**Exploring Machine Learning**

**€229,00**

**Information**

# 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

**And all for a fraction of the cost of a classroom training**

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**Specifications**

**Product**

Colli: | 1 |

**General properties**

Availabilty: |
7 hours |

Language: |
English |

Certificate of participation: |
Yes |

Online access: |
90 days |

Progress monitoring: |
Yes |

Award Winning E-learning: |
Yes |

Suitable for mobile: |
Yes |

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