📅 Cohort opens Friday, May 9th | 2 weeks live + recorded | After week 2 you choose: continue the full program or walk away
What's included
Career outcome: Data Scientist (full program)
Everything you need to know if ML is right for you
These two weeks cover the exact first two chapters of our 3-month ML program.
Same curriculum, same instructor, same project.
Same curriculum, same instructor, same project.
- Real Kaggle Project
Build and submit your first ML model on Kaggle - House Prices Prediction - a dataset used in industry interviews.
- Full Dev Setup
VS Code, virtual environments, Git/GitHub, Jupyter - you'll have a working local ML environment from day one.
- Python + Pandas + EDA
Data wrangling, visualization, statistics. Skills every ML engineer uses daily - taught through hands-on practice.
- Linear Regression + Scikit-Learn
Build your first predictive model with multiple variables, gradient descent, and proper train/test splits.
- Live Mentor Support
Learn alongside a cohort with Sergii, our senior ML instructor, available to review your code and answer questions.
- No Lock-in
After 2 weeks you decide. Join the full 3-month program or stop - your call. No guilt, no catch.
Day-by-day breakdown
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Week 1: Intro to ML, Basic EDA, First Kaggle Submit
- Project: House Prices Prediction (Kaggle)
Day 1: Environment Setup & Intro to ML
- Setup: VS Code, venv, Git/GitHub, Jupyter Notebooks
- Intro to ML: What is ML, Types of ML (Supervised, Unsupervised)
- Learning Paradigms overview
- Problem Framing & ML Workflow overview
- Pandas: DataFrames & Series, Reading & Writing Data (CSV, Excel), Data Selection & Indexing, Filtering & Querying
- Pandas: Descriptive Statistics & Aggregation, Joining & Merging DataFrames
- Core Statistical Concepts (mean, median, std, variance, distributions)
- Data Profiling (dataset shape, types, missing values, basic stats)
- Data Visualization with Matplotlib & Seaborn (histograms, scatter plots, box plots, heatmaps, distributions, correlation analysis)
- Kaggle Platform setup, EDA for House Prices dataset, first baseline model
Week 2: Intro to Regression, Linear Regression
- Project: House Prices Prediction (continued)
Day 1: Linear Regression Fundamentals
- Linear Regression algorithm (1 variable)
- Model Concepts: Cost Function, Loss Function
- Cost Function for Linear Regression (MSE)
- Gradient Descent — conceptual understanding (iteratively minimizing loss)
- Learning Rate
- Gradient Descent Variants (Batch, Mini-Batch, SGD)
- Linear Regression with multiple variables (using scikit-learn)
- Data Splitting: Train-Val-Test Split
- Data Leakage Prevention - common leakage mistakes with examples
- Apply linear regression to House Prices. Experiment with different features. Practice proper train/test splits without leakage.
The full journey
Your path to AI Engineer
These 2 weeks are the beginning. After you experience the program, you decide whether to continue.
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2-Week Trial
Weeks 1–2 of ML Level 1 for just $50
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1
Level 1
Machine Learning
Full 3-month ML foundations program
→
2
Level 2
Deep Learning
Neural nets — junior to mid-level roles
→
3
Level 3
AI Engineering
Full-stack AI pro + internship guarantee
🗓️ Month 1: Tabular Data Mastery & Foundations
- Focus: Mastering the data science toolkit, EDA, and core regression models.
- Project: House Prices - Advanced Regression Techniques
- You'll build models to predict home values, mastering feature engineering and regression on a complex, real-world Kaggle dataset.
- (Skills: EDA, Feature Engineering, Regression Models, XGBoost, Kaggle Competitions)
Main Project Goal: Build the v0.1 command-line version with simple, rule-based responses.
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