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Machine Learning Exploring Machine Learning E-Learning Kurs
Exploring Machine Learning E-Learning Kurs
€139,00 €159,00

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Exploring Machine Learning E-Learning Kurs

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Exploring Machine Learning E-learning

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

 

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Eigenschaften
Produkt
Allgemeine Eigenschaften
Studiengebühr: 5 Stunden Dauer plus Übungen (variabel)
Sprache: Englisch
Online-Zugang: 1 Jahr
Teilnahmebescheinigung: Ja, nach 70% der erfolgreichen Einsätze
Fortschrittsüberwachung: Ja
Geeignet für Handys: Ja
Preisgekröntes E-Learning: Ja
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