Machine Learning [FULL COURSE]

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Machine Learning [FULL COURSE]

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you\'ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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Syllabus:

Aula #1 - Lecture 1.1 — Introduction What Is Machine Learning — [ Machine Learning | Andrew Ng ]
Aula #2 - Lecture 1.2 — Introduction Supervised Learning — [ Machine Learning | Andrew Ng ]
Aula #3 - Lecture 1.3 — Introduction Unsupervised Learning — [ Machine Learning | Andrew Ng]
Aula #4 - Lecture 2.1 — Linear Regression With One Variable | Model Representation — Andrew Ng
Aula #5 - Lecture 2.2 — Linear Regression With One Variable | CostFunction — Andrew Ng
Aula #6 - Lecture 2.3 — Linear Regression With One Variable | Cost Function Intuition #1 | Andrew Ng
Aula #7 - Lecture 2.4 — Linear Regression With One Variable | Cost Function Intuition #2 | Andrew Ng
Aula #8 - Lecture 2.5 — Linear Regression With One Variable | Gradient Descent — [ Andrew Ng]
Aula #9 - Lecture 2.6 — Linear Regression With One Variable | Gradient Descent Intuition — [ Andrew Ng]
Aula #10 - Lecture 2.7 — Linear Regression With One Variable | Gradient Descent For Linear Regression
Aula #11 - Lecture 2.8 — What\'s Next — [ Machine Learning | Andrew Ng | Stanford University]
Aula #12 - Lecture 3.1 — Linear Algebra Review | Matrices And Vectors — [ Machine Learning | Andrew Ng]
Aula #13 - Lecture 3.2 — Linear Algebra Review | Addition And Scalar Multiplication — [Andrew Ng]
Aula #14 - Lecture 3.3 — Linear Algebra Review | Matrix Vector Multiplication — [ Machine Learning | Andrew Ng]
Aula #15 - Lecture 3.4 — Linear Algebra Review | Matrix-Matrix Multiplication — [ Andrew Ng ]
Aula #16 - Lecture 3.5 — Linear Algebra Review | Matrix Multiplication Properties — [ Andrew Ng ]
Aula #17 - Lecture 3.6 — Linear Algebra Review | Inverse And Transpose — [ Machine Learning | Andrew Ng]
Aula #18 - Lecture 4.1 — Linear Regression With Multiple Variables - (Multiple Features) — [ Andrew Ng]
Aula #19 - Lecture 4.2 — Linear Regression With Multiple Variables -- (Gradient Descent For Multiple Variables)
Aula #20 - Lecture 4.3 — Linear Regression With Multiple Variables | Gradient In PracticeaI Feature Scaling
Aula #21 - Lecture 4.4 — Linear Regression With Multiple Variables | Gradient In PracticeaI | Learning Rate
Aula #22 - Lecture 4.5 — Linear Regression With Multiple Variables | Features And Polynomial Regression
Aula #23 - Lecture 4.7 — Linear Regression With Multiple Variables | Normal Equation Non Invertibility
Aula #24 - Lecture 4.6 — Linear Regression With Multiple Variables | Normal Equation — [ Andrew Ng]
Aula #25 - Lecture 5.1 — Octave Tutorial || Basic Operations — [ Machine Learning | Andrew Ng]
Aula #26 - Lecture 5.2 — Octave Tutorial || Moving Data Around — [ Machine Learning | Andrew Ng]
Aula #27 - Lecture 5.4 — Octave Tutorial || Plotting Data — [ Machine Learning | Andrew Ng]
Aula #28 - Lecture 5.5 — Octave Tutorial || While If Statements And Functions — [ Andrew Ng ]
Aula #29 - Lecture 5.6 — Octave Tutorial || Vectorization — [ Machine Learning | Andrew Ng]
Aula #30 - Lecture 5.7 — Octave Tutorial || Programming Exercises — [ Machine Learning | Andrew Ng]
Aula #31 - Lecture 5.3 — Octave Tutorial || Computing On Data — [ Machine Learning | Andrew Ng]
Aula #32 - Lecture 6.1 — Logistic Regression | Classification — — [ Machine Learning | Andrew Ng]
Aula #33 - Lecture 6.2 — Logistic Regression | Hypothesis Representation — [ Machine Learning | Andrew Ng]
Aula #34 - Lecture 6.3 — Logistic Regression | Decision Boundary — [ Machine Learning | Andrew Ng]
Aula #35 - Lecture 6.4 — Logistic Regression | Cost Function — [ Machine Learning | Andrew Ng]
Aula #36 - Lecture 6.5 — Logistic Regression | Simplified Cost Function And Gradient Descent — [ Andrew Ng]
Aula #37 - Lecture 6.6 — Logistic Regression | Advanced Optimization — [ Machine Learning | Andrew Ng]
Aula #38 - Lecture 6.7 — Logistic Regression | MultiClass Classification OneVsAll — [Andrew Ng]
Aula #39 - Lecture 7.1 — Regularization | The Problem Of Overfitting — [ Machine Learning | Andrew Ng]
Aula #40 - Lecture 7.2 — Regularization | Cost Function — [ Machine Learning | Andrew Ng | Stanford University]
Aula #41 - Lecture 7.3 — Regularization | Regularized Linear Regression — [ Machine Learning | Andrew Ng]
Aula #42 - Lecture 7.4 — Regularization | Regularized Logistic Regression — [ Machine Learning | Andrew Ng]
Aula #43 - Lecture 8.1 — Neural Networks Representation | Non Linear Hypotheses — [Andrew Ng]
Aula #44 - Lecture 8.2 — Neural Networks Representation | Neurons And The Brain — [Andrew Ng]
Aula #45 - Lecture 8.3 — Neural Networks Representation | Model Representation-I — [ Andrew Ng ]
Aula #46 - Lecture 8.4 — Neural Networks Representation | Model Representation-II — [Andrew Ng]
Aula #47 - Lecture 8.5 — Neural Networks Representation | Examples And Intuitions-I — [ Andrew Ng]
Aula #48 - Lecture 8.6 — Neural Networks Representation | Examples And Intuitions-II — [ Andrew Ng]
Aula #49 - Lecture 8.7 — Neural Networks Representation | MultiClass Classification — [Andrew Ng]
Aula #50 - Lecture 9.1 — Neural Networks Learning |
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Modules & lessons

