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Course Machine Learning with Python

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  • Course Machine Learning with Python : Content

    In the course Machine Learning with Python participants learn how to implement machine learning algorithms using Python and the Scikit-learn library. Scikit-learn is largely written in Python and makes extensive use of Numpy for high-performance linear algebra and array operations.

    Machine Learning Intro

    The Machine Learning with Python course starts with an overview of the basic concepts of Machine Learning in which models are made on the basis of supplied data. The difference is explained between Supervised and Unsupervised Learning.

    Scikit-learn Library

    Subsequently the libraries that form the foundation behind Machine Learning with Scikit-learn such as Numpy, Pandas, MatPlotLib and Seaborn are discussed. In the basic architecture of Scikit-learn, the data is split into a feature matrix and a target array. Also treated is how a model is trained with a training set and then compared to a test set with the Estimator API.

    Feature Handling

    The course Machine Learning with Python also includes Feature Engineering. This discusses how to deal with categorical features, text features, image features and derived features. And the use of features pipelines is also explained.

    Regressions

    After a treatment of the Naive Bayes theorem with Naive Bayes classifiers and the models based on them, Linear and Logistic regression are discussed. Specialist versions such as Polynomial Regression, Ridge Regression and Lasso Regularization are also covered.

    Classifications

    Then the course Machine Learning with Python pays attention to different variants of Machine Learning algorithms that are based on classification. Support Vector Machines and Decision Trees are discussed here.

    Unsupervised Learning

    Finally the course Machine Learning with Python deals with Principal Component Analysis as an example of an unsupervised learning algorithm. Dimensionality Reduction is then treated as well.

  • Course Machine Learning with Python : Training

    Audience Course Machine Learning with Python

    The course Machine Learning with Python is intended for data analysts who want to use Python and the Python libraries in Data Analysis projects.

    Prerequisites training Machine Learning with Python

    To participate in this course knowledge of and experience with any programming language or package such as SPSS, Matlab or VBA is desirable. The course starts with a discussion of the principles of the Python programming language.

    Realization Course Machine Learning with Python

    The theory is discussed on the basis of presentation slides. Illustrative demos clarify the concepts. The theory is interchanged with exercises. The Anaconda distribution with Jupyter notebooks is used as a development environment. Course times are from 9:30 to 16:30.

    Official Certificate Machine Learning with Python

    After successful completion of the course, participants receive an official Machine Learning certificate with Python.

    Course Machine Learning with Python
  • Course Machine Learning with Python : Modules

    Module 1 : Intro Machine Learning

    Module 2 : Numpy and Pandas

    Module 3 : Scikit-learn Library

    What is Machine Learning?
    Building Models of Data
    Model Based Learning
    Tunable Parameters
    Supervised Learning
    Labeling Data
    Discrete Labels
    Continuous Labels
    Classification and Regression
    Unsupervised Learning
    Data Speaks for Itself
    Clustering and Dimensionality Reduction
    Numpy Arrays
    NumPy Data Types
    Pandas Data Frames
    Inspect Data
    Operations on Data
    Missing Data
    Pandas Series
    Pandas Indexes
    Time Series
    MatplotLib
    Plotting with Pandas
    Seaborn Library
    Data Representation
    Estimator API
    Features Matrix
    Target Array
    Seaborn Visualization
    Model Classes
    Choosing Hyperparameters
    Model Validation
    Fit and Predict Method
    Label Predicting
    Training and Testing Set
    Transform Method

    Module 4 : Feature Engineering

    Module 5 : Naive Bayes

    Module 6 : Linear Regression

    Categorical Features
    Vectorization
    Text and Image Features
    Derived Features
    Adding Columns
    Handling Missing Data
    Imputation of Missing Data
    Feature Pipelines
    Polynomial Basis Functions
    Gaussian Basis Functions
    Regularization
    Naive Bayes Classifiers
    Applicability
    High Dimensional Datasets
    Bayes’s Theorem
    Generative Models
    Gaussian Naive Bayes
    Probabilistic Classification
    predict_proba Method
    Multinomial Naive Bayes
    Confusion Matrix
    When to Use Naive Bayes
    Slope and Intercept
    LinearRegression Estimator
    coef_ and intercept_ Parameter
    Multidimensional Linear Models
    Basis Function Regression
    Polynomial Regression
    PolynomialFeatures Transformer
    Gaussian Basis Functions
    Overfitting
    Ridge Regression
    Lasso Regularization

    Module 7 : Support Vector Machines

    Module 8 : Decision Trees

    Module 9 : Principal Components

    Discriminative Classification
    Maximizing the Margin
    Linear Kernel
    C Parameter
    Support Vectors
    SVM Visualization
    Kernel SVM
    Radial Basis Function
    Kernel Transformation
    Kernel Trick
    Softening Margins
    Ensemble Learner
    Creating Decision Trees
    DecisionTree Classifier
    Overfitting Decision Trees
    Ensembles of Estimator
    Random Forests
    Parallel Estimators
    Bagging Classifier
    Random Forest Regression
    RandomForest Regressor
    Non Parametric Model
    PCA Unsupervised Learning
    Learn about Relationships
    Principal Axes
    Demonstration Data
    Affine Transformation
    Components
    Explained Variance
    Dimensionality Reduction
    Inverse Transformation
    Explained Variance Ratio
    PCA as Noise Filtering
  • Course Machine Learning with Python : General

    Course Forms

    All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.

    Customization

    We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.

    Course times

    The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.

    Hardware

    We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.

    Software

    Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.

    Lunch

    The course includes lunch that we use in a restaurant within walking distance of the course room.

    Locations

    The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.

    Evaluations

    At the end of each course, participants are requested to evaluate the course in terms of course content, course material, trainer and location. The evaluation form can be found at https://www.klantenvertellen.nl/reviews/1039545/spiraltrain?lang=en. The evaluations of previous participants and previous courses can also be found there.

    Copyright

    The intellectual property rights of the published course content, also referred to as an information sheet, belong to SpiralTrain. It is not allowed to publish the course information, the information sheet, in written or digital form without the explicit permission of SpiralTrain. The course content is to be understood as the description of the course content in sentences as well as the division of the course into modules and topics in the modules.

  • Course Machine Learning with Python : Reviews

  • Course Machine Learning with Python : Certificate