machine learning

Natural Language Processing (NLP) refers to tools and methods to explore text data as well as identifiy patterns and making predictions.

Neural networks for natural language processing

Data science 2023-01-10

Natural Language Processing (NLP) refers to tools and methods to explore text data as well as identifiy patterns and making predictions.

Some notes about image recognition while preparing for Google Cloud MLE certification.

Neural networks for image recognition

Data science 2023-01-09

Some notes about image recognition while preparing for Google Cloud MLE certification.

Keras is one of the high level APIs in Tensorflow deep learning stack. It is the recommended framework to get started with neural networks, if you do not have special requirements.

Keras for basic neural networks

Data science 2023-01-07

Keras is one of the high level APIs in Tensorflow deep learning stack. It is the recommended framework to get started with neural networks, if you do not have special requirements.

I have more experience from Pandas and Scikit-Learn Python libraries compared to Tensorflow. I was surprised how large the Tensorflow ecosystem with its ML engineering extensions.

Tensorflow for ML Engineers

Data science 2023-01-05

I have more experience from Pandas and Scikit-Learn Python libraries compared to Tensorflow. I was surprised how large the Tensorflow ecosystem with its ML engineering extensions.

Recommendation systems are useful to personalize experience and find relevant items among huge catalogs.
Recommendations have became signigficant sub topic of machine learning.

Recommendation systems in Google Cloud

Data science 2023-01-03

Recommendation systems are useful to personalize experience and find relevant items among huge catalogs. Recommendations have became signigficant sub topic of machine learning.

Notes about fundamental ML concepts for Google Cloud ML Engineering certification.
Data exploration Perform mainly univariate and bivariate analysis during initial exploration.

Machine learning fundamentals

Data science 2022-12-28

Notes about fundamental ML concepts for Google Cloud ML Engineering certification. Data exploration Perform mainly univariate and bivariate analysis during initial exploration.

Some Google materials refer to it as Fully managed Tensorflow.
Vertex AI assumes that data is prepared elsewhere before training the model.

Vertex AI for ML Engineers in Google Cloud

Data science 2022-12-02

Some Google materials refer to it as Fully managed Tensorflow. Vertex AI assumes that data is prepared elsewhere before training the model.

BigQuery is by far the most important storage and processing service in Google Cloud from ML perspective.
It has many integrated functionalities for ML.

BigQuery for ML Engineers in Google Cloud

Data science 2022-12-01

BigQuery is by far the most important storage and processing service in Google Cloud from ML perspective. It has many integrated functionalities for ML.

Google Colab, Databricks Community Edition, Visual Studio Code and Dcoker are some options to create a free data science workspace.

Free data science workspaces

Data science 2021-12-25

Google Colab, Databricks Community Edition, Visual Studio Code and Dcoker are some options to create a free data science workspace.

Comparing the major machine learning platforms AWS SageMaker, Azure Machine Learning, Google Vertex AI and Databricks.

Comparison of machine learning platforms in major clouds

Data science 2021-11-27

Comparing the major machine learning platforms AWS SageMaker, Azure Machine Learning, Google Vertex AI and Databricks.

What is a machine learning platform?

Data science 2021-11-21

What is a machine learning platform? Introducing different components such as workbench, MLOps tools and cloud computation.

Machine learning in predictive maintenance

Data science 2020-10-29

Machine learning in predictive maintenance. The two-part blog series provides insights for cost savings and an example script in Python.

In my opinion the big difference is that a data scientist focuses more on business problems while data engineer solves technical problems.

Difference between data scientist and data engineer roles

Data science 2020-08-17

In my opinion the big difference is that a data scientist focuses more on business problems while data engineer solves technical problems.

Experiences from DataCamp online training. Structured data science courses are easy to organize for yourself or a team.

DataCamp - Learn data science online

Data science 2020-08-13

Experiences from DataCamp online training. Structured data science courses are easy to organize for yourself or a team.

Clustering time series data with SQL - Nice 3D visualization using simple logic. Python notebook example in GitHub with industrial data.

Clustering data using SQL - An example with industrial IoT data

Data science 2019-11-10

Clustering time series data with SQL - Nice 3D visualization using simple logic. Python notebook example in GitHub with industrial data.

Finnish stemming and lemmatization in python

Data science 2019-06-23

I wrote to Solita's blog about text analytics with the headline "Finnish stemming and lemmatization in python". The post has code examples.

Experiences from funding application classification by text analytics

Data science 2019-01-27

Experiences from funding application classification by text analytics

I give an example about machine learning use case in a format that should be understandable also for less technical people.

Combining machine learning and business - Practical example

Data science 2018-05-15

I give an example about machine learning use case in a format that should be understandable also for less technical people.

You can make the living by sports betting. The blog is not sponsored as I'm sharing my own experiences. Read the tutorial.

Sports betting tutorial - Can you make the living?

Data science 2016-10-26

You can make the living by sports betting. The blog is not sponsored as I'm sharing my own experiences. Read the tutorial.