Machine Learning with Python

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    Machine Learning with Python

    Machine Learning with Python

    Machine learning

    What you’ll learn

    Make predictions using linear regression, polynomial regression, and multivariate regression
    Master Machine Learning on Python
    Make robust Machine Learning models
    Have a great intuition of many Machine Learning models

    Machine Learning with Python


    Python programming Language


    We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

    What is Machine learning

    Features of Machine Learning

    Difference between regular program and machine learning program

    Applications of Machine Learning

    Types of Machine Learning

    What is Supervised Learning

    What is Reinforcement Learning

    What is Neighbours algorithm

    K Nearest Neighbours classification

    K Nearest Neighbours Regression

    Detailed Supervised Learning

    Supervised Learning Algorithms

    Linear Regression

    Use Case(with Demo)

    Model Fitting

    Need for Logistic Regression

    What is Logistic Regression?

    Ridge and lasso regression

    Support vector Machines

    Pre process of Machine learning data

    ML Pipeline

    What is Unsupervised Learning

    What is Clustering

    Types of Clustering

    Tree Based Modeles

    What is Decision Tree

    What is Random Forest

    What is Adaboost

    What is Gradient boosting

    stochastic gradient boostinng

    What is Naïve Bayes

    Who this course is for:

    Any students in college who want to start a career in Data Science.
    Any data analysts who want to level up in Machine Learning.
    Any people who want to create added value to their business by using powerful Machine Learning tools

    Course information :

    MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
    Genre: eLearning | Language: English | Duration: 29 lectures (3 hour, 2 mins) | Size: 1.5 GB

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