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Opleiding: Exploring Machine Learning

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P.J, Oudweg 4
1314 CH ALMERE
 

Inhoud van de cursus

ntroduction 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 Nave Bayes classifiers and how to train them
use scikit-learn to fit a Nave 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

Toelatingseisen: wat heb je nodig?

Er is geen specifieke voorkennis vereist.

Duur van de cursus

7 uur

Bijzonderheden

Award Winning E-learning

Plaatsen / leslocaties

Heel Nederland, E-learning, Online

Algemene informatie over de cursus

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.

Duur: 7 uur
Taal: Engels
Certificaat van deelname: Ja
Online toegang: 365 dagen
Voortgangsbewaking: Ja
Award Winning E-learning: Ja
Geschikt voor mobiel: Ja

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