Lvl.2 -> Machine Learning Engineer
Pre-requisite: Lvl.1 Machine Learning Apprentice
Approx. Time Required: 2 months
✅ NEW! Join our Mentorship Program for this Career Path here. 👈
Begin your journey to becoming a Machine Learning Engineer in three months with our intensive courses. Start with Machine Learning A-Z, diving into practical exercises across various models in Python and R, equipping you with the skills to build and fine-tune a broad spectrum of machine learning solutions. Continue with Python for Statistical Analysis to gain proficiency in data analysis, learning to extract actionable insights through statistical testing and exploratory data analysis. The journey culminates with Machine Learning Level 2, focusing on advanced ensemble techniques like XGBoost, LightGBM, and CatBoost to tackle complex challenges with precision. This condensed learning path will arm you with critical machine learning and analytical skills, preparing you for advanced roles in data science.
Dive into the core of machine learning with practical exercises in both Python and R.
- Implement various machine learning models from simple linear regression to complex neural networks.
- Understand the theory and intuition behind algorithms and apply them to real-world scenarios.
- Study all the different branches of Machine Learning, from Regression to Classification, Clustering, Association Rule Learning for Market Basket Analysis, Reinforcement Learning, NLP, and Deep Learning.
You will get a comprehensive skill set to build, evaluate, and optimize machine learning models for a wide array of data science applications.
2. Python for Statistical Analysis
This course streamlines the journey through data analysis, offering a hands-on approach to understanding data, generating insights, and applying statistical principles. Designed for efficiency, it emphasizes practical examples and real-world applications.
- Master data loading, preparation, and outlier handling to ensure quality analysis.
- Dive into exploratory data analysis with practical examples on distributions, correlations, and more to uncover hidden patterns.
- Learn about characterizing data through statistical measures and distributions, enhancing your analytical precision.
- Get to grips with probability theory, sampling distributions, and the Central Limit Theorem to understand data behavior.
- Develop a solid foundation in hypothesis testing with real-world case studies, preparing you to make data-driven decisions.
Equipped with these skills, you’ll navigate the data science process more confidently, from initial analysis to deriving actionable insights. This condensed learning path enables quick mastery of essential techniques, setting a strong foundation for further exploration into data science and analytics.
3. Kirill’s podcast episode 771: Gradient Boosting (XGBoost, LightGBM, CatBoost)
Get a detailed technical overview of some of the most reliably used ML models in business & industry. Find out exactly what makes them so powerful! In this episode you will learn:
- All about decision trees
- All about ensemble models
- The historical importance of AdaBoost
- Gradient boosting for Regression & Classification
- Why XGBoost was a breakthrough
- How LightGBM is 20x faster
- How CatBoost is expert at Categorical inputs
Dive into the world of Gradient Boosting models with this hands-on, project-focused course. This course is your gateway to understanding and applying Gradient Boosting models, specifically focusing on XGBoost, LightGBM, and CatBoost for regression and classification problems.
- Learn the intuition and mathematics behind Decision Trees, Random Forests, and Gradient Boosting models.
- Master XGBoost by building models to tackle real-world regression and classification problems, understand tree pruning, learning rate adjustments, and advanced concepts like K-fold cross-validation and the bias-variance tradeoff.
- Explore LightGBM, leveraging histogram-based splits, exclusive feature bundling, and gradient-based one-side sampling to build efficient models with real case studies.
- Delve into CatBoost, discovering the power of target encoding, ordered boosting, and symmetric trees through hands-on projects designed to solve practical challenges.
Equipped with practical experience and an in-depth understanding of three of the most powerful machine learning frameworks, you’ll be ready to tackle complex machine learning challenges, optimize model performance, and significantly improve prediction accuracy in your projects. This course prepares you for advanced machine learning roles by providing you with the skills to make informed decisions on which ensemble technique to apply for optimal results.
Plus, check out these Live Lab Recordings:
Live Lab Recording #1: Handling Imbalanced Datasets and Detecting Outliers
Live Lab Recording #5: Time Series Analysis using Amazon Forecast
Live Lab Recording #19: Monitoring HuggingFace models with Weights and Biases
Live Lab Recording #24 : Gradient descent from scratch
Live Lab Recording #27: Creating an Intelligent agent with Q learning
Live Lab Recording #30: Mastering Hyper-Parameter Tuning
Live Lab Recording #32: Building an AI Agent from Scratch
Unlocks: Lvl.3 Machine Learning Expert
👉 Remember to join the Mentorship Program [here] to grow even faster!