Branches of mechanical engineering: Applied Auto Learning?

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R Machine Learning

R is a platform for statistical computing in addition to is the most pop platform amidst professional person information scientists.

It’s pop because of the large release of techniques available, in addition to because of fantabulous interfaces to these methods such every bit the powerful caret package.

Here’s how to acquire started amongst R machine learning:

• Step 1: Discover the R platform in addition to why it is in addition to then popular.
  • What is R
  • Use R For Machine Learning
  • Super Fast Crash Course inwards R
• Step 2: Discover machine learning algorithms inwards R.
  • How To Get Started With Machine Learning Algorithms inwards R
• Step 3: Discover how to work through problems using machine learning inwards R.
  • Your First Machine Learning Project inwards R Step-By-Step
  • R Machine Learning Mini-Course
  • Machine Learning Mastery With R (my book)

You tin flame run into all R machine learning posts here. Below is a alternative of some of the most pop tutorials.

Data Preparation inwards R

• How To Load Your Machine Learning Data Into R
• Better Understand Your Data inwards R Using Descriptive Statistics
• Better Understand Your Data inwards R Using Visualization
• Feature Selection amongst the Caret R Package
• Get Your Data Ready For Machine Learning inwards R amongst Pre-Processing

Applied Machine Learning inwards R

• How to Evaluate Machine Learning Algorithms amongst R
• Spot Check Machine Learning Algorithms inwards R
• Tune Machine Learning Algorithms inwards R
• How to Build an Ensemble Of Machine Learning Algorithms inwards R
• Compare The Performance of Machine Learning Algorithms inwards R

Deep Learning

Deep learning is a fascinating in addition to powerful field.

State-of-the-art results are coming from the plain of deep learning in addition to it is a sub-field of machine learning that cannot live ignored.

Here’s how to acquire started amongst deep learning:

• Step 1: Discover what deep learning is all about.
  • What is Deep Learning?
  • 8 Inspirational Applications of Deep Learning
• Step 2: Discover the best tools in addition to libraries.
  • Introduction to the Python Deep Learning Library Theano
  • Introduction to the Python Deep Learning Library TensorFlow
  • Introduction to Python Deep Learning amongst Keras
• Step 3: Discover how to work through problems in addition to deliver results.
  • Develop Your First Neural Network inwards Python With Keras Step-By-Step
  • Applied Deep Learning inwards Python Mini-Course
  • Deep Learning With Python (my book)

You tin flame run into all deep learning posts here. Below is a alternative of some of the most pop tutorials.

Background

• Crash Course On Multi-Layer Perceptron Neural Networks
• Crash Course inwards Convolutional Neural Networks for Machine Learning
• Crash Course inwards Recurrent Neural Networks for Deep Learning

Multilayer Perceptrons

• 5 Step Life-Cycle for Neural Network Models inwards Keras
• How to Grid Search Hyperparameters for Deep Learning Models inwards Python With Keras
• Save in addition to Load Your Keras Deep Learning Models
• Display Deep Learning Model Training History inwards Keras
• Dropout Regularization inwards Deep Learning Models With Keras

Convolutional Neural Networks

• Handwritten Digit Recognition using Convolutional Neural Networks inwards Python amongst Keras
• Object Recognition amongst Convolutional Neural Networks inwards the Keras Deep Learning Library
• Predict Sentiment From Movie Reviews Using Deep Learning

Recurrent Neural Networks

• Time Series Prediction amongst LSTM Recurrent Neural Networks inwards Python amongst Keras
• Understanding Stateful LSTM Recurrent Neural Networks inwards Python amongst Keras
• Text Generation With LSTM Recurrent Neural Networks inwards Python amongst Keras

Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) Recurrent Neural Networks are designed for sequence prediction problems in addition to are a state-of-the-art deep learning technique for challenging prediction problems.

Here’s how to acquire started amongst LSTMs inwards Python:

• Step 1: Discover the hope of LSTMs.
  • The Promise of Recurrent Neural Networks for Time Series Forecasting
• Step 2: Discover where LSTMs are useful.
  • Making Predictions amongst Sequences
  • A Gentle Introduction to Long Short-Term Memory Networks past times the Experts
  • Introduction to Models for Sequence Prediction
• Step 3: Discover how to use LSTMs on your project.
  • The five Step Life-Cycle for Long Short-Term Memory Models inwards Keras
  • LSTM Mini-Course
  • Long Short-Term Memory Networks With Python (my book)

You tin flame run into all LSTM posts here. Below is a alternative of some of the most pop tutorials using LSTMs inwards Python amongst the Keras deep learning library.

