Are you new to the world of data science and machine learning? Welcome to your ultimate guide and starting point, whether you’re looking to break into the industry, learn something new, or hone your current skills. The Back to Basics: Getting Started in 5 Steps series is all you need and is designed to transform complex concepts into simple, straightforward knowledge.
As part of KDnuggets’ 30-year journey in data science, machine learning, and artificial intelligence, the team has come together to write a variety of articles so you can imbibe with all possible knowledge.
When you start something new, it is always difficult to get started. The KDnuggets team frees you from this weight with our Back to Basics: Getting Started in 5 Steps series, which includes:
- Python Data Structures
- SQL
- Scikit-learn
- PyTorch
- Google Cloud Platform
So let’s get straight to the point…
This tutorial covers the fundamental data structures of Python: lists, tuples, dictionaries, and sets. Discover their characteristics, use cases and practical examples, all in 5 steps.
When it comes to learning to program, regardless of the particular programming language you use for that task, you find that there are a few major topics of your newly chosen discipline to which most of what you are exposed could be categorized.
A few of these, in the general order of understanding, are syntax (the vocabulary of the language); commands (putting vocabulary together in a useful way); flow control (how we guide the order of order execution); algorithms (the steps we take to solve specific problems…how did that word become so confusing?); and, finally, data structures (the virtual storage repositories that we use for data manipulation when running algorithms (which are, again… a series of steps).
Learn the 5 steps: Get started with Python data structures in 5 steps
This comprehensive SQL tutorial covers everything from setting up your SQL environment, to mastering advanced concepts like joins and subqueries, to optimizing query performance. With step-by-step examples, this guide is perfect for beginners looking to improve their data management skills.
When it comes to managing and manipulating data in relational databases, Structured Query Language (SQL) is the biggest name in the game. SQL is a major domain-specific language that serves as a cornerstone to database management and provides a standardized way to interact with databases.
With data driving decision-making and innovation, SQL remains an essential technology that requires high-level attention from data analysts, developers, and data scientists.
Learn the 5 steps: Get started with SQL in 5 steps
This tutorial provides a comprehensive hands-on overview of machine learning with Scikit-learn. Readers will learn key concepts and techniques including data preprocessing, model training and evaluation, hyperparameter tuning, and compiling ensemble models for improved performance.
When you learn to use Scikit-learn, we obviously need to have an existing understanding of the underlying machine learning concepts, because Scikit-learn is nothing more than a practical tool for implementing machine learning principles and related tasks. Machine learning is a subset of artificial intelligence that allows computers to learn and improve from experience without being explicitly programmed. Algorithms use training data to make predictions or decisions by discovering patterns and insights.
Learn the 5 steps: Get started with Scikit-learn in 5 steps
This tutorial provides an in-depth introduction to machine learning using PyTorch and its high-level wrapper, PyTorch Lightning. The article covers essential steps from setup to advanced topics, providing a hands-on approach to building and training neural networks, and emphasizing the benefits of using Lightning.
PyTorch is a popular open-source machine learning framework based on Python and optimized for GPU-accelerated computing. Originally developed by Meta AI in 2016 and now part of the Linux Foundation, PyTorch has quickly become one of the most widely used frameworks for research and deep learning applications.
Unlike some other frameworks like TensorFlow, PyTorch uses dynamic computational graphs which allow for greater flexibility and debugging capabilities.
Learn the 5 steps: Getting started with PyTorch in 5 steps
Learn the essentials of Google Cloud Platform for data science and ML, from account setup to model deployment, with hands-on sample projects.
This article aims to provide a step-by-step overview of getting started with Google Cloud Platform (GCP) for data science and machine learning. We will provide an overview of GCP and its key analytics features, review account setup, explore essential services such as BigQuery And Online storageCreate a sample data project and use GCP for machine learning.
Whether you’re new to GCP or looking for a quick refresher, read on to learn the basics and get started with Google Cloud.
Learn the 5 steps: Get started with Google Cloud Platform in 5 steps
This Back to Basics: Getting Started in 5 Steps series will have enlightened you on the fundamental tools used in data science. You will have familiarized yourself with the basics of Python, SQL, machine learning with Scikit-learn and PyTorch, but you will also have ventured into Google Cloud Platform.
The path to data mastery does not end there, it is an ongoing journey that requires you to continually learn new skills and the tools acquired to be proficient.
Keep an eye on KDnuggets for more information, advanced guides, and support from a community as passionate about data science as you are.
Nisha Arya is a Data Scientist and independent technical writer. She is particularly interested in providing data science career advice or tutorials and theoretical knowledge around data science. She also wants to explore the different ways in which artificial intelligence is/can benefit the longevity of human life. A passionate learner, looking to expand her technical knowledge and writing skills, while helping to mentor others.
