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<!DOCTYPE html>
<html lang="en" >
<head>
<meta charset="UTF-8">
<title>Supervised Machine Learning: Regression and Classification</title>
<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.5.0/css/bootstrap.min.css'>
<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.2/css/all.min.css'><link rel="stylesheet" href="./style.css">
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<body>
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<div class="d-flex justify-content-center align-items-center flex-column">
<h1 class="mt-5 mb-4 text-center">Supervised Machine Learning: Regression and Classification</h1>
<p class="w-75 text-white text-justify mb-4">
In the first course of the Machine Learning Specialization, you will: <br>
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<br>
<!-- <li> -->
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. <br>
<!-- </li> -->
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• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression. <br>
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<br>
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. <br>
<br>
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. <br>
<br>
This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. <br>
<br>
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) <br>
<br>
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. <br>
<div>
<a class="btn btn-primary" href="https://www.coursera.org/learn/machine-learning" target="_blank" rel="noopener noreferrer" role="button">Enroll</a>
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<div class="step one" id="header1">
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<h4>WEEK 1<br />Introduction to Machine Learning</h4>
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<article data-step="1">
<header class="d-flex align-items-center">
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<h6>Welcome to machine learning!</h6>
</header>
<div class="body">
<div>
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<a href="https://www.coursera.org/lecture/machine-learning/welcome-to-machine-learning-iYR2y" target="_blank" rel="noopener noreferrer">
Introduction to machine learning
</a>
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<!-- <a href="https://www.coursera.org/lecture/machine-learning/welcome-to-machine-learning-iYR2y">Start</a> -->
<div>
<a href="https://www.coursera.org/learn/machine-learning/lecture/IjrpM/applications-of-machine-learning" target="_blank" rel="noopener noreferrer">
Applications of machine learning
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<article data-step="2">
<header class="d-flex align-items-center ">
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<h6>Supervised vs Unsupervised learning</h6>
</header>
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<div>
<a href="https://www.coursera.org/learn/machine-learning/lecture/PNeuX/what-is-machine-learning" target="_blank" rel="noopener noreferrer">
What is machine learning?
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<div>
<a href="https://www.coursera.org/learn/machine-learning/lecture/s91wX/supervised-learning-part-1" target="_blank" rel="noopener noreferrer">
Supervised learning part 1
</a>
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<div>
<a href="https://www.coursera.org/learn/machine-learning/lecture/Q8Vvp/supervised-learning-part-2" target="_blank" rel="noopener noreferrer">
Supervised learning part 2
</a>
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<div>
<a href="https://www.coursera.org/learn/machine-learning/lecture/TxO6F/unsupervised-learning-part-1" target="_blank" rel="noopener noreferrer">
Unsupervised learning part 1
</a>
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<div>
<a href="https://www.coursera.org/learn/machine-learning/lecture/jKBHE/unsupervised-learning-part-2" target="_blank" rel="noopener noreferrer">
Unsupervised learning part 2
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<div>
<a href="https://www.coursera.org/learn/machine-learning/lecture/lwqzq/jupyter-notebooks" target="_blank" rel="noopener noreferrer">
Jupyter Notebooks
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Practice quiz: Supervised vs Unsupervised learning
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<h6>Regression Model</h6>
</header>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/1ACA2/linear-regression-model-part-1" target="_blank" rel="noopener noreferrer">
Linear regression model part 1
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<a href="https://www.coursera.org/learn/machine-learning/lecture/nucNi/linear-regression-model-part-2" target="_blank" rel="noopener noreferrer">
Linear regression model part 2
</a>
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<a href="https://www.coursera.org/learn/machine-learning/ungradedLab/PhN1X/optional-lab-model-representation" target="_blank" rel="noopener noreferrer">
Optional lab: Model representation
</a>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/1Z0TT/cost-function-formula" target="_blank" rel="noopener noreferrer">
Cost function formula
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<a href="https://www.coursera.org/learn/machine-learning/lecture/FthLz/cost-function-intuition" target="_blank" rel="noopener noreferrer">
Cost function intuition
</a>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/QI1h6/visualizing-the-cost-function" target="_blank" rel="noopener noreferrer">
Visualizing the cost function
</a>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/Ov8Zt/visualization-examples" target="_blank" rel="noopener noreferrer">
Visualization examples
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<a href="https://www.coursera.org/learn/machine-learning/ungradedLab/udPHh/optional-lab-cost-function" target="_blank" rel="noopener noreferrer">
Optional lab: Cost function
</a>
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<a href="https://www.coursera.org/learn/machine-learning/exam/TNvjK/practice-quiz-regression" target="_blank" rel="noopener noreferrer">
Practice quiz: Regression
</a>
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<h6>Train the model with gradient descent</h6>
</header>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/2f2PA/gradient-descent" target="_blank" rel="noopener noreferrer">
Gradient descent
</a>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/TXDBu/implementing-gradient-descent" target="_blank" rel="noopener noreferrer">
Implementing gradient descent
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<a href="https://www.coursera.org/learn/machine-learning/lecture/2EoN6/gradient-descent-intuition" target="_blank" rel="noopener noreferrer">
Gradient descent intuition
</a>
<input type="checkbox" id="option21"> <br>
<a href="https://www.coursera.org/learn/machine-learning/lecture/OoP3Y/learning-rate" target="_blank" rel="noopener noreferrer">
Learning rate
</a>
<input type="checkbox" id="option22"> <br>
<a href="https://www.coursera.org/learn/machine-learning/lecture/lgSMj/gradient-descent-for-linear-regression" target="_blank" rel="noopener noreferrer">
Gradient descent for linear regression
</a>
<input type="checkbox" id="option23"> <br>
<a href="https://www.coursera.org/learn/machine-learning/lecture/349Ay/running-gradient-descent" target="_blank" rel="noopener noreferrer">
Running gradient descent
</a>
