log
Desarrollo Ciencias de la Información

Comparte si te a gustado:

Facebook Twitter
Machine Learning with Python Training (beginner to advanced)

Publicado en 10 Dec 2020

Udemy UK


Lo que aprenderás


  • Deep dive into the world of Machine Learning (ML)


  • Apply Python for Machine Learning programs


  • Understand what is ML, need for ML, challenges & application of ML in real-life scenarios


  • Types of Machine Learning


  • Components of Python ML Ecosystem


  • Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn


  • Regression analysis


  • scikit-learn Library to implement Simple Linear Regression


  • Multiple Linear Regression and Polynomial Regression


  • Logistic Regression


  • What is Classification, Classification Terminologies in Machine Learning


  • What is KNN? How does the KNN algorithm work?


  • What is a Decision Tree and Implementation of Decision Tree


  • SVM and its implementation


  • What is Clustering and Applications of Clustering


  • Clustering Algorithms


  • K-Means Clustering and K-Means Clustering algorithm example


  • Hierarchical Clustering


  • Agglomerative Hierarchical clustering and how does it work


  • Woking of Dendrogram in Hierarchical clustering


  • Implementation of Agglomerative Hierarchical Clustering


  • Association Rule Learning


  • Apriori algorithm and Implementation of Apriori algorithm


  • Introduction to Recommender Systems


  • Content-based Filtering


  • Collaborative Filtering


  • Implementation of Movie Recommender System


    • Requisitos


  • Enthusiasm and determination to make your mark on the world!


    • Descripción


    Machine Learning with Python - Course Syllabus


    1. Introduction to Machine Learning


    • What is Machine Learning?

    • Need for Machine Learning

    • Why & When to Make Machines Learn?

    • Challenges in Machines Learning

    • Application of Machine Learning


    2. Types of Machine Learning


    • Types of Machine Learning


           a) Supervised learning


           b) Unsupervised learning


           c) Reinforcement learning


    • Difference between Supervised and Unsupervised learning

    • Summary


    3. Components of Python ML Ecosystem


    • Using Pre-packaged Python Distribution: Anaconda

    • Jupyter Notebook

    • NumPy

    • Pandas

    • Scikit-learn


    4. Regression Analysis (Part-I)


    • Regression Analysis

    • Linear Regression

    • Examples on Linear Regression

    • scikit-learn library to implement simple linear regression


    5. Regression Analysis (Part-II)


    • Multiple Linear Regression

    • Examples on Multiple Linear Regression

    • Polynomial Regression

    • Examples on Polynomial Regression


    6. Classification (Part-I)


    • What is Classification

    • Classification Terminologies in Machine Learning

    • Types of Learner in Classification

    • Logistic Regression

    • Example on Logistic Regression


    7. Classification (Part-II)


    • What is KNN?

    • How does the KNN algorithm work?

    • How do you decide the number of neighbors in KNN?

    • Implementation of KNN classifier

    • What is a Decision Tree?

    • Implementation of Decision Tree

    • SVM and its implementation


    8. Clustering (Part-I)


    • What is Clustering?

    • Applications of Clustering

    • Clustering Algorithms

    • K-Means Clustering

    • How does K-Means Clustering work?

    • K-Means Clustering algorithm example


    9. Clustering (Part-II)


    • Hierarchical Clustering

    • Agglomerative Hierarchical clustering and how does it work

    • Woking of Dendrogram in Hierarchical clustering

    • Implementation of Agglomerative Hierarchical Clustering


    10. Association Rule Learning


    • Association Rule Learning

    • Apriori algorithm

    • Working of Apriori algorithm

    • Implementation of Apriori algorithm


    11. Recommender Systems


    • Introduction to Recommender Systems

    • Content-based Filtering

    • How Content-based Filtering work

    • Collaborative Filtering

    • Implementation of Movie Recommender System


    ¿Para quién es este curso?


  • Data Scientists and Senior Data Scientists


  • Machine Learning Scientists


  • Python Programmers & Developers


  • Machine Learning Software Engineers & Developers


  • Computer Vision Machine Learning Engineers


  • Beginners and newbies aspiring for a career in Data Science and Machine Learning


  • Principal Machine Learning Engineers


  • Machine Learning Researchers & Enthusiasts


  • Anyone interested to learn Data Science, Machine Learning programming through Python


  • AI Specialists & Consultants


  • Python Engineers Machine Learning Ai Data Science


  • Data, Analytics, AI Consultants & Analysts


  • Machine Learning Analysts


    • Debes tener en cuenta que los cupones duran maximo 4 dias o hasta agotar 1000 inscripciones,pero puede vencer en cualquier momento. Obten el curso con cupon haciendo clic en el siguiente boton:

    (Cupón válido para las primeras 1000 inscripciones): ML_UPLATZ
    Obtener Cupón Mas Cursos
    Udemy UK
    Tags:
    • #Python
    1065 0

    Articulos Relacionados

    content

    Python 3: Análisis y visualización de datos

    Comienza en el mundo del análisis de datos y añade valor a tu CV

    Ir al Curso
    content

    Data Science: Python for Data Analysis Full Bootcamp

    Build your Practical Python programming skills for Data Handling, Analysis and Visualization with Real Examples

    Ir al Curso
    content

    Python-Introduction to Data Science and Machine learning A-Z

    Python basics Learn Python for Data Science Python For Machine learning and Python Tips and tricks

    Ir al Curso
    Suscríbete a nuestro boletín
    Reciba los últimos Cupones y promociones (Solicitar Cupón)

    Usamos cookies en este sitio web para brindarle la mejor experiencia en nuestro sitio y mostrarle anuncios relevantes. Para obtener más información, lea nuestro política de privacidad& política de cookies.