Introduction to Machine Learning & Supervised Learning

Machine Learning    |    Beginner
  • 17 videos | 46m 26s
  • Includes Assessment
  • Earns a Badge
Machine learning includes many different fields that focus on different problems. Explore what machine learning is and the fundamentals of supervised learning.

WHAT YOU WILL LEARN

  • Define machine learning and how it can be used to solve a variety of problems
    Define supervised machine learning
    Describe the fundamentals of building machine learning models to solve a problem
    Describe overfitting, how it can be a problem, and how to mitigate it
    Evaluate machine learning models and compare them
    Define the linear regression model for one and multiple variable problems
    Describe the gradient descent algorithm for training linear regression models
    Describe the k-nearest neighbor model and how to learn it
    Describe decision tree models and how to learn decision trees using the c4.5 algorithm
  • Setup scikit learn for python
    Import data, and perform basic tasks with scikit learn for python
    Use scikit learn to fit a linear regression model to a dataset
    Use scikit learn's k-nearest neighbor model
    Use scikit learn to fit a decision tree model to a dataset
    Use scikit learn and graphviz to generate a decision tree model from a dataset
    Use scikit learn to calculate the precision and the recall of different machine learning models in python
    Implement a linear regression model and python and fit it to a dataset

IN THIS COURSE

  • 3m 13s
    In this video, you will learn what machine learning is and how it can be used to solve a variety of problems. FREE ACCESS
  • 2m 35s
    In this video, you will learn how to define supervised machine learning. FREE ACCESS
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    3.  How to Build Models
    2m 50s
    After completing this video, you will be able to describe the fundamentals of building machine learning models to solve a problem. FREE ACCESS
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    4.  Overfitting
    1m 47s
    Upon completion of this video, you will be able to describe overfitting, how it can be a problem, and how to mitigate it. FREE ACCESS
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    5.  Precision and Recall
    2m 39s
    During this video, you will learn how to evaluate and compare machine learning models. FREE ACCESS
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    6.  Linear Regression Model
    1m 25s
    In this video, you will learn how to define the linear regression model for one and multiple variable problems. FREE ACCESS
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    7.  Gradient Descent Optimization Algorithm
    1m 26s
    After completing this video, you will be able to describe the gradient descent algorithm for training linear regression models. FREE ACCESS
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    8.  k-Nearest Neighbor (kNN)
    2m 12s
    Upon completion of this video, you will be able to describe the k-nearest neighbor model and how to learn it. FREE ACCESS
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    9.  Decision Tree Learning
    2m 19s
    After completing this video, you will be able to describe decision tree models and how to learn decision trees using the C4.5 algorithm. FREE ACCESS
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    10.  Introducing SciKit Learn for ML in Python
    5m 17s
    During this video, you will learn how to set up SciKit Learn for Python. FREE ACCESS
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    11.  Getting Started with SciKit Learn
    2m 42s
    During this video, you will learn how to import data and perform basic tasks with SciKit Learn for Python. FREE ACCESS
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    12.  Linear Model and Gradient Descent with SciKit Learn
    2m 26s
    During this video, you will learn how to use SciKit Learn to fit a linear regression model to a dataset. FREE ACCESS
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    13.  k-Nearest Neighbor With SciKit Learn
    3m 28s
    In this video, find out how to use the k-nearest neighbor model from SciKit Learn. FREE ACCESS
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    14.  Decision Tree Learning With SciKit Learn
    2m 22s
    Find out how to use SciKit Learn to fit a decision tree model to a dataset. FREE ACCESS
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    15.  Generating a Tree
    4m 52s
    Learn how to use SciKit Learn and GraphViz to generate a decision tree model from a dataset. FREE ACCESS
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    16.  Precision and Recall With SciKit Learn
    2m 54s
    In this video, learn how to use SciKit Learn to calculate the precision and recall of different machine learning models in Python. FREE ACCESS
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    17.  Exercise: Use Python for Linear Regression
    1m 58s
    In this video, you will implement a linear regression model in Python and fit it to a dataset. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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