What Is Machine Learning Classification?
April 11, 2024
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This course is part of multiple programs.
Instructors: Joseph Santarcangelo
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A working knowledge of Python, along with data analysis and visualization techniques, and at least a high school-level understanding of mathematics.
(16,781 reviews)
Recommended experience
Intermediate level
A working knowledge of Python, along with data analysis and visualization techniques, and at least a high school-level understanding of mathematics.
Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.
How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.
How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.
How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.
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Python is one of the most widely used programming languages in machine learning (ML), and many ML job listings require it as a core skill. This course equips aspiring machine learning practitioners with essential Python skills that help them stand out to employers.
Throughout the course, you’ll dive into core ML concepts and learn about the iterative nature of model development. With Python libraries like Scikit-learn, you’ll gain hands-on experience with tools used for real-world applications. Plus, you’ll build a foundation in statistical methods like linear and logistic regression. You’ll explore supervised learning techniques with libraries such as TensorFlow and Pandas, as well as classification methods like decision trees, KNN, and SVM, covering key concepts like the bias-variance tradeoff. The course also covers unsupervised learning, including clustering and dimensionality reduction. With guidance on model evaluation, tuning techniques, and practical projects in Jupyter Notebooks, you’ll gain the Python skills that power your ML journey. ENROLL TODAY to enhance your resume with in-demand expertise!
This module provides you with knowledge of foundational machine learning concepts to delve deeper into applied machine learning modeling. You will learn that machine learning modeling is an iterative process with various lifecycle stages. You will also learn about the daily activities in the life of a machine learning engineer. Here, you will be introduced to various open-source tools for machine learning, including the popular Python package scikit-learn.
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In this module, you will explore two foundational statistical modeling methods, linear regression and logistic regression, which are considered classical machine learning models. Linear regression, often applied in real-world problem-solving, models a linear relationship between independent variables and a dependent variable. Logistic regression, an extension of linear regression, functions as a classifier and can handle nonlinear relationships through input transformation. By implementing these models, you'll gain insight into their limitations and better understand the advancements offered by modern machine learning models.
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In this module, you’ll learn about implementing modern supervised machine learning models. You will start by understanding how binary classification works and discover how to construct a multiclass classifier from binary classification components. You’ll learn what decision trees are, how they learn, and how to build them. Decision trees, which are used to solve classification problems, have a natural extension called regression trees, which can handle regression problems. You’ll learn about other supervised learning models, such as KNN and SVM. You’ll learn what bias and variance are in model fitting and the tradeoff between bias and variance inherent to all learning models in various degrees. You’ll learn strategies for mitigating this tradeoff and work with models that do a very good job accomplishing that goal.
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In this module, you'll explore unsupervised learning, a machine-learning approach that doesn't require labeled data. Instead of using correct answers, these algorithms identify patterns in data based on similarity. These patterns form clusters in an N-dimensional feature space, where data points that are close together can be considered clusters. Clusters may have a hierarchical structure, similar to natural systems such as galaxies or biological taxonomies. You'll learn about clustering algorithms and how unsupervised learning can reduce features for other modeling tasks, using Python to implement various clustering and dimensionality reduction techniques.
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In this module, you will learn how to evaluate the performance of supervised machine learning models using various metrics, depending on whether you are building classification or regression models. You will explore hyperparameter tuning techniques like cross-validation to prevent overfitting and ensure an unbiased model evaluation. Additionally, you will learn about regularization techniques for linear regression to mitigate overfitting caused by noise and outliers. Finally, you will gain hands-on experience in building, fine-tuning, and evaluating models using these techniques.
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This module focuses on applying and demonstrating the skills you have gained throughout the course by completing a comprehensive final assignment. In this assignment, you will analyze historical rainfall data to develop and optimize a classification model. You will perform feature engineering, evaluate the model's performance using different classifiers, and summarize your findings through visualizations. Once completed, your assignment will be graded automatically by an AI grading tool in the next section.
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We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Korea Advanced Institute of Science and Technology(KAIST)
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University of Michigan
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Korea Advanced Institute of Science and Technology(KAIST)
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DeepLearning.AI
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Reviewed on May 25, 2020
Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!
Reviewed on Oct 8, 2020
I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.
Reviewed on Jan 14, 2025
good course , some part is typical more statistical part shown, even i have good understanding of ML , so new learner will find little typical. rest tutor voice and language is understandable.
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Python’s popularity in machine learning stems from its simplicity, readability, and extensive libraries like TensorFlow, PyTorch, and scikit-learn, which streamline complex ML tasks. Its active community and ease of integration with other languages and tools also make Python an ideal choice for ML.
Machine learning engineers use Python to develop algorithms, preprocess data, train models, and analyze results. With Python’s rich libraries and frameworks, they can experiment with various models, optimize performance, and deploy applications efficiently.
Python offers a wide range of ML libraries, is beginner-friendly, and has great support for data visualization and model interpretation. It also supports rapid prototyping, making it easier to test and refine models compared to other languages like C++ or Java.
This course is designed for aspiring and current machine learning practitioners who want to build foundational skills in Python-based machine learning, from data preparation and model development to evaluation and optimization.
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