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How to Nail your next Technical Interview

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Machine Learning Interview Course

Nail Machine Learning interviews at FAANG and Tier-1 Tech Companies
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Course designed and taught by instructors from FAANG & Tier-1 Tech Companies

Manoj Krishnan

Software Engineer
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Adrián Fernández

Engineering Manager
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Qiuping Xu.

Principal Data Scientist
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Machine Learning Engineering Course Curriculum

This is what you'll learn in our Machine Learning career path!

  • 15 Mock Interviews
  • 6-Month Support Period

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Machine Learning course and curriculum

Data structures and Algorithms
5 weeks
5 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

Sorting

  • Introduction to Sorting
  • Basics of Asymptotic Analysis and Worst Case & Average Case Analysis
  • Different Sorting Algorithms and their comparison
  • Algorithm paradigms like Divide & Conquer, Decrease & Conquer, Transform & Conquer
  • Presorting
  • Extensions of Merge Sort, Quick Sort, Heap Sort
  • Common sorting-related coding interview problems
2

Recursion

  • Recursion as a Lazy Manager's Strategy
  • Recursive Mathematical Functions
  • Combinatorial Enumeration
  • Backtracking
  • Exhaustive Enumeration & General Template
  • Common recursion- and backtracking-related coding interview problems
3

Trees

  • Dictionaries & Sets, Hash Tables 
  • Modeling data as Binary Trees and Binary Search Tree and performing different operations over them
  • Tree Traversals and Constructions 
  • BFS Coding Patterns
  • DFS Coding Patterns
  • Tree Construction from its traversals 
  • Common trees-related coding interview problems
4

Graphs

  • Overview of Graphs
  • Problem definition of the 7 Bridges of Konigsberg and its connection with Graph theory
  • What is a graph, and when do you model a problem as a Graph?
  • How to store a Graph in memory (Adjacency Lists, Adjacency Matrices, Adjacency Maps)
  • Graphs traversal: BFS and DFS, BFS Tree, DFS stack-based implementation
  • A general template to solve any problems modeled as Graphs
  • Graphs in Interviews
  • Common graphs-related coding interview problems
5

Dynamic Programming

  • Dynamic Programming Introduction
  • Modeling problems as recursive mathematical functions
  • Detecting overlapping subproblems
  • Top-down Memorization
  • Bottom-up Tabulation
  • Optimizing Bottom-up Tabulation
  • Common DP-related coding interview problems

System design
3 weeks
3 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

Online Processing Systems

  • The client-server model of Online processing
  • Top-down steps for system design interview
  • Depth and breadth analysis
  • Cryptographic hash function
  • Network Protocols, Web Server, Hash Index
  • Scaling
  • Performance Metrics of a Scalable System
  • SLOs and SLAs
  • Proxy: Reverse and Forward
  • Load balancing
  • CAP Theorem
  • Content Distribution Networks
  • Cache
  • Sharding
  • Consistent Hashing
  • Storage
  • Case Studies: URL Shortener, Instagram, Uber, Twitter, Messaging/Chat Services
2

Batch Processing Systems

  • Inverted Index
  • External Sort Merge
  • K-way External Sort-Merge
  • Distributed File System
  • Map-reduce Framework
  • Distributed Sorting
  • Case Studies: Search Engine, Graph Processor, Typeahead Suggestions, Recommendation Systems
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Machine Learning Masterclass
5 weeks
5 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

Supervised Learning I - Rank Relevant Search Results

  • Deep dive into the design of a search relevance system like Google Search (a popular FAANG interview question).
  • Comprehensive coverage of document indexing, retrieval, similarity scoring, filtering, and ranking.
  • Model training: Build a ranking model using Linear or Logistic Regression. Which method performs better for ranking a search result?
  • Online testing scenarios: How to run an A/B test to evaluate if new features improve the model? Which metrics to use?
  • Interesting follow-up questions: How do you offset the lack of negative samples for training without adding new data? Does a “no-click” impression correspond to a negative label?
  • Non-trivial questions on fundamental topics: Does L1/L2 regularization always increase model performance? Which metric is best for evaluating models on imbalanced datasets? When is the k-nearest neighbors algorithm better than logistic regression for classification analysis?
2

Supervised Learning II - Design a YouTube Video Recommendation System

  • Build a video recommendation system for YouTube users. Answer questions like: How to maximize user engagement? How to recommend new content to users?
  • Best practices of feature engineering, data collection, feature encoding, and video embeddings.
  • In-depth coverage of content-based and collaborative filtering, matrix factorization, and maximizing the optimum objective function.
  • Triage critical concerns when building a recommender system: How do you account for positional bias when ranking a video? Strategies to ensure the freshness, diversity, and fairness of the recommendations.
  • Non-trivial questions on fundamental topics: How do you train a State Vector Machine (SVM) on non-linear data? Why only a subset of features for each tree in a random forest? What is the bias-variance tradeoff, and how can ensemble learning techniques like bagging and boosting address it?
3