Module 1: Machine Learning [FULL COURSE]

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Lesson 1.1 : Introdução, Fonte e Autoria

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Lesson 1.2 : Lecture 1.1 — Introduction What Is Machine Learning — [ Machine Learning | Andrew Ng ]

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Lesson 1.3 : Lecture 1.2 — Introduction Supervised Learning — [ Machine Learning | Andrew Ng ]

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Lesson 1.4 : Lecture 1.3 — Introduction Unsupervised Learning — [ Machine Learning | Andrew Ng]

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Lesson 1.5 : Lecture 2.1 — Linear Regression With One Variable | Model Representation — Andrew Ng

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Lesson 1.6 : Lecture 2.2 — Linear Regression With One Variable | CostFunction — Andrew Ng

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Lesson 1.7 : Lecture 2.3 — Linear Regression With One Variable | Cost Function Intuition #1 | Andrew Ng

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Lesson 1.8 : Lecture 2.4 — Linear Regression With One Variable | Cost Function Intuition #2 | Andrew Ng

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Lesson 1.9 : Lecture 2.5 — Linear Regression With One Variable | Gradient Descent — [ Andrew Ng]

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Lesson 1.10 : Lecture 2.6 — Linear Regression With One Variable | Gradient Descent Intuition — [ Andrew Ng]

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Lesson 1.11 : Lecture 2.7 — Linear Regression With One Variable | Gradient Descent For Linear Regression

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Lesson 1.12 : Lecture 2.8 — What's Next — [ Machine Learning | Andrew Ng | Stanford University]

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Lesson 1.13 : Lecture 3.1 — Linear Algebra Review | Matrices And Vectors — [ Machine Learning | Andrew Ng]

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Lesson 1.14 : Lecture 3.2 — Linear Algebra Review | Addition And Scalar Multiplication — [Andrew Ng]

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Lesson 1.15 : Lecture 3.3 — Linear Algebra Review | Matrix Vector Multiplication — [ Machine Learning | Andrew Ng]

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Lesson 1.16 : Lecture 3.4 — Linear Algebra Review | Matrix-Matrix Multiplication — [ Andrew Ng ]

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Lesson 1.17 : Lecture 3.5 — Linear Algebra Review | Matrix Multiplication Properties — [ Andrew Ng ]

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Lesson 1.18 : Lecture 3.6 — Linear Algebra Review | Inverse And Transpose — [ Machine Learning | Andrew Ng]

