Course Machine Learning with PyTorch

In the course Machine Learning with PyTorch data scientists, data engineers and aspiring machine learning practitioners learn how to harness the power of the PyTorch framework to create machine learning applications using Python and the Torch library. The course covers fundamental concepts and advanced techniques and provides a hands-on learning experience in the exciting field of machine learning.

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  • Training
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  • Certificate
  • Course Machine Learning with PyTorch : Content

    Intro PyTorch

    The course Machine Learning with PyTorch starts with an introduction to PyTorch, covering the basic principles of tensors, autograd and the PyTorch ecosystem.

    Linear Regression

    Subsequently linear regression in PyTorch for predicting results is discussed, including optimization with gradient descent, loss functions, regularization techniques and evaluation metrics.

    Neural Networks

    Then neural networks with PyTorch are treated, where activation functions, backpropagation and optimization algorithms are explained.


    Classification tasks in PyTorch are also covered with logistic regression and cross entropy losses. Both binary and multi-class classification are treated.

    Model Building

    And model building is also on the program of the course Machine Learning with PyTorch. Here it is explained how more complex models can be based on fundamental building blocks, using feature engineering, categorical variables and hyperparameter tuning.

    Natural Language Processing

    Then Natural Language Processing with PyTorch is explained. The use of text classification, named entity recognition and sequence to sequence models for machine translations is covered.

    Reinforcement Learning

    And reinforcement learning with PyTorch is also on the program. Among others, Markov Decision Processes, Q-Learning, Policy Gradients and Actor-Critic Methods are discussed then.

    Image Processing

    The use of PyTorch for image processing is also covered, including classification, object detection and semantic segmentation.

    Model Optimization

    Finally attention is paid to optimizing machine learning models in PyTorch with the goal to improve performance and efficiency. Techniques such as batch normalization, hyperparameter tuning and pruning are treated then.

  • Course Machine Learning with PyTorch : Training

    Audience Course Machine Learning with PyTorch

    The course Machine Learning with PyTorch is intended for data scientists who want to use Python and the Torch machine learning library to create models and make predictions.

    Prerequisites training Machine Learning with PyTorch

    To participate in this course, knowledge of and experience with Python is required and knowledge of data analysis libraries such as Numpy and Pandas is desirable.

    Realization course Machine Learning with PyTorch

    The theory is discussed through presentations. Illustrative demos clarify the concepts. The theory is interchanged with exercises.

    Certificate course Machine Learning with PyTorch

    After successfully completing the course, attendants will receive a certificate of participation in Machine Learning with PyTorch.

    Course Machine Learning with PyTorch
  • Course Machine Learning with PyTorch : Modules

    Module 1 : Intro PyTorch

    Module 2 : Linear Regression

    Module 3 : Neural Networks

    Machine Learning Intro
    Overview of PyTorch
    Installing Anaconda
    Setting Up PyTorch
    PyTorch Tensors
    Tensor Operations
    Simple Neural Networks
    Datasets and DataLoaders
    Fundamentals of Autograd
    Model Evaluation Metrics
    Linear Regression in PyTorch
    Gradient Descent Optimization
    Mean Squared Error
    Regularization Techniques
    Feature Scaling
    Feature Normalization
    Categorical Features
    Model Evaluation Metrics
    RMSE, MAE, R-squared
    Hyperparameter Tuning
    Neural Networks Intro
    Building NN with PyTorch
    Multiple Layers of Arrays
    Convolutional Neural Networks
    Activation Functions
    Loss Functions
    Gradient Descent
    Stochastic Gradient Descent
    Recurrent Neural Networks

    Module 4 : Classification

    Module 5 : Model Building

    Module 6 : Natural Language Processing

    Logistic Regression
    Binary Classification
    Multi-class Classification
    Confusion Matrix
    Precision and Recall
    ROC Curve
    Handling Imbalanced Data
    Regularization Techniques
    Hyperparameter Tuning
    PyTorch Models
    Model Components
    Common Layer Types
    Linear Layers
    Convolutional Layers
    Input Channels
    Recurrent Layers
    Data Manipulation Layers
    NLP Overview
    Text Preprocessing
    Stopword Removal
    Spam Detection
    Word Embedding
    Sentiment Analysis
    Attention Mechanisms
    Transformer Models

    Module 7 : Reinforcement Learning

    Module 8 : Image Processing

    Module 9 : Model Optimization

    Intro Reinforcement Learning
    Markov Decision Processes
    Deep Q-Networks
    Policy Gradient Methods
    Actor-Critic Methods
    Proximal Policy Optimization
    Deep Policy Gradient
    Image Preprocessing
    Resizing and Normalization
    Convolution Layer
    Convolutional Neural Networks
    Object Detection
    Transfer Learning
    Semantic Segmentation
    Image Captioning
    Profiling PyTorch
    Profiler With TensorBoard
    Hyperparameter tuning
    Dynamic Quantization
    High-Performance Transformers
  • Course Machine Learning with PyTorch : General

    Read general course information
  • Course Machine Learning with PyTorch : Reviews

  • Course Machine Learning with PyTorch : Certificate