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• Course
6,532 Learners

Learn Python, Jupyter, Linux, NumPy, SciPy, Scikit-learn, Pandas, Linear algebra, From Industry Experts. A foundation course for Machine Learning & Data Science

• Topic
9 Concepts | 14 Questions | 15 Assessments | 2,229 Learners

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 â€¦

Instructor: Sandeep Giri
• Topic
12 Concepts | 7 Questions | 11 Assessments | 1,664 Learners

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 â€¦

Instructor: Sandeep Giri
• Topic
2 Concepts | 1 Assessment | 1,655 Learners

This chapter covers different Pandas constructs and functions which are normally used in Machine Learning projects

Instructor: Sandeep Giri
• M

Topic
4 Concepts | 1 Assessment | 1,418 Learners

This chapter covers different Pandas constructs and functions which are normally used in Machine Learning projects

Instructor: Sandeep Giri
• Topic
7 Concepts | 2 Questions | 12 Assessments | 797 Learners

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 â€¦

Instructor: Cloudxlab
• Topic
14 Concepts | 18 Assessments | 384 Learners

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 â€¦

Instructor: Cloudxlab
• Topic
13 Concepts | 12 Assessments | 369 Learners

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 â€¦

Instructor: Cloudxlab
• Topic
8 Concepts | 1 Question | 8 Assessments | 202 Learners

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 â€¦

Instructor: Cloudxlab
• P

Topic
1 Concept | 9 Assessments | 84 Learners

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
Instructor: Cloudxlab
• P

Topic
2 Questions | 14 Assessments | 32 Learners

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.

Instructor: Cloudxlab