Python scikit-learn Pandas Data Processing Machine Learning Matplotilb Linear Regression Free Guided Project

Welcome to this project on the Forecasting Bike Rentals with DecisionTreeRegressor, LinearRegression, RandomForestRegressor using scikit-learn. In this project, you will use Python and scikit-learn to build models using the above-mentioned algorithms, and apply them to forecast the bike rentals.

Forecasting is a regression problem, which is a highly demanded skill in the real world. This exercise enables you to understand the basic workflow to solve a regression problem, which includes data preprocessing and data modeling steps. You will understand how Pandas and scikit-learn, in association with Python, could be used to solve a machine learning problem end-to-end project. In addition …

Python scikit-learn Pandas Data Processing Machine Learning Dimensionality Reduction Hyperparameter Tuning Free Guided Project

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 …

Python scikit-learn Pandas Data Processing Machine Learning Predictive Modelling Free Guided Project

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 …

NLP Python scikit-learn Data Processing Machine Learning Predictive Modelling NLTK Free Guided Project

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 …

Python Classification scikit-learn Logistic Regression Pandas SMOTE Free Guided Project

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 …

Numpy scikit-learn Pandas Data Processing Machine Learning Matplotilb Guided Project End to End project ML project Data Modelling Free

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

• This project is prepared while keeping beginners in mind. It will walk you through all the steps included in a Machine Learning Pipeline in detail.
• You will learn the answers to the three most important questions, i.e., Why, When, and How to do a particular thing.
• The concepts used in performing a step are explained there and then in a simple way for beginners.
• This project …

Python Tensorflow 2 Keras scikit-learn Custom Function Free Guided Project

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:

1. TensorFlow 2
2. Defining Custom Loss Function
3. Python Programming
4. scikit-learn

Python Tensorflow 2 Matplotlib Numpy scikit-learn Autoencoder

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:

1. TensorFlow 2

2. scikit-learn

3. Matplotlib

4. Numpy

Python Classification scikit-learn Pandas Machine Learning Free Guided Project

In this project, we will learn how to predict images from their noisy version. We will use the MNIST dataset for this project. First, we will load the dataset, explore it, and they we will learn how to introduce noise to an image. Next we will train a KNN Classifier to predict the original image from it's noisy version.

Skills you will develop:

1. scikit-learn
2. Python
3. KNN Classification
4. Machine Learning
5. Pandas