Machine Learning

Matrix decomposition in linear algebra is one of the most valuable techniques for reducing the matrix into smaller parts. In matrix decomposition, the eigendecomposition is a fundamental concept that decomposes the matrix in eigen values and eigen vectors. This type of decomposition technique plays an essential role in techniques like Principle component analysis where there …

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AdaBoost vs. Naive Bayes Algorithms in Machine Learning

In machine learning, the algorithm plays a significant role while training and building a successful model. According to the data and its behavior, a proper machine learning algorithm should be selected for a better ad accurate model. Many choices are available in machine learning to use algorithms with hyperparameter tuning, making the algorithm selection process …

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Logistic Regression vs. K Nearest Neighbors in Machine Learning

In machine learning, using an appropriate algorithm according to the behavior of the data and its pattern is an essential thing to achieve higher accuracies and accurate performing models. Many machine learning algorithms can be used for almost all types of problem statements in machine learning. The best-performing among them can be selected based on …

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Linear Regression vs. Decision Trees vs. Support Vector Machines

Machine Learning algorithms are one of the most important things to decide during model training and building. All the datasets and problem statements related to that in machine learning are not the same and can have different patterns and complexity. In such cases, selecting the best suitable machine learning algorithms becomes a significant step of …

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10 Classification Cost Functions in Machine Learning

Cost functions in machine learning measure the performance of the machine learning algorithm trained on a particular dataset. By measuring the cost function or cost for a specific model, we can easily measure the error of the machine-learning model trained. There are many types of cost functions, from which depending on the problem statement the …

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How to Frame a Machine Learning Problem | Design Your Data Science Project

Machine learning is the concept that a computer program can learn and adapt to new data without human intervention. It is a sub-field of artificial intelligence that keeps a computer’s built-in algorithms current regardless of changes in the worldwide economy. Currently, most companies and agencies are using machine-learning techniques to solve their business problem, these …

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Blending Algorithms in Machine Learning

The Ensemble technique is one of the best-performing techniques used in the field of machine learning for getting better results on complex types of datasets. There are many ensemble techniques available that use multiple machine-learning algorithms for training on the same dataset and return great results compared to the single normal machine-learning algorithms. In this …

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Choosing the right database(RDBMS) for Data analyst?

Which database is best? The question, obviously, depends on what you want to use it for. I, like most Data analysts, want to use a database to warehouse, process, and manipulate data—and there’s no shortage of thoughtful commentary outlining the types of databases I should prefer. But these evaluations, which typically discuss databases in terms …

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Measures in Statistics for Data Science

Statistics is a critical component of data science and machine learning algorithms. Almost all the machine learning algorithms use mathematics in the backend, which is linear algebra and statistics. Learning and understanding the core intuition and the working mechanisms of any machine learning algorithm requires a core knowledge of statistics. In this article, we will …

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Why Does Lasso (L1) Regression Create Sparsity?

The Lasso regression is a regularization technique and a type of regression that is well-suited for models showing high levels of multicollinearity. It is also known as the L1 regularization technique. This technique is often used when there is a high number of features present in the dataset, as this technique can automatically perform feature …

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Auto-differentiation and Autograd explained step by step

We will understand what is automatic differentiation for absolute beginners, although this concept requires a fair amount of theoretical understanding of derivatives and the chain rule, But don’t worry I will try to explain in a very practical way, and we will build our knowledge one concept at a time and the end you will …

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Nesterov Accelerated Gradient (NAG) Optimizer in Deep Learning

In deep learning, optimizers are the type of function which are used to adjust the parameters of the model. The optimizers are used in deep learning to adjust the weights and biases of the neural networks and reduce the overall loss from the model to achieve higher accuracy. There are many types of optimizers used …

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2 Brilliant Breakthroughs in Computational Topology Using Machine Learning

Topology is a classical branch of mathematics, born essentially from Euler’s studies in the XVII century. It deals with the abstract notion of shape and geometry. The last decades were characterized by a renewed interest in topology and topology-based tools, due to the birth of computational topology and Spatial Data Analysis (SDA). Successful applications of …

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