The interview process for Machine Learning roles at top tech companies is challenging and multidimensional. To help you prepare, here’s a curated list of frequently asked interview questions, categorized by type of assessment.

1. Coding & Algorithmic Questions Expect questions that test your data structures, algorithms, and ability to translate ML problems into code. Implement a word segmentation algorithm: Given a string without spaces, recover the original sentence using a valid dictionary. Given a list of timestamps and user IDs, find the longest active streak for each user. Given a binary tree, write a function to return the path with the maximum sum passing through the root. Implement K-means clustering using only NumPy (no scikit-learn). Write a function to apply a convolution operation on a grayscale image matrix. Extend it with stride and padding options. Design a scheduler to assign jobs across machines with varying CPU & RAM capacities. 2. System Design for ML These questions evaluate your ability to build scalable and efficient ML-powered systems. Design a movie recommendation engine for an in-flight entertainment system. Architect a system for real-time fraud detection for an e-commerce platform.Design a scalable ML pipeline to personalize news feed content. How would you build a trigger word detection system like “Hey Google”? Explain how you’d design an ML feature store and manage feature versioning. 3. Core ML Conceptual Questions These test your understanding of fundamental algorithms, optimization techniques, and practical trade-offs. What’s the difference between bias-variance tradeoff and overfitting-underfitting? Explain how you’d handle imbalanced datasets in classification problems. How does dropout prevent overfitting in neural networks? What is the vanishing gradient problem, and how can it be mitigated? When should you use precision over recall in a classification task? Why might XGBoost outperform logistic regression or SVM on tabular data? 4. ML Tools, Frameworks, and Deployment Companies often assess your familiarity with the real-world tech stack. Describe how you’d deploy a model using Docker and Kubernetes. What are the pros and cons of using batch inference vs. real-time inference? How do you monitor model drift in production? Explain the differences between TF Serving, TorchServe, and BentoML. What CI/CD practices would you follow for ML systems? 5. Open-ended & Behavioral ML Questions These assess your ability to think holistically, collaborate cross-functionally, and contribute meaningfully to business impact. Pick a product you love and describe how you’d improve it using ML. How would you design an ML strategy to increase revenue for our business? What are your thoughts on improving our current data pipeline? How do you define and measure the success of an ML project? What’s the most impactful ML project you’ve worked on, and what were your key learnings?

Career Path for Machine Learning Engineers

Machine Learning offers one of the most rewarding and fast-growing career paths in tech. Here’s how your career could evolve — from your first role to advanced leadership positions — along with average annual salaries at top tech companies. 1. ML Engineer / Applied Scientist I (Entry-Level) Experience: 0–2 years Typical Roles: Machine Learning Engineer Applied Scientist Data Scientist (ML-focused) Skills Required: Python, data structures & algorithms, basic ML algorithms, supervised/unsupervised learning, model training & evaluation Average Salary (MAANG+): $130K – $180K base + bonuses + stock 2. ML Engineer II / Research Engineer / Applied Scientist II Experience: 2–4 years Typical Roles: Mid-level ML Engineer Applied Scientist II ML Research Engineer Skills Required: End-to-end ML pipelines, deep learning, production deployment, MLOps, scalable system design Average Salary (MAANG+): $170K – $250K base + performance bonus + equity 3. Senior ML Engineer / Senior Applied Scientist Experience: 4–7 years Typical Roles: Senior ML Engineer Senior Data Scientist (AI/ML focus) Lead Applied Scientist Skills Required: ML system design, optimization at scale, advanced modeling (transformers, GANs, RL), mentoring juniors, cross-team collaboration Average Salary (MAANG+): $220K – $350K total comp (can go higher with equity) 4. Staff ML Engineer / Principal Scientist / Tech Lead Experience: 7–10 years Typical Roles: Staff ML Engineer Machine Learning Tech Lead Principal Applied Scientist Skills Required: Technical leadership, architecting ML platforms, model lifecycle management, stakeholder communication, team mentoring Average Salary (MAANG+): $300K – $500K+ total compensation 5. ML Engineering Manager / Director of ML Experience: 10+ years Typical Roles: Engineering Manager (ML Team) Director / VP of AI/ML Skills Required: People management, strategic planning, roadmap ownership, aligning ML initiatives with business goals Average Salary (MAANG+): $400K – $800K+ total compensation

Techademy AI ML Career Transition Program

Q: What is the Interview Process Like for Machine Learning Roles at MAANG+ and Tier-1 Companies?

