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Statistical Learning


Implementing Logistic Regression for Stock Trading

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Most stock trading algorithms that incorporate machine learning are based upon some form of linear regression. There are benefits and drawbacks to this method. The benefit of this is that the predicted prices of linear regression can be integrated into more complex values, that need the actual price values to function. The drawback is that for the basic "buy low, sell high" strategy, it is not directly related to predicting the direction of the price. What would happen if we used logistic regression, or more specifically binary classification, to predict if the price will increase or decrease? Theoretically, it would hone in on direction itself, and become more accurate than the signals generated by linear regression.


What is support vector regression (SVR) ?

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A support vector regression is a popular machine learning model today in this article, I would be giving you a detailed explanation and how this model works. Support vector model can be used for both problems regression as well as classification and it's divided into 2 parts support vector machine (SVM)is used for classification problems and support vector regression (SVR) is mostly used for regression problems but in this article, I would be telling you about support vector regression (SVR) to know more about support vector machine (SVM) go to this link


A project of upgrading a simple chatbot

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I initially wanted to learn how chatbot works and created a simple one on my own. However, there's a surprising amount of available codes that create chatbots of all levels. My task was to learn from this very simple chatbot model that uses straightforward NN of x and y, using stochastic gradient descent to predict the intention of the writer and generate an answer accordingly. As x is the input text, and y is the intention, the author marks 0 or 1 to the input texts. 1 is close to the intention, and 0 is the opposite. My idea of improvement is instead of marking 0 or 1 to the texts, I would implement words to vectors to create one hot encoded (X, Y) matrices, then feed the encoded words into the model.


Regression in Python using Sklearn, XGBoost and PySpark

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In the above story, we have used a Fitbit dataset. Based on the EDA, it was found that steps taken and calories are somewhat linearly correlated and together they may be indicative of a lower risk for all-cause mortality. More interestingly, among our data there is one dataset which has not been used yet which is a weight and BMI log. These data have a distinct nature since they are not necessarily machine generated, thereafter they serve the purpose of being'labels'. In simple words, users are collecting data regarding their activity using their Fitbit, and once in a while, they log some body information such as weight, fat and BMI.


Top 5 Machine Learning Algorithms for Data Science and ML Interviews

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Hello guys, you may know that Machine Learning and Artificial Intelligence have become more and more important in this increasingly digital world. They are now providing a competitive edge to businesses like NetFlix's Movie recommendations. If you have just started in this field and are looking for what to learn, then I will share 5 essential Machine learning algorithms you can learn as a beginner. These necessary algorithms form the basis of most common Machine learning projects. Knowing them well will help you understand the project and model quickly and change them as per your need.


Complete Machine Learning & Data Science with Python

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Machine learning is constantly being applied to new industries. Learn Machine Learning with Hands-On Examples What is Machine Learning? Machine Learning Terminology What are Classification vs Regression? Evaluating Performance-Classification Error Metrics Evaluating Performance-Regression Error Metrics Cross Validation and Bias Variance Trade-Off Use matplotlib and seaborn for data visualizations Machine Learning with SciKit Learn Linear Regression Algorithm Logistic Regresion Algorithm K Nearest Neighbors Algorithm Decision Trees And Random Forest Algorithm Support Vector Machine Algorithm Unsupervised Learning K Means Clustering Algorithm Hierarchical Clustering Algorithm Principal Component Analysis (PCA) Recommender System Algorithm Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective. Python is a general-purpose, object-oriented, high-level programming language. Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks. Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website. Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar Machine learning describes systems that make predictions using a model trained on real-world data. Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.


Gradient descent Method in Machine Learning

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Many deep learning models pick up objectives using the gradient-descent method. Gradient-descent optimization needs a big number of training samples for a model to converge. That creates it out of shape for few-shot learning. We train our models to learn to achieve a sure objective in generic deep learning models. However, humans train to learn any objective. There are different optimization methods that emphasize learn-to-learn mechanisms.


Commonly Used Machine Learning Algorithms

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KNN can easily be mapped to our real lives. If you want to learn about a person, of whom you have no information, you might like to find out about his close friends and the circles he moves in and gain access to his/her information!


Brief Guide for Machine Learning Model Selection

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Finding the best machine-learning algorithm to use for your problem can be challenging. However, usually, we do not have enough time for that. Given the following seven criteria to choose on, which will help to shortlist your choices to be able to apply them in a short time. The first criteria to choose your model on is explainability. If you need to explain the model and why it produces certain output to a non-technical audience such as stakeholders or business partners.


Courses for Machine Learning beginners

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Mathematics for Machine Learning:Mathematics is essential to understanding the notations of machine learning. It also provides the basics to solve machine learning-related problems. This is a specialization on Coursera to develop mathematical intuition by Imperial College London named Mathematics for Machine Learning. This specialization contains three courses containing Linear Algebra, Calculus, and Principal Component Analysis. Python for everybody: The specialization Python for everybody contains five courses to learn python.