Winkelwagen
U heeft geen artikelen in uw winkelwagen
Machine Learning Exploring Machine Learning
Exploring Machine Learning
€229,00

Goedkoper ergens anders?

Laat het ons weten!

+31367601019 [email protected]

Exploring Machine Learning

|
Klik om te vergroten
€229,00 Excl. btw
€277,09 Incl. btw
Op voorraad
|
Bestel voor 16:00 uur en start vandaag.
Je hebt nog counting... uur
  • Gecertificeerde docenten
  • Beste opleider 2019
  • Cursist staat centraal
Cursusinhoud/-informatie

Exploring Machine Learning E-learning

Bestel deze unieke Elearning cursus Exploring Machine Learning online, 1 jaar 24/ 7 toegang tot rijke interactieve video’s, spraak, voortgangsbewaking door rapportages en testen per hoofdstuk om de kennis direct te toetsen.

Cursusinhoud

Introduction to Machine Learning and Supervised Learning

Introducing Machine Learning

  • start the course
  • define machine learning and how it can be used to solve a variety of problems
  • define supervised machine learning
  • describe the fundamentals of building machine learning models to solve a problem
  • describe overfitting, how it can be a problem, and how to mitigate it
  • evaluate machine learning models and compare them

Simple Models

  • define the linear regression model for one and multiple variable problems
  • describe the gradient descent algorithm for training linear regression models
  • describe the k-nearest neighbor model and how to learn it
  • describe decision tree models and how to learn decision trees using the C4.5 algorithm

Machine Learning in Python

  • set up scikit-learn for Python
  • import data, and perform basic tasks with scikit-learn for Python
  • use scikit-learn to fit a linear regression model to a dataset
  • use scikit-learn's k-nearest neighbor model
  • use scikit-learn to fit a decision tree model to a dataset
  • use scikit-learn and GraphViz to generate a decision tree model from a dataset
  • use scikit-learn to calculate the precision and the recall of different machine learning models in Python

Practice: scikit-learn

  • fit a linear regression model to a dataset with scikit-learn and Python

Supervised Learning Models

Supervised Learning

  • start the course
  • describe the difference between classification and regression models and the use for each of them
  • describe how decision trees can be applied to regression problems
  • describe the CART decision tree learning algorithm and how it's different from C4.5
  • describe the random forests machine learning
  • use scikit-learn to build a random forest model in Python
  • describe the logistic regression model
  • use scikit-learn to fit a logistic regression model
  • describe support vector machine models
  • describe how to use kernel methods with support vector machines to model more complex data
  • use scikit-learn to train and support vector machines in Python
  • describe the Naïve Bayes classifiers and how to train them
  • use scikit-learn to fit a Naïve Bayes classifier in Python

Practice: Supervised Learning with Python

  • describe different supervised learning models in Python

Unsupervised Learning

Introducing Unsupervised Learning

  • start the course
  • describe unsupervised learning and some of the problems it can solve

Rule Association

  • describe rule association and how the apriori algorithm performs this task
  • use the apriori algorithm for rule association in Python

Cluster Analysis

  • describe clustering and the types of problems it applies to
  • describe the k-means clustering algorithm
  • use SciKit Learn to build clusters in python

Anomaly Detection

  • describe anomaly detection, the types of problems solved with anomaly detection, and some approaches to anomaly detection
  • use scikit learn to perform anomaly detection

Dimensionality Reduction

  • describe the problems with dimensionality and why efforts to reduce dimensionality should be taken
  • describe principal component analysis for dimensionality reduction
  • use SciKit Learn to perform dimensionality reduction

Practice: Unsupervised Learning

  • perform dimensionality reduction and clustering tasks in Python

Neural Networks

Introducing Neural Networks (NNs)

  • start the course
  • describe neural networks and their capabilities
  • describe how different neural networks are structured
  • describe how cost functions are used to train neural networks
  • describe activation functions and list different types of commonly used activation functions
  • describe feedforward neural networks and the intuition behind calculating gradients in neural networks
  • describe how to use backpropagation for more efficient neural network training
  • describe batch learning and why it makes neural network training easier

TensorFlow (TF)

  • describe TensorFlow and its high-level architecture
  • set up TensorFlow for use on a CPU
  • import data into TensorFlow using built-in data sources and external data sources
  • build and train a single-layer neural network in TensorFlow
  • build and train a multilayer neural network in TensorFlow

Practice: Neural Networks

  • describe neural networks, network layers, cost functions, activation functions, and gradient descent

Convolutional and Recurrent Neural Networks

Convolutional Neural Networks

  • start the course
  • describe convolutional neural networks, how they are different from regular neural networks, and how they are used
  • describe the high level architecture of convolutional neural networks
  • describe how convolution layers are set in convolutional neural networks
  • describe how pooling layers work in convolutional neural networks
  • describe some training considerations for convolutional neural networks and how training can differ from traditional neural networks
  • describe regularization and how it applies to convolutional neural networks
  • implement and train a convolutional neural network in TensorFlow
  • perform regularizing to a convolutional neural network in TensorFlow

Recurrent Neural Networks

  • describe recurrent neural networks, how they are different from regular neural networks, and how they are used
  • describe the architecture of a recurrent neural network
  • implement an LSTM network in TensorFlow
  • use RNNs to perform time-series analysis in TensorFlow

Practice: CNNs in TensorFlow

  • use TensorFlow to create a CNN that classifies images

Applying Machine Learning

Model Evaluation and Selection

  • start the course
  • describe the two main types of error in machine learning models and the tradeoff between them
  • describe how to use cross-validation to show how generalized a model is
  • describe cross-validation in Python to obtain strong evaluation scores
  • describe different metrics that can be used to evaluate binary classification models
  • describe different metrics that can be used to evaluate non-binary classification models
  • describe common evaluation metrics for evaluating classification models
  • describe different metrics that can be used to evaluate regression models
  • describe how to use Python to calculate common evaluation methods

Machine Learning With AWS

  • describe AWS machine learning
  • set up an AWS environment and import data sources
  • create a model with AWS
  • set training criteria with AWS and train a model

Practice: Bias and Variance

  • define bias, variance, and tradeoffs

En dat alles voor een fractie van de kosten van een klassikale training
Wilt U meer informatie over OEM Cert Kit? Neem contact op.

Heeft u niet gevonden wat u zocht?
Laat ons helpen!
Specificaties
Product
Colli:1
Algemene eigenschappen
Duur: 7 uur
Taal: Engels
Certificaat van deelname: Ja
Online toegang: 365 dagen
Voortgangsbewaking: Ja
Award Winning E-learning: Ja
Geschikt voor mobiel: Ja
Beoordelingen
average of 0 review(s)
Geen reviews gevonden
Help ons en andere klanten door het schrijven van een review
Schrijf uw beoordeling!




Wij slaan cookies op om onze website te verbeteren. Is dat akkoord? Ja Nee Meer over cookies »