Guides, tutorials, & news from Codistwa

Here’s the hard truth: Learning alone won’t make you visible. Every day, smart women like you are spending hours on Python, ML theory, and YouTube playlists. And still feeling like imposters. Why? Because most of what we’re taught to do is passive learning. Not strategic execution.

Whether you’re a beginner just starting or a seasoned professional, managing your timeline is crucial for success. So, here are 5 powerful tips that will keep your project on track and running smoothly.

Understanding machine learning failures helps us improve our systems and make them more robust. Machine learning failures happen due to poor data, overfitting, underfitting, or misalignment with real-world scenarios.

Whether you’ve trained a simple regression model or a complex neural network, getting it to work reliably in the real world can feel daunting. In this blog, I'll guide you through the best practices, tools, and strategies to make this transition smooth and successful.

Let's tackle a crucial topic that every machine learning enthusiast and professional must navigate—selecting the right projects. In the rapidly evolving field of machine learning (ML), it’s essential to find projects that not only ignite your passion but also make a significant impact. In this blog, I’ll guide you through the process of choosing ML projects that align with both your personal interests and the broader needs of the real world.

In today’s rapidly evolving world of artificial intelligence (AI) and machine learning (ML), staying current can feel overwhelming. New research, tools, and techniques are constantly emerging, making it challenging to keep up. However, with the right strategies, you can not only stay ahead but also continue to grow in your career. In this blog, I’ll share practical tips and resources to help you navigate this fast-moving landscape effectively.

If you’ve ever felt overwhelmed by the tedious process of data wrangling or frustrated by the complexities of sourcing and cleaning data, this article is for you. Today, we will transform how you approach data in your machine learning projects, helping you streamline your workflow so that you can focus on building powerful models.

Machine learning models can sometimes feel like black boxes, making it challenging to understand how they make decisions and whether we can trust their outputs. Today, we'll explore strategies and tools that will help you interpret your models effectively, building confidence in their predictions and ensuring that they are reliable and trustworthy.

Let's explore one of the most important aspects of mastering machine learning—striking the right balance between understanding the underlying theories and applying them in practice. In machine learning, both theoretical knowledge and practical skills are essential, and finding the right balance between the two can be challenging. Today, I’ll guide you through strategies for integrating theory and practice effectively, so you can become a well-rounded machine learning practitioner.

Today, we're diving into a topic that's been on many minds lately: Learning Data Science. You might have seen the buzz around it, maybe even tried to tackle it yourself. But here's the thing - learning Data Science doesn't need to be difficult! If you're finding it tough, you're not alone. But it might just be that you haven't found the right method yet. So, let's talk about how you can make your Data Science journey smoother and more enjoyable.

Today, we're diving into the world of machine learning and debunking some common myths that might be holding you back. If you've ever felt intimidated by the idea of learning machine learning, this post is for you. So, let's jump right in!

If you've been thinking about diving into the world of machine learning but feel held back by common misconceptions, this post is for you. We're here to debunk three pervasive myths about learning machine learning that might be stopping you from starting or progressing in your journey. From the supposed complexity of the field to the idea that you need a strong technical background, we’re breaking it all down to show you why machine learning is more accessible than you think.

If you want to succeed, getting started with data science should be a no-brainer. Let me guide you with these simple tips.

Kaggle is a data science platform acquired by Google in 2017. So it includes competitions where you can win money. And do you see the famous recommendation algorithm of Netflix? Well, it was a competition on this very platform, and the prize was $1 million, which is pretty motivating, right? But first and foremost, Kaggle is educational, so you can find tutorials, datasets, and free competitions. Also, there is a big community to help you if you're stuck.

While tutorials offer an accessible entry point, there's a hidden trap known as "tutorial hell." In this article, we’ll explore what tutorial hell is and why diversifying your learning beyond just machine learning tutorials is essential for your growth and long-term success in the field.

Machine learning is a powerful tool that can solve a wide range of problems, but it's not magic. The success of your machine learning models heavily depends on how well you prepare and execute the process. If you're not focusing on these three critical aspects, you're likely wasting your time.

In today’s highly competitive data science job market, having a standout portfolio and an impressive resume is more critical than ever. Yet, many aspiring data scientists struggle to create a portfolio and resume that truly showcase their skills, achievements, and potential. If you’re feeling frustrated with your data science portfolio and resume, don’t worry—this article will guide you through the common pitfalls and provide actionable solutions to help you elevate your profile and land your dream job.

Mastering technical data science interviews requires a blend of technical knowledge, problem-solving skills, effective communication, and a calm demeanor under pressure.

Machine learning (ML) is often portrayed as a powerful, almost magical tool capable of solving complex problems across industries. While experts and tutorials focus on the basics of building models and the exciting potential of ML, there are crucial aspects of the process that don’t always make it into the spotlight. Here are six essential things the experts don’t always tell you about machine learning—and why understanding them is key to your success.

Learning data science can be a daunting task and very overwhelming if we don't have a plan. One day I heard about the Feynman Technique, named after the renowned physicist Richard Feynman.