Machine Learning Specialization by DeepLearning.AI
Key Takeaways
The Machine Learning Specialization by DeepLearning.AI and Stanford Online covers the fundamentals of machine learning, including supervised and unsupervised learning, neural networks, and decision trees, with a focus on practical skills using Python.
Full Transcript
Hi there. I'm thrilled to announce my new machine learning specialization created by deeplearning.ai and Stanford online is now available on Coursera. In this beginner-friendly program, you learn the foundations of how machine learning models work and also gain practical skills for building and training systems using Python. You also practice the ideas you learn by implementing the learning algorithms and seeing them work for yourself. When you finish the specialization, you'll be among that rare select group of people able to build effective learning algorithms and get them to work on important applications. This program does not assume much math background or deep coding experience. Unlike the original course, which had assumed much more math background, I've redesigned the specialization to make sure that you can gain a deep understanding of machine learning and really understand how it works without getting into as much tricky math. We'll start this lesson with an intuitive explanation of a machine learning concept like supervised learning or neural networks of deep learning or decision trees or unsupervised learning, and then we'll go through a step-by-step explanation of how it works and then finally go into coding to make sure that you can make these algorithms work for yourself. Over the years, I've been inspired by the stories of the many learners that had taken my optional course and gone on to build new and exciting careers in machine learning. I'm also proud of the community that has formed as generations of learners lifted each other up. It is now time to empower the next generation of learners. So, please help me spread the word about the new machine learning specialization and encourage others to take machine learning. And if you're interested in breaking into AI yourself, this is a great starting point. I hope you'll enroll today and I look forward to supporting your learning about machine learning, and to seeing you do great things with machine learning.
Original Description
Enroll in the Machine Learning Specialization 👉 https://bit.ly/3ZRBXpq
This 3-course specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. It's a beginner-friendly program that will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
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.
The specialization 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)
Best practices for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
Learn more: https://bit.ly/3ZRBXpq
DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community.
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Forward and Backward Propagation (C1W4L06)
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deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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Using an Appropriate Scale (C2W3L02)
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Gradient Checking (C2W1L13)
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Gradient Checking Implementation Notes (C2W1L14)
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Learning Rate Decay (C2W2L09)
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Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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Mini Batch Gradient Descent (C2W2L01)
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The Problem of Local Optima (C2W3L10)
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Exponentially Weighted Averages (C2W2L03)
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Tuning Process (C2W3L01)
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Understanding Exponentially Weighted Averages (C2W2L04)
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Bias Correction of Exponentially Weighted Averages (C2W2L05)
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Gradient Descent With Momentum (C2W2L06)
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Normalizing Activations in a Network (C2W3L04)
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Hyperparameter Tuning in Practice (C2W3L03)
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Adam Optimization Algorithm (C2W2L08)
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RMSProp (C2W2L07)
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Fitting Batch Norm Into Neural Networks (C2W3L05)
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Why Does Batch Norm Work? (C2W3L06)
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Batch Norm At Test Time (C2W3L07)
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Softmax Regression (C2W3L08)
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Deep Learning Frameworks (C2W3L10)
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Neural Network Overview (C1W3L01)
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Training Softmax Classifier (C2W3L09)
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Why Deep Representations? (C1W4L04)
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Gradient Descent For Neural Networks (C1W3L09)
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Neural Network Representations (C1W3L02)
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TensorFlow (C2W3L11)
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Activation Functions (C1W3L06)
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Explanation For Vectorized Implementation (C1W3L05)
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Getting Matrix Dimensions Right (C1W4L03)
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Understanding Dropout (C2W1L07)
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Building Blocks of a Deep Neural Network (C1W4L05)
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Why Non-linear Activation Functions (C1W3L07)
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Computing Neural Network Output (C1W3L03)
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Backpropagation Intuition (C1W3L10)
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Train/Dev/Test Sets (C2W1L01)
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Deep L-Layer Neural Network (C1W4L01)
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Random Initialization (C1W3L11)
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Other Regularization Methods (C2W1L08)
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Normalizing Inputs (C2W1L09)
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Derivatives Of Activation Functions (C1W3L08)
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Parameters vs Hyperparameters (C1W4L07)
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Vectorizing Across Multiple Examples (C1W3L04)
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What does this have to do with the brain? (C1W4L08)
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Dropout Regularization (C2W1L06)
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Vanishing/Exploding Gradients (C2W1L10)
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Basic Recipe for Machine Learning (C2W1L03)
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Bias/Variance (C2W1L02)
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Forward Propagation in a Deep Network (C1W4L02)
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Weight Initialization in a Deep Network (C2W1L11)
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Numerical Approximations of Gradients (C2W1L12)
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Regularization (C2W1L04)
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Why Regularization Reduces Overfitting (C2W1L05)
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More on: Supervised Learning
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