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Lesson 1.19 : Lecture 4.1 — Linear Regression With Multiple Variables - (Multiple Features) — [ Andrew Ng]

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Lesson 1.20 : Lecture 4.2 — Linear Regression With Multiple Variables -- (Gradient Descent For Multiple Variables)

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Lesson 1.21 : Lecture 4.3 — Linear Regression With Multiple Variables | Gradient In PracticeaI Feature Scaling

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Lesson 1.22 : Lecture 4.4 — Linear Regression With Multiple Variables | Gradient In PracticeaI | Learning Rate

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Lesson 1.23 : Lecture 4.5 — Linear Regression With Multiple Variables | Features And Polynomial Regression

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Lesson 1.24 : Lecture 4.7 — Linear Regression With Multiple Variables | Normal Equation Non Invertibility

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Lesson 1.25 : Lecture 4.6 — Linear Regression With Multiple Variables | Normal Equation — [ Andrew Ng]

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Lesson 1.26 : Lecture 5.1 — Octave Tutorial || Basic Operations — [ Machine Learning | Andrew Ng]

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Lesson 1.27 : Lecture 5.2 — Octave Tutorial || Moving Data Around — [ Machine Learning | Andrew Ng]

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Lesson 1.28 : Lecture 5.4 — Octave Tutorial || Plotting Data — [ Machine Learning | Andrew Ng]

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Lesson 1.29 : Lecture 5.5 — Octave Tutorial || While If Statements And Functions — [ Andrew Ng ]

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Lesson 1.30 : Lecture 5.6 — Octave Tutorial || Vectorization — [ Machine Learning | Andrew Ng]

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Lesson 1.31 : Lecture 5.7 — Octave Tutorial || Programming Exercises — [ Machine Learning | Andrew Ng]

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Lesson 1.32 : Lecture 5.3 — Octave Tutorial || Computing On Data — [ Machine Learning | Andrew Ng]

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Lesson 1.33 : Lecture 6.1 — Logistic Regression | Classification — — [ Machine Learning | Andrew Ng]

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Lesson 1.34 : Lecture 6.2 — Logistic Regression | Hypothesis Representation — [ Machine Learning | Andrew Ng]

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Lesson 1.35 : Lecture 6.3 — Logistic Regression | Decision Boundary — [ Machine Learning | Andrew Ng]

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Lesson 1.36 : Lecture 6.4 — Logistic Regression | Cost Function — [ Machine Learning | Andrew Ng]

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Lesson 1.37 : Lecture 6.5 — Logistic Regression | Simplified Cost Function And Gradient Descent — [ Andrew Ng]

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Lesson 1.38 : Lecture 6.6 — Logistic Regression | Advanced Optimization — [ Machine Learning | Andrew Ng]

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Lesson 1.39 : Lecture 6.7 — Logistic Regression | MultiClass Classification OneVsAll — [Andrew Ng]

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Lesson 1.40 : Lecture 7.1 — Regularization | The Problem Of Overfitting — [ Machine Learning | Andrew Ng]

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Lesson 1.41 : Lecture 7.2 — Regularization | Cost Function — [ Machine Learning | Andrew Ng | Stanford University]

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Lesson 1.42 : Lecture 7.3 — Regularization | Regularized Linear Regression — [ Machine Learning | Andrew Ng]

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Lesson 1.43 : Lecture 7.4 — Regularization | Regularized Logistic Regression — [ Machine Learning | Andrew Ng]

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Lesson 1.44 : Lecture 8.1 — Neural Networks Representation | Non Linear Hypotheses — [Andrew Ng]

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Lesson 1.45 : Lecture 8.2 — Neural Networks Representation | Neurons And The Brain — [Andrew Ng]

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Lesson 1.46 : Lecture 8.3 — Neural Networks Representation | Model Representation-I — [ Andrew Ng ]

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Lesson 1.47 : Lecture 8.4 — Neural Networks Representation | Model Representation-II — [Andrew Ng]

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Lesson 1.48 : Lecture 8.5 — Neural Networks Representation | Examples And Intuitions-I — [ Andrew Ng]

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Lesson 1.49 : Lecture 8.6 — Neural Networks Representation | Examples And Intuitions-II — [ Andrew Ng]

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Lesson 1.50 : Lecture 8.7 — Neural Networks Representation | MultiClass Classification — [Andrew Ng]

