How to Learn Machine Learning

APRIL 26, 2025

The field of data science is now one of the most preferred and lucrative career options available in the area of data because of the increasing dependence on data for decision-making in businesses, which makes the demand for data science hires peak.

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Data Science Dojo

JULY 7, 2023

Data Science intertwines statistics, problem-solving, and programming to extract valuable insights from vast data sets. This discipline takes raw data, deciphers it, and turns it into a digestible format using various tools and algorithms. Tools such as Python, R, and SQL help to manipulate and analyze data.

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Data Science Dojo

MAY 18, 2023

Distributed System Design for Data Engineering: This talk will provide an overview of distributed system design principles and their applications in data engineering. Getting Started with SQL Programming: Are you starting your journey in data science?

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Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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Smart Data Collective

NOVEMBER 8, 2020

This explains the current surge in demand for data engineers, especially in data-driven companies. That said, if you are determined to be a data engineer , getting to know about big data and careers in big data comes in handy. Similarly, various tools used in data engineering revolve around Scala.

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Pickl AI

DECEMBER 25, 2024

Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Techniques such as data cleansing, aggregation, and trend analysis play a critical role in ensuring data quality and relevance. Data Science, however, uses predictive and prescriptive solutions.

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Dataconomy

MARCH 19, 2025

Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics. Data engineering lays the groundwork by managing data infrastructure, while data preparation focuses on cleaning and processing data for analysis.

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Pickl AI

JUNE 5, 2025

In the Indian context, data scientists often work in dynamic environments such as IT services, fintech, e-commerce, healthcare, and telecom sectors. They are expected to be versatile, handling everything from data engineering and exploratory analysis to deploying machine learning models and communicating insights to business stakeholders.

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Engineering 52

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AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. While knowing Python, R, and SQL are expected, you’ll need to go beyond that.

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Dataconomy

JULY 24, 2023

Concepts such as linear algebra, calculus, probability, and statistical theory are the backbone of many data science algorithms and techniques. Programming skills A proficient data scientist should have strong programming skills, typically in Python or R, which are the most commonly used languages in the field.

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DataSeries

AUGUST 15, 2024

Enrich data engineering skills by building problem-solving ability with real-world projects, teaming with peers, participating in coding challenges, and more. Globally several organizations are hiring data engineers to extract, process and analyze information, which is available in the vast volumes of data sets.

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Data Science Dojo

JULY 3, 2024

Data Visualization : Techniques and tools to create visual representations of data to communicate insights effectively. Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Ensure that the bootcamp of your choice covers these specific topics.

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Applied Data Science

AUGUST 2, 2021

Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team? The Data Engineer Not everyone working on a data science project is a data scientist.

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Data Science 101

JUNE 10, 2019

Data Scientists. Data scientists are the bridge between programming and algorithmic thinking. A data scientist can run a project from end-to-end. They can clean large amounts of data, explore data sets to find trends, build predictive models, and create a story around their findings. Data Engineers.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. NLTK is appreciated for its broader nature, as it’s able to pull the right algorithm for any job. Knowing some SQL is also essential.

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ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners tend to do more than just create algorithms all day. First, there’s a need for preparing the data, aka data engineering basics. Which of these related skills in machine learning are important to you? As the chart shows, two major themes emerged.

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Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. What do machine learning engineers do? In the context of a business, machine learning engineers are responsible for creating bots that are utilized for chat purposes or data collection.

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Data Science Dojo

OCTOBER 10, 2023

These events often showcase how AI is being practically applied across diverse sectors – from enhancing healthcare diagnostics to optimizing financial algorithms and beyond. However, in previous iterations of the summit, speakers have included prominent voices in data engineering and analytics. Link to event -> Live!

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

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IBM Journey to AI blog

JULY 6, 2023

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention.

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Pickl AI

JANUARY 13, 2025

Machine Learning Engineer Machine Learning Engineers develop algorithms and models that enable machines to learn from data. Strong understanding of data preprocessing and algorithm development. They employ statistical methods and machine learning techniques to interpret data.

AI 52

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AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon CloudWatch for anomaly detection Amazon CloudWatch supports creating anomaly detectors on specific Amazon CloudWatch Log Groups by applying statistical and ML algorithms to CloudWatch metrics. Use AWS Glue Data Quality to understand the anomaly and provide feedback to tune the ML model for accurate detection.

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AWS Machine Learning Blog

APRIL 19, 2023

We use Amazon SageMaker to train a model using the built-in XGBoost algorithm on aggregated features created from historical transactions. In our use case, we show how using SQL for aggregations can enable a data scientist to provide the same code for both batch and streaming. The application is written using Apache Flink SQL.

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ODSC - Open Data Science

SEPTEMBER 25, 2023

Tapping into these schemas and pulling out machine learning-ready features can be nontrivial as one needs to know where the data entity of interest lives (e.g., customers), what its relations are, and how they’re connected, and then write SQL, python, or other to join and aggregate to a granularity of interest.

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DataRobot

FEBRUARY 22, 2021

Specifically, 10X Academy students use DataRobot to develop the following skills: Problem framing Effectively working with Data Exploring data Modeling setups Feature engineering Developing algorithms Evaluating and understanding models Understanding bias, trust, and ethics Deploying models Time series modeling Working with multi-modal data.

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ODSC - Open Data Science

AUGUST 15, 2023

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Free and paid passes are available now–register here.

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Dataconomy

JULY 28, 2023

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis.

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Dataconomy

APRIL 27, 2023

They are also designed to handle concurrent access by multiple users and applications, while ensuring data integrity and transactional consistency. Examples of OLTP databases include Oracle Database, Microsoft SQL Server, and MySQL. Final words Back to our original question: What is an online transaction processing database?

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ODSC - Open Data Science

APRIL 3, 2023

While a data analyst isn’t expected to know more nuanced skills like deep learning or NLP, a data analyst should know basic data science, machine learning algorithms, automation, and data mining as additional techniques to help further analytics. Cloud Services: Google Cloud Platform, AWS, Azure.

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Dataconomy

JULY 25, 2023

It combines techniques from mathematics, statistics, computer science, and domain expertise to analyze data, draw conclusions, and forecast future trends. Data scientists use a combination of programming languages (Python, R, etc.), Ethical considerations: Data scientists must be mindful of the ethical implications of their work.

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Dataversity

JUNE 3, 2021

First and foremost, what, exactly, is Data Science? Data Science is a multidisciplinary field that uses processes, algorithms, and systems to obtain various insights coming from both structured and unstructured data. It is related to data mining, machine learning, and big data.

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Pickl AI

AUGUST 15, 2023

Coding Skills for Data Analytics Coding is an essential skill for Data Analysts, as it enables them to manipulate, clean, and analyze data efficiently. Programming languages such as Python, R, SQL, and others are widely used in Data Analytics. Ideal for academic and research-oriented Data Analysis.

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ODSC - Open Data Science

APRIL 24, 2023

We use, and recommend, using a query engine in ML pipelines to save time and costs. Here you can find an example SQL query, with parameters we used in our experiments. The distances are then added to a distance matrix which is sent to a clustering algorithm. The algorithm returns clusters — in our example the detected botnets.

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