The Machine Learning interview process at top tech companies generally includes multiple stages designed to assess your technical, analytical, and behavioral fit. Here's a breakdown of what to expect: 1. Initial Technical Screening Format: Phone or virtual interview Focus Areas: Basic understanding of Machine Learning concepts Discussion of past ML projects Coding problems (often LeetCode medium to hard level) 2. Core Technical Rounds (On-site or Virtual – 3 to 8 Rounds) a. Coding Rounds (1–2 rounds) Data Structures and Algorithms ML-specific coding tasks In-depth ML project discussions b. System Design Rounds (1–2 rounds) Scalable software system design ML system design (e.g., building a recommendation engine) Note: For candidates with <3 years of experience, ML system design may be replaced with an additional ML fundamental round. c. ML Technical Rounds (1–3 rounds) Breadth and depth of ML understanding Topics include: Linear/Logistic Regression Decision Trees, SVM, Neural Networks Optimization techniques (e.g., Gradient Descent) Loss functions (e.g., Cross-Entropy Loss) Tools and deployment pipelines 3. Behavioral Round Experience and project-based discussion Open-ended questions to assess cultural and team fit Summary Candidates should prepare for: coding and DSA skills A solid grasp of ML algorithms and system-level thinking The ability to explain and discuss past ML projects Clear communication and alignment with company values during behavioral interviews

AI ML Career Transition P...

Placement Support

AI ML Career Transition Program

Transition to AI/ML with a Proven Program

36 Weeks of Live Fundamental Training by Experts

17 Weeks of Live Interview Preparation

Up to 20 Mocks with Hiring Managers, Tech Leads, Recruiters

Can’t wait?

Book a session with your Learning Advisor

1500+

Careers Transitioned

50 to 70%

Average Salary Hike

$350k

Highest Salary

99.6%

NPS Score

4.8/5

Standout Features of Our AI/ML Program!

AI/ML PROGRAM HIGHLIGHTS

Live Training Sessions

Get 36 weeks of engaging, instructor-led sessions taught by seasoned MAANG+ experts

Interview Preparation

Spend 17 weeks sharpening your skills with focused prep to crack top AI/ML interviews.

Mock Interviews

Practice with up to 20 mock interviews led by hiring managers, tech leads, and recruiters.

Capstone Projects

Work on 2 hands-on industry grade projects that showcase your practical AI knowledge.

AI-Powered Study Plan

Learn with a smart study plan that guides you and includes a built-in coding workspace.

Extended Support

Enjoy six months of continued help after training with mentorship and career support.

Lifetime LMS Access

Get unlimited access to all course content and recorded sessions—review anytime you want.

100% Money-Back Guarantee

Not satisfied? Get a full refund if it doesn’t meet your expectations.

Placement Support

Get help with resumes, LinkedIn, and expert career coaching to land your ideal job.

Unlimited Doubt Support

Ongoing help with technical questions and career guidance—whenever you need.

Trial Week Access

Try before you commit. Explore the program in the first week and decide if it's right for you.

Certification

Earn globally recognized certificates upon course and project completion to boost your professional credibility.

Always Up-to-Date Curriculum

Stay current with floater sessions that cover the latest AI/ML tools, techniques, and trends.

Regular Progress Tracking

Stay accountable with structured check-ins, performance insights, and milestone reviews.

AI ML COURSE REVIEWS

Testimonials

All Reviews

Google

Trustpilot

From Numbers to Neural Networks!

Honestly before taking this program I doubted if I could ever switch from analyt...Read More

Sophia Lee

Data Analyst

Finally, a Program That Gets It Right

The course teaching methods stood out from all other courses I had taken. It was...Read More

Michael Grant

Software Developer

I Was Interview-Ready Way Sooner Than I Expected

I didn’t come from a strong coding background, but the way this program breaks t...Read More

Ayesha Noor

QA Engineer

A Complete Shift—And It Was Worth It

Transitioning from engineering to AI proved difficult, but I found the perfect r...Read More

Daniel Kim

Mechanical Engineer

This Program Was a Turning Point

I spent numerous years trapped in my BI career and wanted to grow. This program ...Read More

Neha Reddy

Business Intelligence Consultant

4.9/5

4.7/5

4.6/5

4.5/5

AI ML CAREER TRANSITION PROGRAM TIMINGS

Class Timings

Sunday – 9 AM PST to 1 PM PST
Thursday – 6 PM PST to 8 PM PST
Tuesday – 6 PM PST to 8 PM PST (for doubt clearing)
Post Program completion – 6 Months of support period will be provided where candidates can go through doubt clearing sessions and take mock interviews.

