Deep Learning In Time Series Course

  Intake: June 2021

Time series data is a type of sequential data taken at different time ranges. The data includes sensory data, stock price and sales data. 

In this course, you will learn the fundamental and advanced concepts and practical skills of deep learning and time series. 

01. Mastering of Machine Learning Fundamentals I

  • Theory (e.g., linear algebra, probability and statistics, calculus)
  • Hands-on (e.g., NumPy, linear regression, feature scaling and more)

2 hours

02. Mastering of Machine Learning Fundamentals II

  • Theory (e.g., machine learning basics, supervised vs unsupervised learning, data preprocessing, regression and more)
  • Hands-on (e.g., diabetes prediction, house price prediction)

4 hours

03. Mastering of Machine Learning Fundamentals III & IV

  • Theory (e.g., classification, clustering, evaluation metrics, dimension reduction and more)
  • Hands-on (e.g., logistic regression, K nearest neighbour, K means clustering, Principal Components Analysis)

6 hours

04. Introduction of Deep Learning with Deeplearning4j I

  • Theory (e.g., basic introduction to AI, Machine Learning and Deep Learning, Feedforward Neural Network, Deeplearning4j and more)
  • Hands-on (e.g., lab environment preparation, DataVec, gender classification by name and more)

6 Hours

05. Introduction of Deep Learning with Deeplearning4j II

  • Theory (e.g., Deeplearning4j training UI overview, hyperparameter tuning and more)
  • Hands-on (e.g., construction of the convolutional neural network, saving and loading trained model)

6 hours

06. Introduction of Deep Learning with Deeplearning4j III & IV

  • Theory (e.g., Introduction to Autoencoder and GAN, Recurrent Neural Network, Vectorization of Time Series Data and more)
  • Hands-on (Mnist GAN, medical data classification and weather forecasting with LSTM)

7 hours

07. Time Series Forecasting 1 - Fundamentals of Time Series Forecasting

  • Theory  (e.g., Introduction to time series analysis, types of time series problems and more)
  • Hands-on (e.g., Pandas for Time Series, Time Series Decomposition)

6 hours

08. Univariate Forecasting with ARIMA

  • Theory (e.g., AR model, MA model, stationarity and more)
  • Hands-on (e.g., time series forecasting)

6 hours

09. Advanced Deep Learning for Time Series Prediction

  • Theory (e.g., deep learning models, cross-validation method for time series)
  • Hands-on (e.g., univariate, multivariate, and multi-step prediction and more)

6 hours

10. Advanced Deep Learning for Time Series Classification and Anomaly Detection

  • Theory (e.g., data sampling and data augmentation for time series classification, deep learning for anomaly detection and more)
  • Hands-on (e.g., Human Activity Recognition, Credit Card Fraud Detection, Machine Anomaly Detection)

6 hours

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