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Basic data science3/25/2023 Linear Regression, Polynomial Regression etc. Let’s deep dive into each one of them and explore what’s in them. I believe that any machine learning algorithm helps answer the following 5 questions: For simplicity I am denoting each type of the algorithm with a question that the algorithm helps us answer. Algorithms spin out an answer to the question asked by the user, given an appropriate dataset. First you need to understand what does an algorithm even do. So once you have the right structure and the amount of data, let’s look at the different types of machine learning algorithms available to solve problems. What are the key broad level types of machine learning algorithms? (I am not covering the data imputation part in this blog.)Ģ. There are various ways (like KNN imputation, Average / Median value imputation etc.) in which we can handle the missing values depending on the application area. If the dataset has a lot of missing values then the data is called a sparse data. (100 is just a multiplying factor and can be varied depending on how much data is available and the application area under consideration)Īlso, there is a very important concept of ‘sparse data’. if you have say 20 categorical variables with 15 levels / classes each and 18 continuous variables (for continuous variables assume 10 levels / classes) then the minimum number of rows needed is: (20 * 15 + 18 * 10) * 100 = 48,000 rows. The different columns are actually qualifying the patient and are termed as ‘Features’ of the dataset.Įstimating the right amount of data needed is crucial while solving a data science problem. Whereas, in the table on right, each row can be associated with a particular patient. The patient identifier has nothing to do with the current stock price and the premium amount of different insurance plans. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks.In the examples above, the left table dataset columns are totally not related to each other. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. If you already have some idea what machine learning is about or you have a strong mathematical background you may best enjoy jumping right in to the Create Machine Learning Models learning path. Option 3: The Create machine learning models learning path ✔ You are currently on this path, scroll down to begin. It's hands-on, but focuses more on understanding fundamentals and less on the power of the tools and libraries available. It makes no assumptions about previous education (other than a light familiarity with coding concepts) and teaches with code, metaphor, and visual that give you the ah ha moment. If you are looking to understand how machine learning works and don't have much mathematical background then this path is for you. ✔ Option 2: The Understand data science for machine learning learning path It's also the best path if you plan to move beyond classic machine learning, and get an education in deep learning and neural networks, which we only introduce here. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. It has all the same modules as the other two learning paths with a custom flow that maximizes reinforcement of concepts. This path is recommended for most people. Option 1: The complete course: Foundations of data science for machine learning These learning paths will get you productive on their own, and also are an excellent base for moving on to deep learning topics.įrom the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy to digest conceptual content and interactive Jupyter notebooks, all without leaving your browser.Ĭhoose your own path depending on your educational background and interests. Microsoft Learn provides several interactive ways to get an introduction to classic machine learning.
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