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Lesson 1.51 : Lecture 9.1 — Neural Networks Learning | Cost Function — [ Machine Learning | Andrew Ng]

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Lesson 1.52 : Lecture 9.2 — Neural Networks Learning | Backpropagation Algorithm — [ Machine Learning | Andrew Ng]

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Lesson 1.53 : Lecture 9.3 — Neural Networks Learning | Backpropagation Intuition — [ Machine Learning | Andrew Ng]

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Lesson 1.54 : Lecture 9.4 — Neural Networks Learning | Implementation Note Unrolling Parameters — [ Andrew Ng]

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Lesson 1.55 : Lecture 9.5 — Neural Networks Learning | Gradient Checking — [ Machine Learning | Andrew Ng]

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Lesson 1.56 : Lecture 9.6 — Neural Networks Learning | Random Initialization — [ Machine Learning | Andrew Ng]

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Lesson 1.57 : Lecture 9.7 — Neural Networks Learning | Putting It Together — [ Machine Learning | Andrew Ng]

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Lesson 1.58 : Lecture 9.8 — Neural Networks Learning | Autonomous Driving Example — [Andrew Ng]

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Lesson 1.59 : Lecture 10.1 — Advice For Applying Machine Learning | Deciding What To Try Next — [ Andrew Ng]

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Lesson 1.60 : Lecture 10.2 — Advice For Applying Machine Learning | Evaluating A Hypothesis — [ Andrew Ng]

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Lesson 1.61 : Lecture 10.3 — Advice For Applying Machine Learning | Model Selection And Train Validation Test Sets

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Lesson 1.62 : Lecture 10.4 — Advice For Applying Machine Learning | Diagnosing Bias Vs Variance — [Andrew Ng]

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Lesson 1.63 : Lecture 10.5 — Advice For Applying Machine Learning | Regularization And Bias Variance — [Andrew Ng]

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Lesson 1.64 : Lecture 10.6 — Advice For Applying Machine Learning | Learning Curves — [Andrew Ng]

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Lesson 1.65 : Lecture 10.7 — Advice For Applying Machine Learning | Deciding What To Do Next (Revisited)

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Lesson 1.66 : Lecture 11.1 — Machine Learning System Design | Prioritizing What To Work On — [ Andrew Ng]

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Lesson 1.67 : Lecture 11.2 — Machine Learning System Design | Error Analysis — [ Machine Learning | Andrew Ng ]

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Lesson 1.68 : Lecture 11.3 — Machine Learning System Design | Error Metrics For Skewed Classes — [Andrew Ng]

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Lesson 1.69 : Lecture 11.4 — Machine Learning System Design | Trading Off Precision And Recall — [Andrew Ng]

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Lesson 1.70 : Lecture 11.5 — Machine Learning System Design | Data For Machine Learning — [Andrew Ng]

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Lesson 1.71 : Lecture 12.1 — Support Vector Machines | Optimization Objective — [ Machine Learning | Andrew Ng]

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Lesson 1.72 : Lecture 12.2 — Support Vector Machines | Large Margin Intuition — [Machine Learning | Andrew Ng]

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Lesson 1.73 : Lecture 12.3 — Support Vector Machines | Mathematics Behind Large Margin Classification (Optional)

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Lesson 1.74 : Lecture 12.4 — Support Vector Machines | (Kernels-I) — [ Machine Learning | Andrew Ng]

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Lesson 1.75 : Lecture 12.5 — Support Vector Machines | (Kernels-II) — [Machine Learning | Andrew Ng]

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Lesson 1.76 : Lecture 12.6 — Support Vector Machines | Using An SVM — [ Machine Learning | Andrew Ng]

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Lesson 1.77 : Lecture 13.1 — Clustering | Unsupervised Learning | Introduction — [ Andrew Ng ]

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Lesson 1.78 : Lecture 13.2 — Clustering | KMeans Algorithm — [ Machine Learning | Andrew Ng ]

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Lesson 1.79 : Lecture 13.3 — Clustering | Optimization Objective — [ Machine Learning | Andrew Ng ]

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Lesson 1.80 : Lecture 13.4 — Clustering | Random Initialization — [ Machine Learning | Andrew Ng ]

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Lesson 1.81 : Lecture 13.5 — Clustering | Choosing The Number Of Clusters — [ Machine Learning | Andrew Ng ]

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Lesson 1.82 : Lecture 14.1 — Dimensionality Reduction Motivation I | Data Compression — [ Andrew Ng ]

