Machine Learning [FULL COURSE]

5 (1 reviews)

REGISTER: Sign me up

Categories Free courses Featured courses Courses on sale All courses
I want to learn I want to teach Earn Money!
BR - USD ($)

free course

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.

Saiba mais.

Start now for free!

This course is available to subscribers! Get to know the plans.

Digital or printed certificate available for individual purchase upon completion

workload

19h

premium access

7 days

price

Free Course

created on

04/12/2017

Complete course
with certificate!

On completion of this course, you can purchase a separate digital certificate for download at a cost of $ 3,74

Increase your chances of being hired by qualifying your skills with certificates.

See More
View Certificate
Why choose Learncafe?

Here at Learncafe you can access your course whenever you want. Courses are available 24 hours a day, every day of the week.

Além de ser uma plataforma fácil de usar, em qualquer aparelho com acesso à internet, você ainda poderá adquirir um certificado digital para download ao concluir o seu curso, ao custo de $ 3,74

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 |
+ Show more

Get to know
the person in charge:

Learncafe in English

Learning is never too much. We create this profile so you can access various free courses. From the available material, you can acquire new knowledge on topics such as: education, health, among other areas. All works and materials have the rights reserved to their respective authors.

view profile see courses
0 people enrolled in this course.

General evaluation of the course:

Modules & lessons

Module 1: Machine Learning [FULL COURSE]

Creating a class flash text

Lesson 1.1 : Introdução, Fonte e Autoria

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

Creating a class flash video

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

view all

Student evaluation :

Average rating

5

100.00%

Choose a plan:

Free Plan Free
  • 19 CLASS HOURS
  • 30 free access days*
  • Grace period of 120 days**
  • Certificate of completion***
  • Immediate access
Sign up now
* O prazo de acesso ao conteúdo do curso é válido durante a vigência da mensalidade ou compra avulsa.
** O prazo de carência refere-se ao tempo total de espera para que um usuário possa iniciar um outro curso na plataforma.
*** O certificado de conclusão do curso é oferecido separadamente para compra avulsa em dois formatos: digital para download e impresso via Correios.

Start studying in just a few clicks:

The knowledge you've been looking for to improve your
career is just a few clicks away!

Sign up now

Sign up for Learncafe

+200.000 study hours
+300.000 videos available
+30.000 fixation exercises
+9.000 available courses
Ei, espera! Que tal você concorrer a 20 mil?
Comprar e participar
Não, não quero concorrer a R$ 20 MIL.