Oshtrainers https://oshtrainers.com OSH TRAINERS PLT Sat, 11 Feb 2023 10:21:16 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 Consultancy https://oshtrainers.com/consultancy/ https://oshtrainers.com/consultancy/#comments Fri, 21 Feb 2020 01:03:01 +0000 https://oshtrainers.com/?p=2864 This Machine Learning Glossary aims to briefly introduce the most important Machine Learning terms - both for the commercially and...

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Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. NLP – Natural Language Processing

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

He goal of this task is to predict a class (label) of a document, or rank documents within in a list based on their relevance. It could be used in spam filtering (predicting whether an e-mail is spam or not) or content classification (selecting articles from the web about what is happening to your competitors).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!

Aron Larsson

– CEO, Strategy Director

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]]> https://oshtrainers.com/consultancy/feed/ 3 Auditing https://oshtrainers.com/auditing/ Wed, 08 Jan 2020 03:35:04 +0000 https://oshtrainers.com/?p=577 This Machine Learning Glossary aims to briefly introduce the most important Machine Learning terms - both for the commercially and...

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DataRobot’s automated machine learning platform helps banks leverage their substantial investments in data to meet today’s challenges. By learning from their own data, banks can find and attract the best new clients, deepen existing client relationships, improve the client experience, and identify new growth opportunities while meeting regulatory requirements and fighting financial crime effectively and efficiently.

1. NLP – Natural Language Processing

This new eBook highlights practical use cases for AI in today’s investment banking market. Armed with this knowledge, investment bankers can take advantage of the enormous amount of data they generate and transform into AI-enabled enterprises.

The most important use cases of Natural Language Processing are:

Sentiment analysis aims to determine the attitude or emotional reaction of a person with respect to some topic – e.g. positive or negative attitude, anger, sarcasm. It is broadly used in customer satisfaction studies (e.g. analyzing product reviews).

2. Reinforcement learning

Download your copy to find out how AI and machine learning can help you grow your business and outperform your competition.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!

Aron Larsson

– CEO, Strategy Director

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]]> Trainings https://oshtrainers.com/trainings/ Wed, 08 Jan 2020 03:34:49 +0000 https://oshtrainers.com/?p=579 Not that long ago, people lived and functioned in tight communities. Every vendor knew their customers personally and could make...

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In marketing analytics, a marketing and sales funnel is the set of steps a visitor goes through before making a purchase. More and more, this customer’s journey includes a diverse set of touchpoints, involving both push and pull:

1. A Customer Journey Can Include Many Touchpoints

Historically, organizations have sent the same messages to all customers, using the same media channels. But, in the modern world, this may be perceived as SPAM that annoys customers and pushes them away. The diagram below (Figure 2) shows the traditional approach in action – every customer sees the same sequence of touchpoints.

The most important use cases of Natural Language Processing are:

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2. When All Customers Receive the Same Outbound Marketing Content

More sophisticated marketers have applied customer segmentation to their customer database. They group customers into segments according to their demographics, creating different customer journeys within each customer segment. Then, they send the same messages, using the same media, to all customers within each segment. This is a marked improvement on the previous approach, but not every customer within a segment is the same. The customer segments are typically grouped by demographics and not according to the content preferences of the customers.

3. When All Customers Within a Segment Receive the Same Marketing Content

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

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