Capstone Projects:

Real-World AI Implementation

Enterprise Knowledge Assistant (RAG + LangChain + OpenAI)
Airline Delay Predictor with MLOps Pipeline
AI-Based Patient Risk Prediction System (EDA + ML + Deployment)
AutoGPT-Powered Marketing Campaign Planner
Personalized Mental Health Coach (LLMs + Emotion Detection + LangChain Agents)

TARGET CAREER ROLES

Unlock High-Impact AI/ML Roles

Machine Learning Engineer
AI Consultant
Data Scientist
AI Developer / AI Engineer
NLP Engineer
Computer Vision Engineer
MLOps Engineer

WHAT YOU WILL LEARN IN AI ML COURSE

Learning Objectives

Programming Languages & Development Environments

Work hands-on with Python 3 core language for use cases like (ML, DL, LLMs, scripting). Use Jupyter, Colab, VS Code, and the terminal for writing, testing, and organizing code.

Data Handling & Processing

Use Pandas and NumPy to clean, shape, and analyze data efficiently. OpenCV handles images, while TimeSeriesGenerator helps prepare sequences for deep learning.

Visualization & EDA

Plot with Matplotlib and Seaborn for quick insights using charts, heatmaps, and distributions. Optionally explore interactive visuals with Plotly or view model attention with AttentionVisualizer.

Statistics & Scientific Computing

Apply SciPy for statistical tests and optimization. For deeper statistical modeling, StatsModels can be added to the toolkit if needed.

Prerequisites for AI ML Training

Prerequisites and Eligibility

Basic coding (any language) is a plus, but not mandatory.
No ML background needed—perfect for ambitious beginners.
STEM background who are looking to transition into ML Roles.
Software Engineers wanting to move into ML/AI roles.
Data Scientists looking to transition into production ML systems.
MLOps Engineers targeting end-to-end ML Engineering roles.
Researchers (MS/PhD holders) exploring applied ML/AI career.
IT/ Tech Roles (DevOps, Backend, Cloud) upskilling into AI/ML.

WHO CAN ATTEND THE AI ML COURSE

Who This Course Is For

Software Developers Ready for AI/ML
IT Pros Craving AI Innovation
Anyone from Tech Background
Anyone from STEM Background
Data Scientists Scaling Up ML Skills

GET THE AI/ML CERTIFICATION

Earn the Coveted AI/ML Credential

Earn Verified Certificates That Add Weight to Your Resume and LinkedIn Profile. Completing the AI/ML Career Transition Program and earning your Certificate of Accomplishment/Completion is more than a learning milestone—it's a recognized testament to your hands-on expertise in real-world machine learning and AI workflows. This certificate showcases your ability to work with production-grade models, advanced algorithms, deployment pipelines and make you well-prepared for a variety of entry-level and intermediate roles in the AI, machine learning, and data science fields.

INDUSTRY-RELEVANT AI ML TOOLS

Master the Tools That Power Modern AI

Trusted by Leading Companies to Upskill Their Workforce

Group Discounts Available

Transform your team

Gain proficiency in industry-standard tools

Learn to manage and deploy models

Empower yourself with best practices and the latest tools.

Boost in-role productivity with new skills.

AI ML TRAINING PROGRAM

Curriculum

Module 1: Python, SQL & Statistics (8 Weeks)

Learning Objectives:

Build strong foundations in Python programming, SQL querying, and key statistical concepts. Learn how to write clean code, work with data structures, and apply statistics for data analysis.