Data Preparation for LSTMs

• How to Reshape Input Data for Long Short-Term Memory Networks
• How to One Hot Encode Sequence Data
• How to Remove Trends in addition to Seasonality amongst a Difference Transform
• How to Scale Data for Long Short-Term Memory Networks
• How to Prepare Sequence Prediction for Truncated BPTT
• How to Handle Missing Timesteps inwards Sequence Prediction Problems

LSTM Behaviour

• A Gentle Introduction to Backpropagation Through Time
• Demonstration of Memory amongst a Long Short-Term Memory Network
• How to Use the TimeDistributed Layer for Long Short-Term Memory Networks
• How to purpose an Encoder-Decoder LSTM to Echo Sequences of Random Integers
• Attention inwards Long Short-Term Memory Recurrent Neural Networks

Modeling amongst LSTMs

• Generative Long Short-Term Memory Networks
• Stacked Long Short-Term Memory Networks
• Encoder-Decoder Long Short-Term Memory Networks
• CNN Long Short-Term Memory Networks
• Diagnose Overfitting in addition to Underfitting of LSTM Models
• How to Make Predictions amongst Long Short-Term Memory Models

LSTM for Time Series

• On the Suitability of LSTMs for Time Series Forecasting
• Time Series Forecasting amongst the Long Short-Term Memory Network
• Multi-step Time Series Forecasting amongst Long Short-Term Memory Networks
• Multivariate Time Series Forecasting amongst LSTMs inwards Keras

XGBoost

XGBoost is a highly optimized implementation of gradient boosted determination trees.

It is popular because it is existence used by some of the best information scientists inwards the Blue Planet to win machine learning competitions.

Here’s how to acquire started amongst XGBoost:

• Step 1: Discover the Gradient Boosting Algorithm.
  • A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
• Step 2: Discover XGBoost.
  • A Gentle Introduction to XGBoost for Applied Machine Learning
• Step 3: Discover how to get goodness at delivering results amongst XGBoost.
  • How to Develop Your First XGBoost Model inwards Python amongst scikit-learn
  • XGBoost With Python Mini-Course
  • XGBoost With Python (my book)

You tin flame run into all XGBoosts posts here. Below is a alternative of some of the most pop tutorials.

XGBoost Basics

• Data Preparation for Gradient Boosting amongst XGBoost inwards Python
• How to Evaluate Gradient Boosting Models amongst XGBoost inwards Python
• Avoid Overfitting By Early Stopping With XGBoost In Python
• Feature Importance in addition to Feature Selection With XGBoost inwards Python

XGBoost Tuning

• How to Configure the Gradient Boosting Algorithm
• Tune Learning Rate for Gradient Boosting amongst XGBoost inwards Python
• Stochastic Gradient Boosting amongst XGBoost in addition to scikit-learn inwards Python
• How to Tune the Number in addition to Size of Decision Trees amongst XGBoost inwards Python
• How to Best Tune Multithreading Support for XGBoost inwards Python

Time Series Forecasting

Time serial forecasting is an of import topic inwards concern applications.

Many datasets incorporate a fourth dimension component, exactly the topic of fourth dimension serial is rarely covered inwards much depth from a machine learning perspective.

Here’s how to acquire started amongst Time Series Forecasting:

• Step 1: Discover Time Series Forecasting.
  • What Is Time Series Forecasting?
• Step 2: Discover Time Series every bit Supervised Learning.
  • Time Series Forecasting every bit Supervised Learning
• Step 3: Discover how to get goodness at delivering results amongst Time Series Forecasting.
  • Python Environment for Time Series Forecasting
  • Time Series Forecasting With Python Mini-Course
  • Time Series Forecasting With Python (my book)

You tin flame run into all Time Series Forecasting posts here. Below is a alternative of some of the most pop tutorials.

Data Preparation Tutorials

• 7 Time Series Datasets for Machine Learning
• How to Load in addition to Explore Time Series Data inwards Python
• How to Normalize in addition to Standardize Time Series Data inwards Python
• Basic Feature Engineering With Time Series Data inwards Python
• How To Backtest Machine Learning Models for Time Series Forecasting

Forecasting Tutorials

• How to Make Baseline Predictions for Time Series Forecasting amongst Python
• How to Check if Time Series Data is Stationary amongst Python
• How to Create an ARIMA Model for Time Series Forecasting amongst Python
• How to Grid Search ARIMA Model Hyperparameters amongst Python
• How to Work Through a Time Series Forecast Project

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