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Supervised Machine Learning Principles and Practices-Python
Rating: 4.3 out of 5(48 ratings)
976 students

Supervised Machine Learning Principles and Practices-Python

Algorithms and Practical Examples in Python
Last updated 3/2023
English

What you'll learn

  • Understand the mathematics behine Machine Learning
  • Supervised Machine Learning Models such as Decision Tree, Support Vector Machine, k-Nearest Neighbor, Linear Regression etc.
  • Python Code for Supervised learning models
  • Creating a ML model and solving for a given set of data.

Course content

8 sections24 lectures5h 19m total length
  • Learning by Observation8:53

    Learning by observation

  • Learning Agents16:38

    Learning Agents

Requirements

  • Basic Mathematics, Programming foundations

Description

In this course, we present the concept of machine learning and the classification of different methods of learning such as Supervised and Unsupervised Learning. We also present reinforcement learning. We offer popular techniques and implement them in Python. We begin with the Decision Tree method. We present this simply with all the required mathematical tools such as entropy. We implement them in Python and explain how the accuracy can be improved. We offer the classification problem with a suitable real-life scenario. Linear Regression is taught using simple real-life examples. We present the L2 Error estimation and explain how we can minimize the error using gradient optimization. This is implemented using the Python library. We also offer the Logistic Regression method with an example and implement in Python. The Nearest Neighbourhood approach is explained with examples and implemented in Python. Support Vector Machines (SVM) are a popular supervised learning model that you can use for classification or regression. This approach works well with high-dimensional spaces (many features in the feature vector) and can be used with small data sets effectively. When trained on a data set, the algorithm can easily classify new observations efficiently. We also present a few more methods. The Bayesian model of classification is used for large finite datasets. It is a method of assigning class labels using a direct acyclic graph. The graph comprises one parent node and multiple children nodes. And each child node is assumed to be independent and separate from the parent. As the model for supervised learning in ML helps construct the classifiers in a simple and straightforward way, it works great with very small data sets. This model draws on common data assumptions, such as each attribute is independent. Yet having such simplification, this algorithm can easily be implemented on complex problems.


Who this course is for:

  • Bachelor and Master Degree students
  • Machine Learning Programmers