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Lesson 1.83 : Lecture 14.2 —Dimensionality Reduction Motivation II | Visualization — [ Andrew Ng ]

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Lesson 1.84 : Lecture 14.3 — Dimensionality Reduction | Principal Component Analysis | Problem Formulation

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Lesson 1.85 : Lecture 14.4 — Dimensionality Reduction | Principal Component Analysis Algorithm — [ Andrew Ng ]

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Lesson 1.86 : Lecture 14.5 — Dimensionality Reduction | Choosing The Number Of Principal Components

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Lesson 1.87 : Lecture 14.6 — Dimensionality Reduction | Reconstruction From Compressed Representation

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Lesson 1.88 : Lecture 14.7 — Dimensionality Reduction | Advice For Applying PCA — [ Machine Learning | Andrew Ng ]

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Lesson 1.89 : Lecture 15.1 — Anomaly Detection Problem | Motivation — [ Machine Learning | Andrew Ng ]

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Lesson 1.90 : Lecture 15.2 — Anomaly Detection | Gaussian Distribution — [ Machine Learning | Andrew Ng ]

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Lesson 1.91 : Lecture 15.3 — Anomaly Detection Algorithm — [ Machine Learning | Andrew Ng | Stanford University ]

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Lesson 1.92 : Lecture 15.4 — Anomaly Detection | Developing And Evaluating An Anomaly Detection System

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Lesson 1.93 : Lecture 15.5 — Anomaly Detection | Anomaly Detection Vs Supervised Learning — [ Andrew Ng ]

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Lesson 1.94 : Lecture 15.6 — Anomaly Detection | Choosing What Features To Use — [ Andrew Ng ]

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Lesson 1.95 : Lecture 15.7 — Anomaly Detection | Multivariate Gaussian Distribution — [ Andrew Ng ]

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Lesson 1.96 : Lecture 15.8 — Anomaly Detection | Anomaly Detection Using The Multivariate Gaussian Distribution

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Lesson 1.97 : Lecture 16.1 — Recommender Systems | Problem Formulation — [ Machine Learning | Andrew Ng ]

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Lesson 1.98 : Lecture 16.2 — Recommender Systems | Content Based Recommendations — [ Andrew Ng ]

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Lesson 1.99 : Lecture 16.3 — Recommender Systems | Collaborative Filtering — [ Machine Learning | Andrew Ng ]

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Lesson 1.100 : Lecture 16.4 — Recommender Systems | Collaborative Filtering Algorithm — [ Andrew Ng ]

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Lesson 1.101 : Lecture 16.5 — Recommender Systems | Vectorization Low Rank Matrix Factorization — [ Andrew Ng ]

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Lesson 1.102 : Lecture 16.6 — Recommender Systems | Implementational Detail Mean Normalization — [ Andrew Ng ]

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Lesson 1.103 : Lecture 17.1 — Large Scale Machine Learning | Learning With Large Datasets — [ Andrew Ng ]

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Lesson 1.104 : Lecture 17.2 — Large Scale Machine Learning | Stochastic Gradient Descent — [ Andrew Ng ]

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Lesson 1.105 : Lecture 17.3 — Large Scale Machine Learning | Mini Batch Gradient Descent — [ Andrew Ng ]

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Lesson 1.106 : Lecture 17.4 — Large Scale Machine Learning | Stochastic Gradient Descent Convergence

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Lesson 1.107 : Lecture 17.5 — Large Scale Machine Learning | Online Learning — [ Machine Learning | Andrew Ng ]

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Lesson 1.108 : Lecture 17.6 — Large Scale Machine Learning | Map Reduce And Data Parallelism — [ Andrew Ng ]

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Lesson 1.109 : Lecture 18.1 — Application Example Photo OCR | Problem Description And Pipeline — [ Andrew Ng ]

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Lesson 1.110 : Lecture 18.2 — Application Example Photo OCR | Sliding Windows — [ Machine Learning | Andrew Ng ]

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Lesson 1.111 : Lecture 18.3 — Application Example Photo OCR | Getting Lots Of Data Artificial Data Synthesis

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Lesson 1.112 : Lecture 18.4 — Application Example Photo OCR | Ceiling Analysis | What Part to Work On Next

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Lesson 1.113 : Lecture 19 — Conclusion Summary And ThankYou — [ Andrew Ng | Stanford University ]

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