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    Real World Data Science and Machine Learning Projects

    Posted By: lucky_aut
    Real World Data Science and Machine Learning Projects

    Real World Data Science and Machine Learning Projects
    Duration: 4h 3m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.78 GB
    Genre: eLearning | Language: English

    Apply Machine Learning Algorithms and Build 8 real world machine learning projects in Python

    What you'll learn:
    Train machine learning algorithms to detect Heart Diesease.
    Build a Music Recommendation system.
    Train machine learning algorithms to detect Breast Cancer
    Train machine learning algorithms to predict Diabetes
    Automated Malaria detection using deep learning models like CNN
    Bitcoin price prediction using machine learning
    Time Series Prediction with LSTM Recurrent Neural Networks
    Artificial intelligence, Data science, Machine learning, Deep learning projects

    Requirements:
    Should Know Basics of Machine Learning
    Should Know about Machine Learning Libraries
    A passion to learn data science
    Jupyter notebook

    Description:
    Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions.
    In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action.
    Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.
    Basically, the machine learning process includes these stages:
    Feed a machine learning algorithm examples of input data and a series of expected tags for that input.
    The input data is transformed into text vectors, an array of numbers that represent different data features.
    Algorithms learn to associate feature vectors with tags based on manually tagged samples, and automatically makes predictions when processing unseen data.
    While artificial intelligence and machine learning are often used interchangeably, they are two different concepts. AI is the broader concept – machines making decisions, learning new skills, and solving problems in a similar way to humans – whereas machine learning is a subset of AI that enables intelligent systems to autonomously learn new things from data.
    In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 8 projects from scratch using real world dataset, here’s a sample of the projects we will be working on:
    Build a Music Recommendation system.
    Human activity recognition using smartphones
    Time Series Prediction with LSTM Recurrent Neural Networks
    Predicting presence of Heart Diseases using Machine Learning
    Automated malaria detection using deep learning models like CNN
    Predicting Prices of Bitcoin with Machine Learning
    Breast Cancer Prediction using Machine Learning
    Predicting Diabetes With Machine Learning Techniques

    Who this course is for:
    Beginner in machine learning
    Want to build real world machine learning projects

    More Info