# Tag: Beginners (41)

**Getting Started with Machine Learning in One Hour!**- Nov 1, 2017.

Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.**Top 6 errors novice machine learning engineers make**- Oct 30, 2017.

What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.**Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning**- Oct 28, 2017.

This is a short post for beginners learning neural networks, covering several essential neural networks concepts.**Top 10 Machine Learning Algorithms for Beginners**- Oct 20, 2017.

A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.

**Introduction to Neural Networks, Advantages and Applications**- Jul 25, 2017.

Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.**Getting Started with Python for Data Analysis**- Jul 5, 2017.

A guide for beginners to Python for getting started with data analysis.

**Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2**- Jul 1, 2017.

Here are deep learning examples and demos you can just download and run, including Spotify Artist Search using Speech APIs, Symbolic AI Speech Recognition, and Algorithmia API Photo Colorizer.**Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners**- Jun 26, 2017.

Here are deep learning demos and examples you can just download and run. No Math. No Theory. No Books.**Introduction to Correlation**- Feb 22, 2017.

Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.**The Gentlest Introduction to Tensorflow – Part 4**- Feb 22, 2017.

This post is the fourth entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner, and focuses on logistic regression for classifying the digits of 0-9.**The Gentlest Introduction to Tensorflow – Part 3**- Feb 21, 2017.

This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.**Data Science Basics: Power Laws and Distributions**- Dec 21, 2016.

Power laws and other relationships between observable phenomena may not seem like they are of any interest to data science, at least not to newcomers to the field, but this post provides an overview and suggests how they may be.**Data Science Basics: What Types of Patterns Can Be Mined From Data?**- Dec 14, 2016.

Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.**Introduction to Machine Learning for Developers**- Nov 28, 2016.

Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.**Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed**- Nov 25, 2016.

Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.**Data Science and Big Data, Explained**- Nov 14, 2016.

This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.**How to Rank 10% in Your First Kaggle Competition**- Nov 9, 2016.

This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.**Data Science Basics: An Introduction to Ensemble Learners**- Nov 8, 2016.

New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.**Data Science 101: How to get good at R**- Nov 1, 2016.

Everybody talks about R programming, how to learn, how to be good at it. But in this article, Ari Lamstein tells us his story about why and how he started with R along with how to publish, market and monetise R projects.**Learn Data Science for Excellence and not just for the Exams**- Oct 31, 2016.

Are you currently pursuing your masters in Data Science? Overwhelmed with Buzzwords and Information? Don’t know where and how to start your study? Then start with this article and a starter kit provided, but learn it for excellence and not just for the exams.**A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18!**- Oct 20, 2016.

This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.**Data Science Basics: Data Mining vs. Statistics**- Sep 28, 2016.

As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes.**Data Science Basics: 3 Insights for Beginners**- Sep 22, 2016.

For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.**Machine Learning in a Year: From Total Noob to Effective Practitioner**- Sep 19, 2016.

Read how the author went from self-described total machine learning noob to being able to effectively use machine learning effectively on real world projects at work... all within a year.**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2**- Sep 8, 2016.

This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1**- Sep 6, 2016.

Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.**The Gentlest Introduction to Tensorflow – Part 2**- Aug 19, 2016.

Check out the second and final part of this introductory tutorial to TensorFlow.**The Gentlest Introduction to Tensorflow – Part 1**- Aug 17, 2016.

In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.**A Beginner’s Guide to Neural Networks with R!**- Aug 11, 2016.

In this article we will learn how Neural Networks work and how to implement them with the R programming language! We will see how we can easily create Neural Networks with R and even visualize them. Basic understanding of R is necessary to understand this article.**KDnuggets™ News 16:n29, Aug 10: Data Science for Beginners: Fantastic series; Automating Data Science Contest Winners**- Aug 10, 2016.

Data Science for Beginners: Fantastic Introductory Video; Contest 2nd Place: Automating Data Science; Contest Winner: Winning the AutoML Challenge with Auto-sklearn; Reinforcement Learning and the Internet of Things.**Getting Started with Data Science – R**- Aug 3, 2016.

A great introductory post from DataRobot on getting started with data science in R, including cleaning data and performing predictive modeling.**Data Science for Beginners: Fantastic Introductory Video Series from Microsoft**- Aug 3, 2016.

The remaining videos in Microsoft's Data Science for Beginners video series are available now. Have a look at what they offer.**KDnuggets™ News 16:n28, Aug 3: Data Science Stats 101; Understanding NoSQL Databases; Core of Data Science**- Aug 3, 2016.

Data Science Statistics 101; 7 Steps to Understanding NoSQL Databases; The Core of Data Science; Data Science for Beginners 2: Is your data ready?**Getting Started with Data Science – Python**- Aug 1, 2016.

A great introductory post from DataRobot on getting started with data science in the Python ecosystem, including cleaning data and performing predictive modeling.**Data Science Statistics 101**- Jul 28, 2016.

Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.**Data Science for Beginners 2: Is your data ready?**- Jul 28, 2016.

This second video and write-up in the Data Science for Beginners series discusses what is required of your data before it can be useful.**Data Science for Beginners 1: The 5 questions data science answers**- Jul 26, 2016.

A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.**A Pocket Guide to Data Science**- Apr 11, 2016.

A pocket guide overview of how to get started doing data science, with a focus on the practical, and with concrete steps to take to get moving right away.**Introduction to Random Forests for Beginners – free ebook**- Mar 6, 2014.

Random Forests is of the most powerful and successful machine learning techniques. This free ebook will help beginners to leverage the power of Random Forests.**Data Mining for Beginners Boot Camp, Salford video series**- Jan 29, 2014.

This series shows how to easily apply SPM software suite to your predictive modeling projects, using a modern banking application as an example. This series is at the beginner level, and is perfect for first-time users or for those who need a refresher course in model building and data analysis.**Top KDnuggets tweets, Jan 27-28: Dilbert takes on #BigData Analysis and Salaries; Free Tutorial, Data Analytics for Beginners**- Jan 29, 2014.

Dilbert takes on #BigData Analysis and Salaries of Top Performers. Hilarious! ; Free Tutorial - Data Analytics for Beginners: Part 1 - Installing R and RStudio; Part 2: Data Cleaning; A New Science of Cities emerges from Mobile Phone Data Analysis.