<input type="checkbox" id="option24"> <br>
<a href="https://www.coursera.org/learn/machine-learning/ungradedLab/lE1al/optional-lab-gradient-descent" target="_blank" rel="noopener noreferrer">
Optional lab: Gradient Descent
</a>
<input type="checkbox" id="option25"> <br>
<a href="https://www.coursera.org/learn/machine-learning/exam/sWDb5/practice-quiz-train-the-model-with-gradient-descent" target="_blank" rel="noopener noreferrer">
Practice quiz: Train the model with gradient descent
</a>
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<h4>WEEK 2<br />Regression with multiple input variables</h4>
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<article data-step="5">
<header class="d-flex align-items-center text-success">
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<h6>Multiple linear regression</h6>
</header>
<div class="body">
<a href="https://www.coursera.org/learn/machine-learning/lecture/gFuSx/multiple-features" target="_blank" rel="noopener noreferrer">
Multiple features
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<a href="https://www.coursera.org/learn/machine-learning/lecture/ismjc/vectorization-part-1" target="_blank" rel="noopener noreferrer">
Vectorization part 1
</a>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/p2Nqv/vectorization-part-2" target="_blank" rel="noopener noreferrer">
Vectorization part 2
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<a href="https://www.coursera.org/learn/machine-learning/ungradedLab/zadmO/optional-lab-python-numpy-and-vectorization" target="_blank" rel="noopener noreferrer">
Optional lab: Python, NumPy and Vectorization
</a>
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<a href="https://www.coursera.org/learn/machine-learning/lecture/ltMMp/gradient-descent-for-multiple-linear-regression" target="_blank" rel="noopener noreferrer">
Gradient descent for multiple linear regression
</a>
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<a href="https://www.coursera.org/learn/machine-learning/ungradedLab/7GEJh/optional-lab-multiple-linear-regression" target="_blank" rel="noopener noreferrer">
Optional lab: Multiple linear regression
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<h6>Gradient descent in practice</h6>
</header>
<div class="body">
<a href="https://www.coursera.org/learn/machine-learning/lecture/KMDV3/feature-scaling-part-1" target="_blank" rel="noopener noreferrer">
Feature scaling part 1
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Feature scaling part 2
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<a href="https://www.coursera.org/learn/machine-learning/lecture/rOTkB/checking-gradient-descent-for-convergence" target="_blank" rel="noopener noreferrer">
Checking grading descent for convergence
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Choosing the learning rate
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<a href="https://www.coursera.org/learn/machine-learning/ungradedLab/kIf25/optional-lab-feature-scaling-and-learning-rate" target="_blank" rel="noopener noreferrer">
Optional lab: Feature scaling and learning rate
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<a href="https://www.coursera.org/learn/machine-learning/lecture/dgZYR/feature-engineering" target="_blank" rel="noopener noreferrer">
Feature engineering
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<a href="https://www.coursera.org/learn/machine-learning/lecture/OnGhN/polynomial-regression" target="_blank" rel="noopener noreferrer">
Polynomial regression
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<a href="https://www.coursera.org/learn/machine-learning/ungradedLab/Xat0X/optional-lab-feature-engineering-and-polynomial-regression" target="_blank" rel="noopener noreferrer">
Optional lab: Feature engineering and polynomial regression
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Optional lab: linear regression with scikit-learn
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<h6>Practice quiz: Gradient descent in practice</h6>
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Start
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<h6>Practice lab: Linear regression</h6>
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Start
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<h4 class="header4">WEEK 3<br />Classification</h4>
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<h6 class="header3">Classification with logistic regression</h6>
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Motivations
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Optional lab: Classification
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Logistic Regression
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Optional lab: Sigmoid function and logistic regression
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Optional lab: Feature scaling and learning rate
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Decision Boundary
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Optional lab: Decision Boundary
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Practice Quiz: Classification with logistic regression
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<h6 class="header3">Cost function for logistic regression</h6>
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Cost function for logistic regression
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Optional lab: Logistic Loss
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Simplified Cost function for logistic regression
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Optional lab: Cost function for logistic regression
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Practice Quiz: Cost function for logistic regression
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<h6 class="header3">Gradient descent for logistic regression</h6>
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Gradient descent implementation
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Optional lab: Gradient descent for logistic regression
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Optional lab: Logistic Regression with scikit-learn
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Practice Quiz: Gradient descent for logistic regression
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<h6 class="header3">The problem of overfitting</h6>
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The problem of overfitting
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Addressing overfitting
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Optional lab: Overfitting
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Cost function with regularization
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Regularized linear regularization
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Regularized logistic regularization
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Optional lab: Regularization
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Practice quiz: The problem of overfitting
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<h6 class="header3">PRACTICE LAB: Logistic Regression</h6>
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Start
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<h6>Acknowledgments</h6>
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<h6></h6>
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Acknowledgments
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