Machine Learning Interview Course

Nail Machine Learning interviews at FAANG and Tier-1 Tech Companies
Want to know more?

Next webinar starts in

00
Days
:
00
Hrs
:
00
Mins
:
00
Secs
Want to know more?

Course designed and taught by instructors from FAANG & Tier-1 Tech Companies

Manoj Krishnan

Software Engineer
No items found.

Adrián Fernández

Engineering Manager
No items found.

Qiuping Xu.

Principal Data Scientist
No items found.

To learn more about the course

Next webinar starts in

00
Days
:
00
Hrs
:
00
Mins
:
00
Secs

Machine Learning Engineering Course Curriculum

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

  • 15 Mock Interviews
  • 6-Month Support Period

To learn more about this course

Next webinar starts in

00
Days
:
00
Hrs
:
00
Mins
:
00
Secs

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

00
Days
:
00
Hrs
:
00
Mins
:
00
Secs

Best Suited for

  • Current or Former Machine Learning Engineers
  • Software Engineers working on Machine Learning Engineers 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
No items found.

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

What our students say

Akshay Lodha
Data Engineer
Offers from
Google_logo

"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
Offers from
Google_logo

“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
Google_logo

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.

Frequently Asked Questions