With pattern recognition approach, there are many methods to implement a handwriting digits recognition task. In my previous stories, I have introduced the Linear Discriminant Analysis base on the Gaussian model maximum likelihood estimation. In this post, I apply the Logistic Regression model on the English numeral handwriting digits recognition task. In the logistic regression model, an occurrence probability of an event is represented by a logistic function. For example, in a two-class problem, the logistic sigmoid function is commonly used.
Regression is unarguably one of the most used models in data science and statistics. It is prevalent in almost every field in industry and academia. I will go through in this blog the statistical concepts involved in Simple Linear Regression i.e. regression involving only one predictor variable. The readers are assumed to have some basic knowledge of probability theory and statistics, although I have given references to the concepts. Let go through an example in R on a sample data.
Why? Existing tools are not well-suited to time series tasks and do not easily integrate together. Methods in the scikit-learn package assume that data is structured in a tabular format and each column is i.i.d. Packages containing time series learning modules, such as statsmodels, do not integrate well together. Further, many essential time series operations, such as splitting data into train and test sets across time, are not available in existing python packages. To address these challenges, sktime was created.
In this article, we will explain briefly about some of the best books that can help you understand the concepts of Machine Learning, and guide you in your journey in becoming an expert in this engaging domain. Moreover, these books are a great source of inspiration, filled with ideas and innovations, granted that you are familiar with the fundamentals of programming languages. As the title explains, if you're an absolute beginner to Machine Learning, this book should be your entry point. Requiring little to no coding or mathematical background, all the concepts in the book have been explained very clearly. Examples are followed by visuals to present the topics in a friendlier manner, for understanding the vitals of ML.
You can use MATLAB with AutoML to support many workflows, such as feature extraction and selection and model selection and tuning. Feature extraction reduces the high dimensionality and variability present in the raw data and identifies variables that capture the salient and distinctive parts of the input signal. The process of feature engineering typically progresses from generating initial features from the raw data to selecting a small subset of the most suitable features. But feature engineering is an iterative process, and other methods such as feature transformation and dimensionality reduction can play a role. Feature selection identifies a subset of features that still provide predictive power, but with fewer features and a smaller model.
Dimensionality reduction is an unsupervised learning technique. Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms. There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm. In this tutorial, you will discover how to fit and evaluate top dimensionality reduction algorithms in Python.
There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.
The journey of machine learning started in 1959 when Arthur Samuel introduced the term called Machine Learning. It is defined as a Field of study that gives computers the capability to learn without being explicitly programmed. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The main aim is to allow the machine to learn automatically from the examples that have been provided during learning. Now, when the term Machine Learning has become familiar to everyone and has become the most popular career and research choice as it is getting adopted by many industries, it has become important for everyone working in all industries to learn and explore Machine Learning and see what it has to offer. Machine Learning engineer is surveyed as the best job of 2019 and has shown the growth rate above 300%.
Why are there so many machine learning techniques? The thing is that different algorithms solve various problems. The results that you get directly depend on the model you choose. That is why it is so important to know how to match a machine learning algorithm to a particular problem. In this post, we are going to talk about just that. First of all, to choose an algorithm for your project, you need to know about what kinds of them exist.
How oversampling yielded great results for classifying cases of Sexual Harassment. When it comes to data science, sexual harassment is an imbalanced data problem, meaning there are few (known) instances of harassment in the entire dataset. An imbalanced problem is defined as a dataset which has disproportional class counts. Oversampling is one way to combat this by creating synthetic minority samples. SMOTE -- Synthetic Minority Over-sampling Technique -- is a common oversampling method widely used in machine learning with imbalanced high-dimensional datasets using Oversampling.