Este curso forma parte de Certificado profesional de Profesional en Inteligencia Artificial certificado por CertNexus

Anastas Stoyanovsky

Ofrecido Por

Certificado profesional de Profesional en Inteligencia Artificial certificado por CertNexusCertNexus

Acerca de este Curso

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In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business.
This third course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate introduces you to some of the major machine learning algorithms that are used to solve the two most common supervised problems: regression and classification, and one of the most common unsupervised problems: clustering. You'll build multiple models to address each of these problems using the machine learning workflow you learned about in the previous course.

Ultimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 3 de 5 en

Certificado profesional de Profesional en Inteligencia Artificial certificado por CertNexus

Nivel intermedio

ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.

Aprox. 20 horas para completar

Inglés (English)

Qué aprenderás

Train and evaluate linear regression models.
Train binary and multi-class classification models.
Evaluate and tune classification models to improve their performance.
Train and evaluate clustering models to find useful patterns in unsupervised data.

Habilidades que obtendrás

Machine Learning
clustering
classification
Linear Regression
Machine Learning (ML) Algorithms

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 3 de 5 en

Certificado profesional de Profesional en Inteligencia Artificial certificado por CertNexus

Nivel intermedio

ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.

Aprox. 20 horas para completar

Inglés (English)

Instructor

Anastas Stoyanovsky

Watson Senior Software Engineer, Software Architect at IBM

1105 alumnos

1 curso

Ofrecido por

CertNexus

Programa - Qué aprenderás en este curso

Semana1

Semana 1

2 horas para completar

Build Linear Regression Models Using Linear Algebra

2 horas para completar

13 videos (Total 32 minutos), 2 lecturas, 1 cuestionario

13 videos

Course Intro: Build Regression, Classification, and Clustering Models2mBuild Linear Regression Models Using Linear Algebra Module Introduction1mLinear Regression1mLinear Equation2mStraight Line Fit to Data Example1mLinear Regression in Machine Learning3mMatrices in Linear Regression4mNormal Equation5mAdvanced Linear Models2mCost Function1mMSE and MAE3mCoefficient of Determination1mNormal Equation Shortcomings43s

2 lecturas

Overview2mGuidelines for Building a Regression Model Using Linear Algebra10m

1 ejercicio de práctica

Building Linear Regression Models Using Linear Algebra30m

Semana2

Semana 2

3 horas para completar

Build Regularized and Iterative Linear Regression Models

3 horas para completar

8 videos (Total 14 minutos), 3 lecturas, 1 cuestionario

8 videos

Build Regularized and Iterative Linear Regression Models Module Introduction1mRegularization Techniques59sRidge Regression2mLasso Regression1mElastic Net Regression2mIterative Models1mGradient Descent3mGradient Descent Techniques1m

3 lecturas

Overview2mGuidelines for Building a Regularized Linear Regression Model5mGuidelines for Building an Iterative Linear Regression Model5m

1 ejercicio de práctica

Building Regularized and Iterative Linear Regression Models30m

Semana3

Semana 3

3 horas para completar

Train Classification Models

Besides linear regression, the other major type of supervised machine learning outcome is classification. To begin with, you'll train some binary classification models using a few different algorithms. Then, you'll train a model to handle cases in which there are multiple ways to classify a data example. Each algorithm may be ideal for solving a certain type of classification problem, so you need to be aware of how they differ.

3 horas para completar

9 videos (Total 13 minutos), 3 lecturas, 1 cuestionario

9 videos

Train Classification Models Module Introduction1mLinear Regression Shortcomings1mLogistic Regression1mDecision Boundary51sCost Function for Logistic Regression1mk-Nearest Neighbor (k-NN)2mLogistic Regression vs. k-NN35sMulti-Label and Multi-Class Classification1mMultinomial Logistic Regression2m

3 lecturas

Overview2mGuidelines for Training Binary Classification Models10mGuidelines for Training Multi-Class Classification Models3m

1 ejercicio de práctica

Training Classification Models30m

Semana4

Semana 4

3 horas para completar

Evaluate and Tune Classification Models

3 horas para completar

16 videos (Total 27 minutos), 3 lecturas, 1 cuestionario

16 videos

Evaluate and Tune Classification Models Module Introduction1mModel Performance1mConfusion Matrix1mClassifier Performance Measurement58sAccuracy1mPrecision1mRecall1mF₁ Score1mReceiver Operating Characteristic (ROC) Curve2mThresholds and AUC2mPrecision–Recall Curve (PRC)1mHyperparameter Optimization53sGrid Search2mRandomized Search1mBayesian Optimization2mGenetic Algorithms2m

3 lecturas

Overview2mGuidelines for Evaluating Classification Models10mGuidelines for Tuning Classification Models10m

1 ejercicio de práctica

Evaluating and Tuning Classification Models30m

Acerca de Certificado profesional de Profesional en Inteligencia Artificial certificado por CertNexus

The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demnstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP.

AI and ML have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. The specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam. Your journey to CAIP Certification 1) Complete the Coursera Certified Artificial Intelligence Practitioner Professional Certificate 2) Review the CAIP AIP Exam Blueprint 3) Purchase your CAIP Exam Voucher 4) Register for your CAIP Exam

Profesional en Inteligencia Artificial certificado por CertNexus

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
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Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Certificado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos del Certificado y te darán un certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.

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Build Regression, Classification, and Clustering Models

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás