Exploring Machine Learning

Learn about Machine Learning, various learning modes in machine learning, neural networks, and how to apply machine learning to a problem.

Access Time: 1 Month

Course Details:

Learn what machine learning is and the fundamentals of supervised learning

Learn about more complicated supervised learning models and how to use them to solve problems

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
describe different supervised learning models in Python

Learn several different techniques in machine learning

Learn about neural networks and how to use them

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

Learn about convolutional and recurrent neural networks and the types of problems they can solve

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
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
use TensorFlow to create a CNN that classifies images

Learn how to evaluate and select machine learning models and apply machine learning to a problem

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
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
define bias, variance, and tradeoffs

Course Fee: USD 75

Categories: Technology