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.

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  • Duration: 3 days
  • Regular price: On request

Course outline

Reference : Python machine learning

Duration : 3 days

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

Surround yourself with the best

Pierre-Edouard Brondel
Pierre-Edouard Brondel
Trainer and Desktop Application Consultant
Renowned as an educational expert in the IT and office technology field who has accumulated more than 25 years of experience, Pierre-Édouard is first and foremost passionate about human capital.
Marc Maisonneuve
Marc Maisonneuve
Trainer and Professional Efficiency Consultant
Frédéric Paradis
Frédéric Paradis
Certified Trainer and Cloud Architect
As a certified Microsoft trainer, Frédéric describes himself as a Cloud magician who easily navigates the mythical space between technology and reality.
Virginie Louis
Virginie Louis
Efficiency Trainer, Facilitator and Spatial Intelligence Consultant
Virginie sees herself first and foremost as a facilitator: she strays from the standard training to provide solutions that are adapted to her clients’ realities and objectives.
Be aware of trends, innovations and best practices, every month.
Confidentiality
Training center accredited by Emploi-Québec, Accreditation : 0051460
GST : 141582528 – QST : 1019557738
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