Welcome to this project on the Titanic Machine Learning Project with Support Vector Machine Classifier and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to build SVC and random forest, and apply them to predict the survival rate of Titanic passengers.
Data preprocessing is one of the most prominent steps to make an effective prediction model in Machine Learning, and it is often a best practice to use data preprocessing pipelines. In this exercise, you will also learn how to build your custom data transformers and chain all these data pre-processing steps using scikit-learn pipelines …
Welcome to this project on Image Classification with Pre-trained InceptionV3 Network. This project aims to impart the knowledge of how to access the pre-trained models(here we get pre-trained Inception model) from Keras of TensorFlow 2, and appreciate its powerful classification capacity by making the model predict the class of an input image.
Understanding the pre-trained models is very important because this forms the basis of transfer learning. one of the most appreciated techniques to perform the classification of a different task thus reducing the training time, the number of iterations, and resource consumption. Learning about the pre-trained models and …
Welcome to this project on the Spam Classifier Project with Logistic Regression Classifier using scikit-learn. In this project, you will use Python and scikit-learn to build a Logistic Regression Classifier, and apply it to predict whether an email is Spam or Ham.
The world is full of textual data being generated at a very rapid pace each second. The most important data preprocessing steps include accessing and cleansing the real-time data, transforming it to get a refined form, and making it in an ML-algorithm compatible way by representing the textual data into numerical form. You will learn to achieve all …
Welcome to this project on Getting Started with Matplotlib. In this project, you will understand how to use Matplotlib, one of the most famous visualizing libraries in Python.
Data visualization is one of the most prominent ways of analyzing the data. It presents visually appealing ways to detect the patterns, noise, outliers, and many other insights, which would assist the data scientists to understand, transform, and refine the data to build better comprehensive models. This project would help you build data visualization skills on top of your existing Python programming skills. You will understand how to use Matplotlib to depict …
This is an end-to-end Machine Learning project. You would start by learning how to load a dataset, visualize it, fill in the missing values, create pipelines, handle categorical variables, train models based on that data, and finally predict using that model.
This will not only help you understand how to train a machine learning model, but will also give you a detailed idea of how to clean and prepare data for machine learning, train the model, and fine tune it in real life projects.
Skills you will develop:
Welcome to this project on Credit Card Fraud Detection. In this project, you will use Python, SMOTE Technique(to over-sample data), build a Logistic Regression Classifier, and apply it to detect if a transaction is fraudulent or not.
The real world datasets often might be with data of imbalanced classes. It is very important to feed a decent number of data samples of each class in a classification problem so that the classifier would detect the underlying hidden patterns for each class and prepare itself to reasonably classify the test data. Upon completing this project, you will understand the pragmatic …
Welcome to this project on NYSE Closing Price Prediction. In this project, you will use Pandas, Keras, and Python in order to build a predictive model and apply it to predict the closing prices.
Time-series modeling has a huge demand in today's numbers-filled world. It has a wide variety of applications in sales s forecasting, prediction of meteorological elements like rainfall, economic forecasting in the financial worlds, and many more.
In this exercise, we shall understand how to predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data. We shall focus …
Welcome to the project on Building a Neural Network for Image Classification with TensorFlow. In this project, we would learn how to develop a neural network classifier from very scratch, using TensorFlow 2.
We would build and train a dense neural network on the Fashion MNIST dataset and evaluate its performance with some test samples. This project aims to impart the knowledge of the basic steps involved in building a neural network, working with TensorFlow 2, training a neural network, and make the learner comfortable with the cutting-edge technology - TensorFlow 2.
Skills you will develop:
Welcome to this project on Image Classification with Pre-trained Keras models. This project aims to impart the knowledge of how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2, and appreciate its powerful classification capacity by making the model predict the class of an input image.
Understanding the pre-trained models is very important because this forms the basis of transfer learning. one of the most appreciated techniques to perform the classification of a different task thus reducing the training time, the number of iterations, and resource consumption. Learning about the pre-trained models and …
Welcome to this project on Deploy Image Classification Pre-trained Keras model using Flask. In this project, we will have a comprehensive understanding of how to deploy a deep learning model as a web application using the Flask framework.
Developing a machine learning or deep learning model is very important to solve problems using AI. On the other hand, it is equally important to have a knowledge of how to deploy those amazing problem-solving models into such an interface which enables the users to make use of these solutions. Even many apps we use today, like YouTube, Amazon/Flipkart shopping, FaceApps …
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 …
Welcome to this project on Sentiment Analysis using TensorFlow 2. This project aims to impart an understanding of how to process English sentences, apply NLP techniques, make the deep learning model understand the context of the sentence, and classify the sentiment the sentence implies.
Our real-world is being flooded with a lot of reviews all around us. Be it an online shopping mart, movie reviews, offline market, or anything else. It has become very common for us to rely on these reviews. Hence it would be really helpful for a Machine Learning aspirant to understand various techniques related to processing …
Welcome to this project on Cat vs Non-cat Classifier using Transfer Learning. In this project, you will use Python and Keras to apply the Transfer Learning technique in order to build an image classifier, and apply it to predict the class of an input image - whether it is a cat or a non-cat.
Deep Learning is computationally intensive, often demanding powerful computational resources to yield reasonable accuracies in the real world. The idea of Transfer Learning has become a boon for the computer vision and deep learning community as it has reduced the hunger of Deep Learning algorithms for powerful …
Welcome to the project on Working with Custom Loss Function. This project aims to provide an understanding of how we could use the custom defined loss functions along with TensorFlow 2.
Though TensorFlow 2 already provides us with a variety of loss functions, knowing how to use a user-defined loss function would be crucial for a machine learning aspirant because often times in real-world industries, it is expected to experiment with various custom defined functions. This exercise is designed to achieve that goal.
Skills you will develop:
Welcome to this project on Classifying Flowers in Iris dataset with Deep Neural Network using Keras. In this project, you will use Python and Keras to build a Deep Neural Network, and apply it to predict the classes of Flowers in the Iris dataset.
Keras is one of the most extensively used APIs in the world of Deep Learning. It provides an amazing developer-friendly deep learning framework to build deep learning models with wide-ranging features to support high scalability, because of which it is not only widely used in academics but also in organizations to build state-of-the-art research models. In …