Learn Python, Jupyter, Linux, NumPy, SciPy, Scikit-learn, Pandas, Linear algebra, From Industry Experts. A foundation course for Machine Learning & Data Science
Welcome to this project on the Numpy - Cat vs Non-cat Classifier with Logistic Regression using Numpy. In this project, you will use Python and Numpy to build a Logistic Regression Classifier from scratch, and apply it to predict the class of an input image - whether it is a cat or a non-cat.
Though we have a lot of ready-made APIs like scikit-learn and Keras to build Machine Learning and Deep Learning models, it is very essential for a Machine Learning enthusiast to clearly understand the hidden mechanism behind the working of ML models. Upon completing this project, you will understand …
Welcome to the project on Mask R-CNN with OpenCV for Object Detection. In this project, we will learn how to read a pre-trained TensorFlow model for object detection using OpenCV.
The real-world scenarios have a lot of applications based on object detection. For example, object detection models are used in self-driving cars to recognize where the pedestrians are, where the are vehicles located, where the signals are, etc in the given frame of view. So, it is very important to develop an understanding of how to use a pre-trained object detection model so that we could later customize it based …
Welcome to this project on the Neural Style Transfer. In this project, you will use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image.
Neural Artistic Style Transfer finds a wide range of applications to fancily modify images. This field has so much influenced the technical world that many apps, such as Prisma, have received great craze amongst the users. In recent days, decent work has also been done in this area, which served as a holy grail to our project. The heart of this capability is the convolutional neural network …
This is a beginner-friendly end-to-end project for Machine Learning. The only prerequisite of the project is to know Python. Other than it, everything is covered in the project itself.
Perks of this project
Welcome to this project on Autoencoders for MNIST Fashion. In this project, we will understand how to implement Autoencoders using TensorFlow 2.
We will be understanding how to practically implement the autoencoder, stacking an encoder and decoder using TensorFlow 2. We will also depict the reconstructed output images by the autoencoder model.
Skills you will develop:
TensorFlow 2
scikit-learn
Matplotlib
Numpy
Data analysis is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making.
The steps to perform Data Analysis depends on the end goal we want to pursue such as to drive business decisions, evaluate performance, for making predictions, etc.
In this tutorial, we will perform Data Analysis with the end goal of feeding the data to a Machine Learning model i.e for making predictions.
This is a beginner-friendly end-to-end project for Data Analysis. The only prerequisite of the project is to know Python. Other than it, everything …
In any machine learning project, 90% of work is about data extraction, cleaning, preprocessing. This is a very challenging part of the machine learning projects. This skill is must have for any machine learning engineer.
Solve these problems to become very efficient at solving data preprocessing, cleaning, transforming, or extracting using Pandas, Python, and Numpy.