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What are the first steps for a data warehousing professional to learn machine learning?

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If you are a data warehousing professional, you may be wondering how to learn machine learning and apply it to your projects. Machine learning is a branch of artificial intelligence that uses algorithms and data to learn from patterns and make predictions. It can help you enhance your data analysis, improve your data quality, and create new insights and value from your data. In this article, we will guide you through the first steps for a data warehousing professional to learn machine learning.

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Jeff Moden

Senior DBA and T-SQL Mentor
Claudio Bonel, M.Sc.

Professor e Pesquisador em Inteligência artificial | Cientista de dados e engenheiro de machine learning | Hardcore…

1 Understand the basics

The first step is to understand the basics of machine learning, such as the types of problems it can solve, the types of data it can use, and the types of algorithms it can employ. You should familiarize yourself with the common terminology, such as supervised and unsupervised learning, classification and regression, training and testing, and evaluation and validation. You should also learn about the main challenges and limitations of machine learning, such as overfitting and underfitting, bias and variance, and data quality and quantity.

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Jeff Moden

Senior DBA and T-SQL Mentor
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My first very first step is to "Ask the Ozar Question" and that is "What problem are you trying to solve"? The next question is to ask is to ask why you think the task cannot be accomplished without Machine Learning (as people currently refer to it)?? If it turns out to be "essential", then make sure that you focus on what is "essential" instead of just hopping on the "Too cool for school" and "Look at me" and "Everyone is doing it" bandwagons. As the knight in the cave of the Holy Grail said, "Choose wisely"... and then also remember that "AI" stands for "Artificial Intelligence" and NOT "ACTUAL Intelligence"

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Claudio Bonel, M.Sc.

Professor e Pesquisador em Inteligência artificial | Cientista de dados e engenheiro de machine learning | Hardcore culture
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Faaalaí! Tudo bem com vc? Certamente quando falamos de aprendizado de máquina, o primeiro passo é: Fundamentar-se nos principais conceitos da inteligência artificial, n?o apenas na parte técnica, mas principalmente, na fundamenta??o teórica a respeito do tema. Entender o que os autores que iniciaram os estudos, como Babbage, Lovelace, Turing, McCarthy, colocaram de questionamentos, testes, acertos, erros e avan?os, é essencial para se iniciar nessa caminhada. A partir daí, focalizar nas técnicas de mapeamento de processos e requisitos; seguindo para os básico de uma linguagem de programa??o que dê o suporte para o desenvolvimento. Esse 3 primeiros passos s?o essenciais, para avan?armos nos estudos e práticas. Tmj!

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ABHIJEET SUBUDHI

Assistant Manager at Bharat Petroleum Corporation Limited | Energy Manager | BOE | UDCT | TutorBin | Chegg
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Familiarize yourself with fundamental machine learning concepts and algorithms, focusing on regression, classification, and clustering.

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2 Choose a tool or platform

The next step is to choose a tool or platform that can help you implement and deploy machine learning solutions. There are many options available, depending on your preferences, skills, and budget. Some of the popular tools and platforms include Python, R, SQL, Excel, Power BI, Azure Machine Learning, AWS SageMaker, Google Cloud AI Platform, and IBM Watson Studio. You should compare the features, benefits, and drawbacks of each option, and select the one that suits your needs and goals.

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3 Learn by doing

The best way to learn machine learning is by doing it. You should start with simple and well-known datasets, such as the Iris, Titanic, or Boston Housing datasets, and apply the basic steps of machine learning, such as data exploration, data preprocessing, data modeling, and data evaluation. You should experiment with different algorithms, parameters, and techniques, and compare the results and performance. You should also document your process, findings, and learnings, and share them with others.

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Deepak Vittal

Technology Sales, Solutions & Partner Engineering Leader - Data & Analytics, AI, Embedded, Modern BI | Empower enterprises and software vendors to embed Data, BI and AI capabilities and launch Data products Faster!
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A DW professional has a good foundation on database technologies be it tables, Indexes, caching, relationships, etc. What they might know intimately is the deep knowledge of data. So, understanding the data by taking one step towards understanding the business is a good start. They also can volunteer to work on data science projects at the company so they can learn by doing and supplement by online courses and community forums.

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4 Follow online courses and resources

Another step is to follow online courses and resources that can teach you the theory and practice of machine learning. There are many free and paid courses and resources available, covering different levels, topics, and domains of machine learning. Some of the popular ones include Coursera's Machine Learning by Andrew Ng, Udemy's Machine Learning A-Z, Kaggle's Learn Machine Learning, and DataCamp's Machine Learning Tracks. You should choose the courses and resources that match your interests and objectives.

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5 Join online communities and events

The last step is to join online communities and events that can help you network, learn, and grow as a machine learning practitioner. There are many online communities and events that cater to different aspects, fields, and levels of machine learning. Some of the popular ones include Stack Overflow, Reddit, Quora, Medium, LinkedIn, Meetup, Kaggle, DataHack, and Hackathons. You should participate in the discussions, questions, articles, challenges, and competitions that can enhance your skills and knowledge.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the first steps for a data warehousing professional to learn machine learning?

1

2

3

4

5

6

1 Understand the basics

2 Choose a tool or platform

3 Learn by doing

4 Follow online courses and resources

5 Join online communities and events

6 Here’s what else to consider

Data Warehousing

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What are the first steps for a data warehousing professional to learn machine learning?

1

2

3

4

5

6

1 Understand the basics

2 Choose a tool or platform

3 Learn by doing

4 Follow online courses and resources

5 Join online communities and events

6 Here’s what else to consider

Data Warehousing

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