Here’s what I learned along the way — Thanks to a group of dedicated data science boot camp grads, Zoom, and the World Wide Web, I have listened to and answered too many data science interview practice questions to count. These past months of practice have taught me a lot, not only about data science, but also about…

Learn the code and math behind Euclidean Distance, Cosine Similarity and Pearson Correlation to power recommendation engines — I’ve deployed a few recommender systems into production. Not surprisingly, the simplest turned out to be the most effective. At the core of most recommender systems lies collaborative filtering. And at the core of collaborative filtering is document similarity. We’ll walk through 3 algorithms for calculating document similarity.

Towards more efficient natural language processing — Natural language processing is perhaps the most talked-about subfield of data science. It’s interesting, it’s promising, and it can transform the way we see technology today. Not just technology, but it can also transform the way we perceive human languages. Natural language processing has been gaining too much attention and…

A practical guide for time series data manipulation in Python Pandas — Time series data is one of the most common data types in the industry and you will probably be working with it in your career. Therefore understanding how to work with it and how to apply analytical and forecasting techniques are critical for every aspiring data scientist. In this series…

Best machine learning algorithms for beginners with coding samples in Python. Launch the coding samples with Google Colab — Author(s): Pratik Shukla, Roberto Iriondo, Sherwin Chen Last updated April 14, 2021 Join us ↓ | Towards AI Members | The Data-driven Community Join Towards AI, by becoming a member, you will not only be supporting Towards AI, but you will have access to…members.towardsai.net

A practical guide for time series data analysis in Python Pandas — Time series data is one of the most common data types in the industry and you will probably be working with it in your career. Therefore understanding how to work with it and how to apply analytical and forecasting techniques are critical for every aspiring data scientist. In this series…

A quick guide to hyperparameter tuning utilizing Scikit Learn’s GridSearchCV, and the bias/variance trade-off — Before we dive in, let’s start with a quick definition. In machine learning, a hyperparameter (sometimes called a tuning or training parameter) is defined as any parameter whose value is set/chosen at the onset of the learning process. Whereas other parameter values are computed during training.

I started an intuitive on LinkedIn in May 2022 in which I am posting daily data science interview questions and the answers I got on each post I summarize them and post on the next day in another post. The questions are prepared by me, in addition to others that…

Learn the most important data science skill in 15 weeks — Be sure to subscribe to never miss another article on data science guides, tricks and tips, life lessons, and more! Introduction As I work more and more in the corporate world as a data scientist, I am increasingly convinced that mastering SQL is essential to have a successful career. …

Connect the dots… — Welcome back peeps. This post( part 5) is all about Data Science and Machine Learning Projects that you can build to practically understand the concepts. Some of the other best Series — 30 Days of Natural Language Processing ( NLP) Series 30 days of Data Engineering with projects Series 60 days of Data Science and ML Series…