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 |