Machine Learning

Machine learning can be overwhelming because it is a complex and rapidly-evolving field that involves a wide range of concepts, algorithms, tools, and techniques. The sheer breadth of the field can make it challenging to know where to begin, and even experienced practitioners may struggle to keep up with the constant influx of new developments and advancements.

If you want to master the field of AI and machine learning, it’s important to start with a solid foundation in the fundamental concepts and techniques. Below you will find some hints that will help you to get started.

How to Get Started

Here are some tips to get started with machine learning:

  1. Fundamentals: Start by learning about the fundamental concepts and principles of machine learning, such as supervised and unsupervised learning, different types of algorithms, and how to evaluate the performance of a machine learning model. There is plenty of material available online, and some of these topics may even be covered in this blog.
  2. Frameworks: Once you have a good understanding of the concepts, familiarize yourself with the tools and frameworks commonly used in machine learning. Depending on your preferred programming language, you’ll need to get comfortable with libraries and functions like Scikit-learn, TensorFlow, and PyTorch. It’s best to focus on one language and framework initially and understand the functions it provides.
  3. Practice: Next, practice your skills by applying machine learning algorithms to real-world data sets. Start with a simple project and experiment with different algorithms and performance metrics to compare their effectiveness. You can use Jupyter notebooks to analyze and visualize data and share your work with others.
  4. Networking: Join online communities and forums like Kaggle or Reddit to learn from others and get feedback on your work. Don’t be afraid to ask questions and share your progress with the community. You can also take online courses or attend workshops to learn from experts and gain practical experience.
  5. Courses: As you gain more experience, you can progress to more complex projects and datasets. Focus on one area at a time and build your skills in that area before moving on to another field. It’s essential to stay up-to-date with the latest developments in the field by reading research papers and following leading experts and practitioners on social media.
  6. Stay up-to-date with the latest developments in the field by reading research papers and staying active in the machine learning community.

Relataly Machine Learning Tutorials Archive

The page shows an overview of all Relataly Python machine learning tutorials with related tags and structured by category.


predictive maintenance python machine learning tutorial iot manufacturing
Algorithms | Classification (multi-class) | Cross-Validation | Data Visualization | Exploratory Data Analysis (EDA) | Gradient Boosting | Machine Learning | Manufacturing | Plotly | Predictive Maintenance | Python | Scikit-Learn | Seaborn | Yahoo Finance API

Predictive Maintenance: Predicting Machine Failure using Sensor Data with XGBoost and Python

Predictive maintenance is a game-changer for the modern industry. Still, it is based on a simple idea: By using machine … Read more

churn prediction python
Churn Prediction | Classification (two-class) | Data Science | Data Sources | Feature Permutation Importance | Hyperparameter Tuning | Machine Learning | Python | Random Decision Forests | Retail | Scikit-Learn | Seaborn | Use Cases

Customer Churn Prediction – Understanding Models with Feature Permutation Importance using Python

One of the primary goals of many service companies is to build solid and long-lasting relationships with their customers. Customers … Read more

Time-Series Forecasting

clustering stock markets machine learning cryptocurrencies blockchain bitcoin ethereum-min
Affinity Propagation (Clustering) | Clustering | Coinmarketcap API | Correlation | Covariance | Crypto Exchange APIs | Data Visualization | Dimensionality Reduction | Finance | Python | Scikit-Learn | Seaborn | Stock Market Forecasting | Time Series Forecasting

Uncover Hidden Patterns in Financial Markets using Affinity Propagation Clustering in Python

This article introduces affinity propagation – an unsupervised clustering technique that stands out from other clustering approaches by its capacity … Read more

feature engineering for stock market prediction, multivariate time series modelling midjourney relataly python tutorial
Algorithms | Feature Engineering | Finance | Keras | Machine Learning | Neural Networks | Python | Recurrent Neural Networks | Stock Market Forecasting | Tensorflow | Time Series Forecasting | Use Cases | Yahoo Finance API

Mastering Multivariate Stock Market Prediction with Python: A Guide to Effective Feature Engineering Techniques

Are you interested in learning how multivariate forecasting models can enhance the accuracy of stock market predictions? Look no further! … Read more


Agglomerative Clustering | Algorithms | Clustering | Customer Segmentation | Data Science | Data Visualization | Exploratory Data Analysis (EDA) | Finance | Insurance | Kaggle Competitions | Machine Learning | Marketing Automation | Python | Scikit-Learn | Seaborn | Telecommunications | Use Cases

Efficiently Segment Customers using Hierarchical Clustering in Python

Have you ever found yourself wondering how you can better understand your customer base and target your marketing efforts more … Read more

Anomaly Detection

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Neural Networks

API Tutorials

Distributed Computing


  • Florian Follonier

    Hi, I am Florian, a Zurich-based Cloud Solution Architect for AI and Data. Since the completion of my Ph.D. in 2017, I have been working on the design and implementation of ML use cases in the Swiss financial sector. I started this blog in 2020 with the goal in mind to share my experiences and create a place where you can find key concepts of machine learning and materials that will allow you to kick-start your own Python projects.