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: **

##### Introduction to Machine Learning and Supervised Learning

Learn what machine learning is and the fundamentals of supervised learning

- Introducing Machine Learning
- Simple Models
- Machine Learning in Python
- Practice: scikit-learn

##### Supervised Learning Models

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

##### Unsupervised Learning

Learn several different techniques in machine learning

- Introducing Unsupervised Learning
- Rule Association
- Cluster Analysis
- Anomaly Detection
- Dimensionality Reduction
- Practice: Unsupervised Learning

##### Neural Networks

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

##### Convolutional and Recurrent Neural Networks

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

##### Applying Machine Learning

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**

Register Now