Course Outline

Supervised learning: classification and regression

  • Machine Learning in Python: intro to the scikit-learn API
    • linear and logistic regression
    • support vector machine
    • neural networks
    • random forest
  • Setting up an end-to-end supervised learning pipeline using scikit-learn
    • working with data files
    • imputation of missing values
    • handling categorical variables
    • visualizing data

Python frameworks for for AI applications:

  • TensorFlow, Theano, Caffe and Keras
  • AI at scale with Apache Spark: Mlib

Advanced neural network architectures

  • convolutional neural networks for image analysis
  • recurrent neural networks for time-structured data
  • the long short-term memory cell

Unsupervised learning: clustering, anomaly detection

  • implementing principal component analysis with scikit-learn
  • implementing autoencoders in Keras

Practical examples of problems that AI can solve (hands-on exercises using Jupyter notebooks), e.g. 

  • image analysis
  • forecasting complex financial series, such as stock prices,
  • complex pattern recognition
  • natural language processing
  • recommender systems

Understand limitations of AI methods: modes of failure, costs and common difficulties

  • overfitting
  • bias/variance trade-off
  • biases in observational data
  • neural network poisoning

Applied Project work (optional)

Requirements

There are no specific requirements needed to attend this course.

  28 Hours
 

Testimonials

Related Courses

AdaBoost Python for Machine Learning

  14 hours

Artificial Intelligence (AI) with H2O

  14 hours

AutoML with Auto-Keras

  14 hours

AutoML

  14 hours

Google Cloud AutoML

  7 hours

AutoML with Auto-sklearn

  14 hours

Pattern Recognition

  21 hours

DataRobot

  7 hours

Data Mining with Weka

  14 hours

H2O AutoML

  14 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

Pattern Matching

  14 hours

Machine Learning with Random Forest

  14 hours

RapidMiner for Machine Learning and Predictive Analytics

  14 hours

Apache SystemML for Machine Learning

  14 hours