This chapter covers different Pandas constructs and functions which are normally used in Machine Learning projects
Welcome to this project on Classify Clothes from Fashion MNIST Dataset with a couple of Machine Learning algorithms like SGD Classifier, XGBClassifier, Softmax Regression (multi-class LogisticRegression), DecisionTreeClassifier, RandomForestClassifier, Ensemble (with soft voting) using scikit-learn. In this project, you will use Python and scikit-learn to build Machine Learning models, and apply them to predict the class of clothes from Fashion MNIST Dataset.
In this end-to-end Machine Learning project, you will get a hands-on overview of how to methodologically solve a machine learning classification problem. As a part of it, you will understand various methods of improvising the models using hyperparameter tuning …
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 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 …
A gentle introduction to Artificial Neural Networks. Know more about perceptrons, backpropagation, and build an image classifier with Keras.
Learn more about Analytics and Data Science, probability, normal distribution, variance, data cleaning, feature scaling, standardization from industry experts.
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 …
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 …