Learn Python, NumPy, Pandas, Scikit-learn, HDFS, ZooKeeper, Hive, HBase, NoSQL, Oozie, Flume, Sqoop, Spark, Spark RDD, Spark Streaming, Kafka, SparkR, SparkSQL, MLlib, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Learn Python, NumPy, Pandas, scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Learn Python, NumPy, Scipy, Pandas, Jupyter, Scikit-learn, Regression, Clustering, Classification, Support Vector Machines, Random Forests, Decision Trees and Dimensionality Reduction
Learn Python, Jupyter, Linux, NumPy, SciPy, Scikit-learn, Pandas, Linear algebra, From Industry Experts. A foundation course for Machine Learning & Data Science
Learn Python, NumPy, Pandas, scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning, Git, Docker, Kubernetes, Ansible, Terraform, Jenkins, and Graffana.
This chapter covers different NumPy constructs and functions along with Overview of Pandas, Matplotlib and Linear Algebra which is normally used in Machine Learning projects
A gentle introduction to the world of Machine Learning. Know about the various types of Machine Learning, and their various applications.
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€¦
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 â€¦
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 â€¦
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€¦