30 days
To effectively prepare for a machine learning interview at Google in the next 30 days, it's essential to cover a comprehensive range of topics that are relevant to the role. Below is a structured schedule that outlines key areas to focus on each week, incorporating both theoretical knowledge and practical applications.
Week 1: Foundations of Machine Learning
Day 1-2: Introduction to Machine Learning
Overview of ML concepts: supervised vs. unsupervised learning
Key algorithms: linear regression, logistic regression, decision trees
Day 3-4: Data Preprocessing Techniques
Data cleaning and handling missing values
Feature scaling and normalization techniques
Day 5-7: Exploratory Data Analysis (EDA)
Visualization techniques using Matplotlib and Seaborn
Understanding distributions, correlations, and outliers
Week 2: Core Algorithms and Models
Day 8-9: Supervised Learning Algorithms
In-depth study of SVMs, k-NN, and ensemble methods (Random Forests, Gradient Boosting)
Day 10-11: Unsupervised Learning Algorithms
Clustering techniques (K-means, hierarchical clustering)
Dimensionality reduction (PCA, t-SNE)
Basics of neural networks and backpropagation
Introduction to deep learning frameworks (PyTorch)
Week 3: Advanced Topics in Machine Learning
Day 15-16: Model Evaluation and Selection
Metrics for classification and regression (PR vs ROC, F1 score)
Cross-validation techniques
Day 17-18: Hyperparameter Tuning
Grid search vs. random search vs. Bayesian
Understanding overfitting and underfitting
Day 19-21: Specialized Techniques
Natural Language Processing basics (Embeddings, Transformers)
Computer vision concepts (CNNs, Data Augmentation)
Week 4: System Design and Practical Applications
Frameworks for building ML systems
Scalability & production considerations
Day 23: Case Studies in ML Applications
Review case studies from Google’s applications (YouTube, Photos, Search)
Day 24-25: Behavioral & Soft Skills
STAR method, common behavioral questions
Aligning with company values
Day 26-30: Final Review & Mental Prep
Revisit challenging topics
Mock interviews and thought articulation
Mental readiness and logistics
By following this structured schedule, you will be well-prepared for your machine learning interview at Google. Focus on both theoretical understanding and practical application through coding exercises and mock interviews to enhance your readiness.
Sources [1] Google Machine Learning Engineer Interview Prep https://www.interviewkickstart.com/blogs/companies/google-machine-learning-engineer-interview-prep [2] 30 days of Data Science InterviewPreparation - All in one | Kaggle https://www.kaggle.com/discussions/getting-started/124056
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