Topics :

Week 1: Python Basics & Programming Logic

1.Python setup and IDEs

Installing Python and setting up Anaconda
Using Jupyter, VS Code, or PyCharm

2.Basic syntax and indentation

Code structure and whitespace importance

3.Variables and data types

Integer, float, string, boolean
Type checking with type()

4.Type casting and conversions

Using int(), float(), str()

5.Basic operators

Arithmetic: +, -, *, /, %, //, **
Comparison: ==, !=, >, <, >=, <=
Logical: and, or, not

6.Conditional statements

Simple if
if-else, elif
Nested conditions

Week 2: Loops & Functions

1.for and while loops

Syntax and flow control
Looping over ranges, lists, strings

2.Loop control

break, continue, pass

3.Function definition and syntax

def keyword, indentation, naming

4.Parameters and return values

Positional and keyword arguments

5.Lambda functions

Anonymous functions
When and how to use

6.Recursion

Concept and examples
Base and recursive cases

Week 3: Python Data Structures Deep Dive

1.Lists

Creation, indexing, slicing
Methods: append, extend, insert, remove

2.Tuples

Immutability and packing/unpacking

3.Sets

Unique values, operations: union, intersection

4.Dictionaries

Key-value access, get(), items(), values()

5.List comprehensions

Syntax and performance benefits

6.Iteration tools

enumerate(), zip(), map(), filter()

Week 4: File Handling, Modules & Error Handling

1.File I/O

Reading and writing text files
Working with CSV using csv module
Reading JSON using json module

2.Working with OS and paths

os, pathlib

3.Importing modules

Standard libraries: math, datetime, random
Installing third-party packages

4.Custom modules

Creating and importing user-defined modules

5.Error types

Syntax vs. runtime errors

6.Try-except blocks

Handling exceptions gracefully

7.Finally, raise, and custom exceptions

Week 5: SQL for Data Science – Part 1

1.Relational database concepts

Tables, rows, columns, keys

2.SELECT queries

Syntax, selecting multiple columns

3.Filtering with WHERE

Operators: =, >, <, BETWEEN, IN, LIKE

4.Sorting with ORDER BY

ASC, DESC

5.Grouping with GROUP BY and HAVING

Aggregate functions: COUNT, SUM, AVG, MIN, MAX

Week 6: SQL: Joins, Subqueries, Window Functions – Part 2

1.Joins

INNER JOIN
LEFT, RIGHT, FULL OUTER JOIN

2.Subqueries

Inline subqueries
Correlated subqueries

3.Window functions

ROW_NUMBER, RANK, DENSE_RANK
LAG, LEAD for time-based analytics

4.Indexing and optimization

Primary vs. secondary indexes
Query performance tips

Week 7: Descriptive Statistics & Probability

1.Measures of central tendency

Mean, median, mode

2.Measures of dispersion

Range, variance, standard deviation, IQR

3.Probability basics

Sample space, events
Probability rules

4.Conditional probability

Bayes’ Theorem

5.Law of Large Numbers

6.Central Limit Theorem

Week 8: Distributions, Hypothesis Testing & Inference

1.Probability distributions

Normal, Binomial, Poisson

2.Sampling techniques

Random, stratified, systematic

3.Hypothesis testing

Null vs. alternative hypothesis

4.Statistical tests

t-tests, z-tests
ANOVA, chi-square

5.Significance and p-values

6.Confidence intervals

Module 2: Data Processing – (3 Weeks)

Module 3: Core ML – Supervised & Unsupervised (4 Weeks)

Module 4: Unsupervised Learning & Recommenders (3 Weeks)

Module 5: Deep Learning (9 Weeks)

Module 6: LLMs & Generative AI (4 Weeks)

AI ML FAQs

Frequently Asked Questions

Machine Learning Interview rounds

Sample Interview Questions

Career Path & Salaries

What is the Interview Process Like for Machine Learning Roles at MAANG+ and Tier-1 Companies?

