Showing results for ;
  • Course
    13,500 Learners

    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

  • 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

  • Course
    3,532 Learners

    Learn Python foundations, Conditional Execution, Loops, Strings, Files, Lists, Tuples, Dictionaries, Jupyter and Linux from Industry Experts.

  • Learn Python foundations, Conditional Execution, Loops, Strings, Files, Lists, Tuples, Dictionaries, Jupyter and Linux from Industry Experts. The course for Valuebound team

  • Topic
    9 Concepts | 62 Questions | 7 Assessments | 11,860 Learners

    Learn Python foundations, Conditional Execution, Loops, Strings, Files, Lists, Tuples, Dictionaries, Jupyter and Linux from Industry Experts.

    Instructor: Sandeep Giri
  • Topic
    5 Concepts | 6 Assessments | 4,775 Learners

    Welcome to this project on Churning the Emails Inbox with Python. In this project, you will use Python to access the data from files and process it to achieve certain tasks. You will explore the MBox email dataset, and use Python to count lines, headers, subject lines by emails and domains. Know your way on how to work with data in Python.

    Skills you will develop:

    1. Python
    2. File Handling in Python
    Instructor: Abhinav Singh
  • 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 …

    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 …

    Instructor: Sandeep Giri
  • Topic
    11 Concepts | 2 Questions | 19 Assessments | 986 Learners

    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 …

    Instructor: Cloudxlab
  • 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 …

    Instructor: Cloudxlab
  • P

    Topic
    1 Concept | 4 Assessments | 686 Learners

    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 …

    Instructor: Cloudxlab
  • Topic
    13 Concepts | 2 Questions | 12 Assessments | 606 Learners

    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 …

    Instructor: Cloudxlab
  • E

    Topic
    2 Concepts | 15 Assessments | 436 Learners

    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:

    1. scikit-learn
    2. Data visualization
    3. Treating missing values in dataset 4 …
    Instructor: Cloudxlab