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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.

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  • Duration: 3 days
  • Regular price: $2,100
  • Preferential price: $1,785

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

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.
Marc Maisonneuve
Marc Maisonneuve
Training program director
Marc Maisonneuve has acted as a Training Program Director, professional effectiveness trainer and user tools practice leader at AFI for several years. Mr. Maisonneuve is known for his analytical skills, his legendary calm and his undeniable desire to encourage people to develop their skills. He has the ability to present technological solutions in a natural way and to adapt them to the concrete needs of the workplace.
Vicky Moreau
Vicky Moreau
Trainer
Vicky Moreau is a passionate freelancer and professional in the area of office automation. She holds a college diploma in office automation, most of her solid experience with the Office Suite was acquired while being an autodidact. In fact, she has successfully completed an MOS (Microsoft Office Specialist) Excel certification.
Francis Ferland-Stevenson
Francis Ferland-Stevenson
Trainer
Francis began as a trainer more than 5 years ago by testing office automation tools designed specifically to met the needs of his colleagues. His calm and his empathy makes him able to adapt his language according to the level of experience of the group. This makes his learnings clear and accessible to anyone. As a trainer, he is therefore attentive to the needs of his students to make sure they meet their objectives and face the challenges.