Unsupervised Learning - Detect Fraud Transactions for Airbnb

  • Design an anomaly detection system for Airbnb transactions.
  • Comprehensive coverage of fraud detection techniques: Reputation lists, rules-based detection, classification vs. clustering.
  • A top-down approach to building a high-level architecture: User and agent data, feature aggregation, model dashboard, data embedding
  • Difficult follow-up questions: How to speed up computation time for unsupervised anomaly detection? Strategies to combine clustering with supervised learning techniques.
  • Non-trivial questions on fundamental topics: What could be the possible reason(s) for producing two different dendrograms using an agglomerative clustering algorithm for the same dataset? Dimensionality reduction with computational power constraints - t-SNE vs. PCA?
4

Deep Learning I - Detect and Process Objects in a Scene

  • Design an Image Processing system for Object Detection (frequently asked in FAANG ML interviews).
  • Deep-dive into object detection workflow: Preprocessing, Candidate Generation and Selection, Unprocessing, and Postprocessing.
  • Multiple strategies to build the object detector: Convolutional Networks, Region-based CNNs, You Only Look Once (YOLO), Transfer Learning, etc.
  • Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?
  • Address possible follow-up questions: How do you detect and replace multiple instances of the same object from an image? What if direct ground truth labels are absent? 
  • Non-trivial questions on fundamental topics: Using dropout layers in a small neural network. How to combat vanishing and exploding gradients in CNNs? What is the best learning rate optimizer for improving model performance on large datasets?
5

Deep Learning II - Build a Tech Support Chatbot

  • Design an intelligent Discord bot to provide Technical Support for a software Bootcamp.
  • In-depth coverage of functional and non-functional requirements: Scale and latency estimation, throughput, passive feedback mechanisms.
  • Knowledge base creation: Embedding, Sharding, Caching, etc. Strategies to expand the knowledge base.
  • Challenges in bot design: How to deal with a cold-start with no knowledge base? How do you generate answers to previously unasked questions?
  • Logical follow-up questions: How to handle increasing complexity and scale? How can we introduce a continuous learning mechanism in the chatbot design?
  • Non-trivial questions on fundamental topics: Which type of word embedding method is more suitable for measuring context similarity? Why is “Exploding Gradients'' a problem in the context of RNNs? When should we not use a bi-directional LSTM?
6

Additional Topics:

A comprehensive step-by-step approach to ML System Design interview rounds
  • How is ML System Design different from General System Design?
  • What does an interviewer expect from this round?
  • How do you breakdown and answer open-ended questions like: 
Modern ML Architectures
  • Why do we learn a distribution instead of a deterministic model during encoding? How do we introduce variability in a variable autoencoder?
  • What is the difference between Discriminative and Generative models? 
Reinforcement Learning
  • How do you evaluate the state and responses of an agent?
  • How is value iteration different from policy iteration? What problem does it address?
Career Coaching
3 weeks
3 live classes
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

Interview Preparation

Interview Questions
Placement assistance
Behavioral Coaching
2

Resume & LinkedIn Masterclass

3

Salary Negotiation Masterclass

Support Period
6 months
1

Online Processing Systems

Common Scalable Concepts like DBs, Cache, Messaging Queue, etc., and Common Design Problems
2

Batch Processing Systems

Batch Processing Concepts in-depth and Common Design Problems for FAANG+ interviews
3

Stream Processing Systems

  • Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube
Design real-time data-intensive applications like Google Maps, Netflix, etc.
1

15 mock interviews

2

Take classes you missed/retake classes/tests

3

1:1 technical/career coaching

4

Interview strategy and salary negotiation support


Next webinar starts in

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Best Suited for

  • Current or Former Machine Learning Engineers
  • Software Engineers working on Machine Learning Models
  • Other relevant role-responsibility dynamics (some companies have Data Scientists/Research Engineers or Software Engineers for this job)

Machine Learning Interview Process at Tier-1 Companies

We prepare you for all stages of a typical Machine Learning interview process at FAANG and Tier-1 companies

Initial Technical Screening

  • Basic ML understanding — discussion on past projects
  • Coding problems

Behavioral Round

  • Questions related to your job experience
  • Discussions on past projects
  • Open-ended questions to gauge if you're a "good fit"

Onsite: 4-5 Rounds

  • Coding round: Can be a mix of project discussion + coding
  • System design round
  • For candidates with < 3 years of experience, ML system design is often replaced by another core ML understanding round of medium/high difficulty
  • ML fundamental round: Also known as ML depth round
Top companies love hiring our candidates
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Top companies love hiring our candidates!

10K+

Experienced engineers enrolled

7

Years of successful training in Silicon Valley

18

Highest number of offers received by an alum

5

Avg years of experience of our alumni
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What our students say

Akshay Lodha
Data Engineer
Offers from
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"The way the instructors taught was awesome, the career coaching and the mock interview sections were also really helpful. Interview Kickstart helped me a lot in orienting myself and getting into the rhythm., and eventually transition from Goldman Sachs to Facebook."

Sujay Ghosh
Software Development Manager
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“Interview Kickstart's program is wonderful. I found the classes as well as the materials provided by Interview Kickstart very helpful. In the mock interview sessions, I was able to clear my concerns on a 1-on-1 basis.”

Rupesh Dabbir
Offers from
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Interview Kickstart (IK) provides you a solid platform to not only strengthen your algorithm and interview game, I've had the pleasure of meeting some of the best/brightest minds in the industry (Faculty and students included). It was a humble experience, to say the least.

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