Machine Learning with Python

📄 Summary

This course provides an overview of the purpose of Machine Learning, and where it applies to the real world. It then covers topics such as supervised vs unsupervised learning, model evaluation, and various useful Machine Learning algorithms.

To explore the methods of machine learning, and the algorithms involved, many example projects are embarked upon and explored, including health care, banking, telecommunication, and so on.

The final project within this course is the building of a classifier to predict whether there will be rain the following day. It is a classification problem, and KNN, Decision Tree, SVM, and Logistic Regression are all used to determine the best algorithm to use.

📑 Main Topics

• Introduction to Machine Learning

  • Examples of machine learning in various industries
  • The steps machine learning uses to solve problems
  • Examples of techniques and Python libraries used
  • Differences between Supervised and Unsupervised algorithms

• Regression

  • Simple linear regression
  • Multiple linear regression
  • Non-linear regression
  • Evaluating regression models

• Classification

  • Comparisons between the different classification methods
  • K Nearest Neighbours (KNN) algorithm
  • Decision Trees
  • Logistic Regression
  • Support Vector Machines

• Clustering

  • k-Means Clustering
  • Hierarchical Clustering
  • Density Based Clustering

• Linear Classification

  • Compare and contrast the characteristics of different Classification methods. Explain the capabilities of logistic regression.
  • Compare and contrast linear regression with logistic regression.
  • Explain how to change the parameters of a logistic regression model.
  • Describe the cost function and gradient descent in logistic regression.
  • Provide an overview of the Support Vector Machine method.
  • Explain how multi-class prediction works. Apply Classification algorithms on various datasets to solve real world problems.

🔑 Key Skills Learned

• Understanding of various Machine Learning models, such as Regression, Classification, Clustering, and Recommender Systems
• Use of Python for Machine Learning (including Scikit Learn)
• Application of Regression, Classification, Clustering, and Recommender Systems algorithms on various datasets to solve real world problems

🏆 Certificates

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