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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

Duur: 7 uur

Taal: Engels

Certificaat van deelname: Ja

Online toegang: 365 dagen

Voortgangsbewaking: Ja

Award Winning E-learning: Ja

Geschikt voor mobiel: Ja

Gegevens aangeduid met een ***** zijn verplicht in te vullen.

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