Python: Introduction to Machine Learning with Python - Training Courses | Afi U.
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Python: Introduction to Machine Learning with Python

Acquire the expertise to choose the right algorithms to use and be able to analyze the results of the chosen algorithms.
Private session

This training is available in a private or personalized format. It can be provided in one of our training centres or at your offices. Call one of our consultants of submit a request online.

Call now at 1 877 624.2344

  • Duration: 3 days
  • Regular price: On request

Course outline

Reference : Python machine learning

Duration : 3 days

© AFI par Edgenda inc.

Prerequisites

Basic knowledge of programming with Python

Objectives

  • Understanding machine learning and its subfields
  • Getting used to common machine learning algorithms
  • Making the right choice about what algorithm to use depending on the case
  • Acquiring expertise in analyzing algorithms results and performance metrics

Contents

Introduction
  • Supervised learning, unsupervised learning and reinforcement learning
  • Classification, regression, structural prediction
  • Model evaluation: metrics
  • Hyperparameter selection, model selection
  • Initiation to Scikit-learn
  • Data types and methods selection guide
Classification: Introduction with Optical Character Recognition (OCR)
  • K nearest neighbors’ algorithm (KNN)
  • Decision trees
  • Ensemble methods
  • Support Vector Machines (SVM)
  • Results visualization
Classification: Advanced concepts with sentiment analysis
  • Data preprocessing for learning algorithms
  • Dimensionality reduction
  • Batchwise training
  • Interpretability
Regression
  • Linear regression
  • Non-linear regression with kernel methods
  • Outlier detection and handling
  • Time series: Challenges, decomposition and predictive methods
  • Time series: non-stationary regression and auto-regressive models
Recommendation systems, case study
  • Collaborative filtering per user
  • Collaborative filtering per item
  • Advanced concepts and algorithms
Unsupervised learning
  • Clustering: K-means, hierarchical clustering, density methods
  • Dimensionality reduction: PCA, t-SNE,
  • Generative models: Introduction to autoencoders and variational autoencoders
Practical debugging guide
  • Overfitting test
  • Data pipelines test
Exploration of alternative metrics