Tag: Machine Learning
Traffic Sign Classification Using Deep Learning in Python/Keras. In this Guided Project, you will: ... Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.
Logistic Regression with NumPy and Python. Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels.
Sentiment Analysis with Deep Learning using BERT. In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification.
Predicting House Prices with Regression using TensorFlow In this 2-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic ...
Unsupervised Machine Learning for Customer Market Segmentation. Compile and fit unsupervised machine learning models such as PCA and K-Means to training data. In this hands-on guided project, we will train unsupervised machine learning algorithms to perform customer market segmentation.
Introduction to Machine Learning This class will teach you the end-to-end process of investigating data through a machine learning lens. Learn online
Build a Machine Learning Web App with Streamlit and Python Build interactive web applications with Streamlit and Python
Machine Learning A-Z: Support Vector Machine with Python © The Complet Machine Learning and Support Vector Machine Course for Beginners in 2020
Upskilling in Machine Learning using R Learning Supervised & Unsupervised ML algorithms and implementation in R
machine learning, they need to be trained. But unlike most algorithms, neural networks are very critical to the amount of data, to the volume of the training sample, which is necessary in order to train them. And on a small amount of data