Machine Learning & AI
Hands-on AI and data analytics workshops — built around your team's real cases.
Ideal for teams that…
Hands-on AI and data analytics workshops — built around your team's real cases.
Understand the role of data in machine learning and principles of data preparation
Apply machine learning algorithms to solve practical problems
Learn the main types of machine learning and core algorithms in each area
Prepare and deliver a Proof of Concept (POC) project
What you should know before we start
- The training is based on Python and popular libraries such as pandas, NumPy, scikit-learn, PyTorch, and others.
- The training is delivered using Google Colaboratory.
- Participants only need a standard Google account (e.g., Gmail).
What we actually do
Day 1 – Data
- · Introduction: What is Machine Learning (ML)? Definition and key differences between traditional programming and ML History and evolution of ML, industry impact Types of learning: supervised, unsupervised, reinforcement
- · Data – EDA (Exploratory Data Analysis) & Preprocessing Why data matters in ML and where it comes from Exploratory data analysis: visualizations, descriptive statistics, outlier detection Data preparation: cleaning missing values, encoding categorical variables, scaling and normalization, train/test split Workshop: Data preprocessing and EDA with real datasets (Google Colab, Python)
Day 2 – Supervised Learning
- · Characteristics of supervised learning: problems solved, pros & cons
- · Regression Linear, polynomial, and logistic regression
- · Classification Decision trees, SVM, k-nearest neighbors (k-NN)
- · Evaluation metrics: MSE, precision, recall, F1, ROC curve, AUC
- · Workshop: Implementing supervised algorithms and building a POC with real datasets (Google Colab, Python)
Day 3 – Unsupervised Learning
- · Characteristics of unsupervised learning: problems solved, pros & cons
- · Clustering k-means, DBSCAN, hierarchical clustering
- · Dimensionality Reduction PCA, t-SNE
- · Workshop: Applying unsupervised learning algorithms and building a POC with real datasets (Google Colab, Python)
Day 4 – Neural Networks
- · Introduction: What are neural networks, applications, pros & cons
- · Basics Perceptrons, network architecture, activation functions, forward & backpropagation
- · Deep Learning Deep neural networks, CNNs for image analysis
- · Workshop: Implementing neural networks and building a POC with real datasets (Google Colab, Python)
From brief to retro in 30 days.
Brief & diagnosis
A call with the team lead + a short survey for participants. We define goals, gap and context.
Program customization
We adapt modules, case studies and code examples to your stack. Approval in 5 days.
Workshop
Trainer-led sessions, hands-on, code review. Mentor available between sessions too.
Retro + report
Outcome report for the team and lead. 30 days of consulting included.
Send a brief. We'll reply within 1 day.
After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.
Thank you!
We'll get back to you within 1 business day.
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