In natural language processing, Word2Vec is one of the most widely used vectorization techniques used for transferring words or textual data into vector or numerical form to feed it to the machine learning model and train the model. It has certain advantages over the BagofWords and TfIdf vectorizers which make more advanced and accurate vectorization …
Word2Vec Vectorizer in Natural Language Processing Read More »
Vectorization is a technique in natural language processing that is used to transform the text into vectors. However, multiple vectorization techniques are used to transform different texts. TFIDF is a widely used and efficient vectorization technique that data scientists use. In this article, We will discuss the TFIDF vectorization technique, how it works, the core …
TFIDF Vectorizers in Natural Language Processing Read More »
We can take linear algebra as a backbone of machine learning, as almost all the machine learning techniques and algorithms somehow use linear algebra to some extent. Some famous machine learning techniques like Principle component analysis and matrix factorization are entirely based on linear algebra. Although not only in dimensionality reduction, it is also helpful …
Linear algebra in machine learning is one of the essential topics to learn and have an idea about, as it is applicable and plays a critical role in many machine learning applications. Recommendation systems are primary unsupervised machine learning applications industries and companies use to recommend particular things to their customers and audience. In this …
Linear Algebra in Recommendation Systems of Machine Learning Read More »
In machine learning to achieve higher accuracies and performance of the model, the data quality plays a major role while enhancing the model. If good quality data with meaningful features are provided to the machine learning model, then the model can be very accurate and reliable with lesser computational powers. Principle Component Analysis, or PCA, …
Principle Component Analysis in Machine Learning Read More »
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 …
Data and its quality affect machine learning models and their accuracy, and the quality of the data can not be well in some types of problems which can be solved by machine learning. Here the scale of the data is also represented as the quality of the data, which needs to be expected while training …
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 …
AdaBoost vs. Naive Bayes Algorithms in Machine Learning Read More »
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 …
Logistic Regression vs. K Nearest Neighbors in Machine Learning Read More »
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 …
Linear Regression vs. Decision Trees vs. Support Vector Machines Read More »
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 …
10 Classification Cost Functions in Machine Learning Read More »
Activation functions in deep learning are the functions that decide the output from node or hidden layers from a given set of inputs in neural networks. There are many activation functions used in deep learning but among all of them rectified linear unit or ReLU is the widely used activation function in almost all deep …
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 …
How to Frame a Machine Learning Problem | Design Your Data Science Project Read More »
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 …
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 …
