Interview Preparation Roadmap

Jan 28, 2019
1 minute read

Week 1-2: Fundamentals & Problem-Solving

Software Engineering:

Data Structures: Arrays, Linked Lists, Stacks, Queues, Hash Tables, Trees, Graphs.
Algorithms: Sorting, Searching, Dynamic Programming, Recursion, Backtracking.
LeetCode/ HackerRank: Solve easy and medium-level problems. Aim for at least 3 problems daily.

ML Engineering:

Math Basics: Linear algebra, calculus, probability, statistics.
ML Fundamentals: Supervised vs. Unsupervised Learning, Overfitting/Underfitting, Bias-Variance Tradeoff.
Coding: Implement basic algorithms (e.g., Linear Regression, Logistic Regression).

Week 3-4: Advanced Topics & System Design

Software Engineering:

Advanced Algorithms: Graph algorithms, DP on trees, Segment Trees, Trie.
System Design: Learn about scalability, load balancing, caching, databases, microservices. Read “Designing Data-Intensive Applications” by Martin Kleppmann.
LeetCode/HackerRank: Solve medium and hard-level problems. Aim for at least 2 problems daily.

ML Engineering:

Advanced ML Concepts: Deep learning, CNNs, RNNs, NLP basics.
Projects: Work on one or two significant projects demonstrating ML skills (e.g., Kaggle competitions, custom projects).
System Design: Understand ML system design concepts, model deployment, monitoring.

Week 5-6: Mock Interviews & Review

Software Engineering:

Mock Interviews: Use platforms like Pramp or Interviewing.io for mock interviews.
Review: Revisit weaker topics, solve previous interview questions, and conduct mock system design interviews.
Behavioral Questions: Prepare answers for common behavioral questions. Use the STAR method (Situation, Task, Action, Result).

ML Engineering:

Mock Interviews: Practice with peers or use interview prep platforms.
Review Projects: Ensure your projects are well-documented and you can explain every detail.
Behavioral Questions: Prepare for questions on project experiences, teamwork, and challenges.

Daily Routine:

Morning (2 hours): Coding practice.
Afternoon (2 hours): Study theoretical concepts (Data Structures, Algorithms, ML theory).
Evening (2 hours): Work on projects, system design practice, or mock interviews.
Night (1 hour): Review and consolidate learning, work on behavioral questions.

Resources:

Books: “Cracking the Coding Interview” by Gayle Laakmann McDowell, “Introduction to Algorithms” by Cormen, “Pattern Recognition and Machine Learning” by Christopher M. Bishop.
Online Courses: Coursera, edX, Udacity for ML courses; AlgoExpert, LeetCode for coding.
YouTube Channels: TechLead, Computerphile, 3Blue1Brown.

Consistency and regular practice are key. Stay focused, and you’ll be well-prepared for your interviews. Good luck!

LLM Based Chatbot using RAG

LLM based chatbot using RAG.

Time Series Forecasting

Time series forecasting problems can be approached using various methods, including statistical and machine learning methods. This article focuses primarily on machine learning. Statistical methods may be covered in upcoming articles.

Distributed Training with Tensorflow

Distributed training is used to speed up the time-consuming task of training machine learning models as dataset size increases.

Machine Learning Concepts

Machine Learning Basic Concepts.

Advance Python Tutorials

Contains advance python tutorials.

Coding Patterns Cheatsheet

Coding Patterns Cheatsheet.

Python Data Structures and Algorithms (DSA)

Contains advance python DSA concepts.

System Design [HLD]

Basics of System Design.

System Design [LLD]

System Design-LLD.