Learning by doing
Trainers with practical experience
Detailed course material
Clear content description
Tailormade content possible
Training that proceeds
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.
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.
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.
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.
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.
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.
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.
The course Machine Learning with Python is intended for data analysts who want to use Python and the Python libraries in Data Analysis projects.
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.
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.
After successful completion of the course, participants receive an official Machine Learning certificate with Python.
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
Classification and Regression
Data Speaks for Itself
Clustering and Dimensionality Reduction
NumPy Data Types
Pandas Data Frames
Operations on Data
Plotting with Pandas
Fit and Predict Method
Training and Testing Set
Module 4 : Feature Engineering
Module 5 : Naive Bayes
Module 6 : Linear Regression
Text and Image Features
Handling Missing Data
Imputation of Missing Data
Polynomial Basis Functions
Gaussian Basis Functions
Naive Bayes Classifiers
High Dimensional Datasets
Gaussian Naive Bayes
Multinomial Naive Bayes
When to Use Naive Bayes
Slope and Intercept
coef_ and intercept_ Parameter
Multidimensional Linear Models
Basis Function Regression
Gaussian Basis Functions
Module 7 : Support Vector Machines
Module 8 : Decision Trees
Module 9 : Principal Components
Maximizing the Margin
Radial Basis Function
Creating Decision Trees
Overfitting Decision Trees
Ensembles of Estimator
Random Forest Regression
Non Parametric Model
PCA Unsupervised Learning
Learn about Relationships
Explained Variance Ratio
PCA as Noise Filtering
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.
We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.
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.
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.
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.
The course includes lunch that we use in a restaurant within walking distance of the course room.
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.
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.
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.