Level 1
Machine Learning
Career outcome: Data Scientist
Prerequisites
- Python
- Python fundamentals
- Python data structures (lists, dictionaries, sets, tuples)
- List/dictionary comprehensions and functional programming tools
- OOP principles in Python (classes, inheritance, encapsulation)
- Virtual environments (venv, conda)
- Package management
- Git (branching, merging, pull requests)
- IDE (VSCode or another IDE of your preference)
- 15-20 hours/week commitment
If you are unfamiliar with some of these topics or want to refresh your knowledge,
these free resources linked below cover all prerequisites
these free resources linked below cover all prerequisites
If you’re a complete beginner in programming, start with one of these:
- AI Python for Beginners course by DeepLearning.AI (~10h)
- Intro to Programming and Python courses by Kaggle (~10h)
- Python tutorial by Spark Code Hub
- A Byte of Python book by Swaroop Chitlur
- learnpython.org interactive tutorial by Ron Reiter
- Python for Beginners tutorial by Beau Carnes
- Object Oriented Programming with Python tutorial by JimShapedCoding
- Intermediate Python tutorial by Patrick Loeber
- Python Virtual Environments: A Primer by Martin Breuss
- Using Python's pip to Manage Your Projects' Dependencies by Philipp Acsany
For a basic understanding, check one of these introductory resources:
- Git & GitHub Crash Course by Kalob Taulien (Udemy)
- GitHub and Git Tutorial for Beginners by Abid Ali Awan (DataCamp)
- Pro Git book by Scott Chacon and Ben Straub (check sections: Getting Started, Git Basics, Git Branching, Distributed Git, GitHub)
- Atlassian Git tutorials (check sections: Beginner, Getting started, Collaborating workflows)
Official tutorials for Visual Studio Code:
Machine Learning Path — 3 Levels to Master AI
Machine Learning is a journey with three levels:
- Level 1: Data ScienceLearn how to understand and organize data so computers can make sense of it.
- Level 2: Classical ML MasteryTeach computers to learn from data and make smart predictions.
- Level 3: LLMs & AgentsStep into the world of modern AI, where machines can think, talk, and act on their own.
Course Program
Month 1: The Dawn of Intelligence: ML Foundations
Enter the ML universe and master the fundamentals
Mastering the data science toolkit, Exploratory data analysis (EDA), and core classical ML algorithms.
House Prices Prediction
- You'll build models to predict home values, mastering feature engineering and regression on a complex, real-world Kaggle dataset.
EDA, Pandas, Matplotlib, Scikit-learn, Linear Models, Kaggle Competitions.
Build the v0.1 command-line version with simple, rule-based responses.
Month 2: Classical ML Mastery & Unveiling Hidden Structures
Master advanced techniques and explore Unsupervised Learning algorithms
Iterative model improvement and discovering hidden patterns in data.
■ House Prices Prediction - Advanced Optimization
■ Credit Card Customer Segmentation
- You'll return to your house price predictor and improve it using advanced ensemble methods, sophisticated feature engineering, and hyperparameter optimization to climb the Kaggle leaderboard.
■ Credit Card Customer Segmentation
- You'll act as a data scientist for a bank, using clustering algorithms to find distinct marketing groups in a dataset of customer spending habits.
Decision Trees, Ensemble Methods, Stacking/Blending, Feature Engineering, Hyperparameter Optimization, K-Means, PCA.
v0.2, with a classical ML model to understand user travel intents.
Month 3: Time Series Oracle & Unveiling Hidden Structures
Conquer temporal data and explore Unsupervised Learning algorithms
Mastering time series forecasting and discovering hidden patterns in data
Store Sales Time Series Forecasting
■ You'll tackle a classic business problem by predicting future sales for a retail chain, mastering temporal feature engineering, and comparing
various forecasting models.
Credit Card Customer Segmentation
■ You'll act as a data scientist for a bank, using clustering algorithms to
find distinct marketing groups in a dataset of customer spending habits.
■ You'll tackle a classic business problem by predicting future sales for a retail chain, mastering temporal feature engineering, and comparing
various forecasting models.
Credit Card Customer Segmentation
■ You'll act as a data scientist for a bank, using clustering algorithms to
find distinct marketing groups in a dataset of customer spending habits.
Decision Trees, Ensemble Methods, Stacking/Blending, Feature Engineering, Hyperparameter Optimization, K-Means, PCA.
Time Series Analysis, Forecasting, Prophet, K-Means, PCA
🗓️ 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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