Machine Learning has brought about a revolution in the field of science and technology. Machine learning is finding widespread application in the fields of medical diagnosis, image processing, prediction of stock prices, and countless more.
Over the years, catering to your requests and with the emerging industry, we have churned out multiple articles on machine learning. Although it was difficult, we have curated a list of some of the finest of them out of the countless ones. This is a list of our top 10 machine learning blogs!
Have you been curious to learn how you can use Machine Learning algorithms for trading? This blog aims to guide you with it and impart the knowledge of using a machine learning system with Python for trading in financial markets. Towards the end, you’ll be able to create and use machine learning algorithms for trading using Python. And ustilising this, you’ll be able to create a simple ML algorithm to predict the next day’s closing price for a stock.
Harness the power of machine learning to make better trades by understanding the basic logic behind some popular and incredibly resourceful machine learning algorithms which have been used by the trading community as well as serve as the foundation stone on which you step on to create the best machine learning algorithms.
Machine Learning is broadly categorised into Supervised Learning and Unsupervised Learning, which further branch out to various concepts and terminologies that will be vital to your learning and application. Learn about the complete structure and classification and their implementation in Python, models and more! All covered in a robust manner in this grand article.
Applications for cluster analysis ranges from medical to face recognition to stock market analysis. In this blog, we talk about Heirarchical Clustering - a type of unsupervised learning that groups similar data points or objects into groups called clusters. Learn all there is to it through a detailed introduction, differences, classification, concepts, pros and cons etc.
The human brain, neurons, networks, and technology? How can these be related to each other? One of our most loved tutorials, it explains the concept of neural networks, how they work and their applications in trading. Learn about gradient descent, backpropagation and even create your own trading strategy.using the predictions from a neural network.
Develop a machine learning technique called Deep learning (Artificial Neural network) by using TensorFlow and predicting stock price in python. By the end of this article you will learn how to build artificial neural network by using TensorFlow and how to code a strategy using the predictions from the neural network.
You too can use the power of RNN (Recurrent Neural Networks), LSTM (Short Term Memory Networks) & GRU (Gated Recurrent Unit Network) and predict the stock price! How? This blog discusses it right from coding the strategy to building the model. In addition to this, there are several other important subtopics cost function, optimiser etc. that are addressed here.
One of the simplest algorithms used in machine learning for regression and classification problem, this blog explains the K-Nearest Neighbors ie. KNN machine learning algorithm. KNN algorithms use data and classify new data points based on similarity measures (e.g. distance function). A step by step implementation of KNN machine learning algorithm is done using Python in creating a trading strategy in this blog.
Did you know that it is possible to predict the price of Gold? Learn the interesting concept of using machine learning regression techniques to predict the price of one of the most important and precious metals, i.e., Gold. A reader favourite, this is a definite read for any trading enthusiast or expert!
This blog is a step-by-step illustration of building a simple template of a trading system using Regression modelling. You will not only understand regression and its types but also learn how you could build a trading system that uses regression analysis at its core. The Python programming language is utilized for this.
An erroneous bit of information in your price data can lead your Machine Learning model to yield false results. NOBODY would want that to happen. This blog is all about answering your data cleaning questions in the simplest form. It deals with the basic challenges in cleaning data like missing and duplicate data, and provides easy solutions.
It is possible to improve the performance of decision trees. We present to you the concepts of Bagging and Boosting. This blog focuses on Bagging and Boosting to build decision trees in parallel and in sequence respectively. It then ensembles the output to improve the forecast accuracy and reduce the variance.
TensorFlow is a core open source library to help you develop and train ML models. Combine this with the superior performance of the GPUs, you can drastically reudce the time and efforts. This blog shares the steps to Tensorflow GPU installation on a Nvidia GPU system in detail. With this guide you will be able to successfully install the Tensorflow GPU even as a complete beginner.
XGBoost has been able to parallelise the tree building component of the boosting algorithm. Thus, it reduces computation time and consumes fewer resources and this leads to a dramatic gain in terms of processing time as we can use more cores of a CPU or even go on and utilise cloud computing as well. This blog explains all about it as well as provides a Python code to predict long-short on US stocks.
This blog simply aims to help the reader be knowledgeable about using a classifier to predict whether the Bank Nifty index listed in NSE will go up or down, on the next day open using two Deep Learning models known as a stock model and index model. It takes you through the key findings and model implementation in detail.
Hope you enjoyed the glimpse of the top 10 machine learning blogs that were the favourites of our readers in the year 2021. Do comment and let us know what topics you’d like us to write blogs on in 2022.
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