The Machine Learning interview process at top tech companies generally includes multiple stages designed to assess your technical, analytical, and behavioral fit. Here's a breakdown of what to expect:

1. Initial Technical Screening

Format: Phone or virtual interview

Focus Areas:

Basic understanding of Machine Learning concepts
Discussion of past ML projects
Coding problems (often LeetCode medium to hard level)

2. Core Technical Rounds (On-site or Virtual – 3 to 8 Rounds)

a. Coding Rounds (1–2 rounds)

Data Structures and Algorithms
ML-specific coding tasks
In-depth ML project discussions

b. System Design Rounds (1–2 rounds)

Scalable software system design
ML system design (e.g., building a recommendation engine)
Note: For candidates with <3 years of experience, ML system design may be replaced with an additional ML fundamental round.

c. ML Technical Rounds (1–3 rounds)

Breadth and depth of ML understanding
Topics include:

Linear/Logistic Regression
Decision Trees, SVM, Neural Networks
Optimization techniques (e.g., Gradient Descent)
Loss functions (e.g., Cross-Entropy Loss)
Tools and deployment pipelines

3. Behavioral Round

Experience and project-based discussion
Open-ended questions to assess cultural and team fit

Summary

Candidates should prepare for:

Strong coding and DSA skills
A solid grasp of ML algorithms and system-level thinking
The ability to explain and discuss past ML projects
Clear communication and alignment with company values during behavioral interviews

Standout Features of Our AI/ML Program!

AI/ML PROGRAM HIGHLIGHTS

Live Training Sessions

Get 36 weeks of engaging, instructor-led sessions taught by seasoned MAANG+ experts

Interview Preparation

Spend 17 weeks sharpening your skills with focused prep to crack top AI/ML interviews.

Mock Interviews

Practice with up to 20 mock interviews led by hiring managers, tech leads, and recruiters.

Capstone Projects

Work on 2 hands-on industry grade projects that showcase your practical AI knowledge.

AI-Powered Study Plan

Learn with a smart study plan that guides you and includes a built-in coding workspace.

Extended Support

Enjoy six months of continued help after training with mentorship and career support.

Lifetime LMS Access

Get unlimited access to all course content and recorded sessions—review anytime you want.

100% Money-Back Guarantee

Not satisfied? Get a full refund if it doesn’t meet your expectations.

Placement Support

Get help with resumes, LinkedIn, and expert career coaching to land your ideal job.

Unlimited Doubt Support

Ongoing help with technical questions and career guidance—whenever you need.

Trial Week Access

Try before you commit. Explore the program in the first week and decide if it's right for you.

Certification

Earn globally recognized certificates upon course and project completion to boost your professional credibility.

Always Up-to-Date Curriculum

Stay current with floater sessions that cover the latest AI/ML tools, techniques, and trends.

Regular Progress Tracking

Stay accountable with structured check-ins, performance insights, and milestone reviews.

AI ML COURSE REVIEWS

Testimonials

All Reviews

Google

Trustpilot

From Numbers to Neural Networks!

Honestly before taking this program I doubted if I could ever switch from analyt...Read More

Sophia Lee

Data Analyst

Finally, a Program That Gets It Right

The course teaching methods stood out from all other courses I had taken. It was...Read More

Michael Grant

Software Developer

I Was Interview-Ready Way Sooner Than I Expected

I didn’t come from a strong coding background, but the way this program breaks t...Read More

Ayesha Noor

QA Engineer

A Complete Shift—And It Was Worth It

Transitioning from engineering to AI proved difficult, but I found the perfect r...Read More

Daniel Kim

Mechanical Engineer

This Program Was a Turning Point

I spent numerous years trapped in my BI career and wanted to grow. This program ...Read More

Neha Reddy

Business Intelligence Consultant

4.9/5

4.7/5

4.6/5

4.5/5

AI ML CAREER TRANSITION PROGRAM TIMINGS

Class Timings

Sunday – 9 AM PST to 1 PM PST
Thursday – 6 PM PST to 8 PM PST
Tuesday – 6 PM PST to 8 PM PST (for doubt clearing)
Post Program completion – 6 Months of support period will be provided where candidates can go through doubt clearing sessions and take mock interviews.

Capstone Projects:

Real-World AI Implementation

Enterprise Knowledge Assistant (RAG + LangChain + OpenAI)
Airline Delay Predictor with MLOps Pipeline
AI-Based Patient Risk Prediction System (EDA + ML + Deployment)
AutoGPT-Powered Marketing Campaign Planner
Personalized Mental Health Coach (LLMs + Emotion Detection + LangChain Agents)

TARGET CAREER ROLES

Unlock High-Impact AI/ML Roles

Machine Learning Engineer
AI Consultant
Data Scientist
AI Developer / AI Engineer
NLP Engineer
Computer Vision Engineer
MLOps Engineer

WHAT YOU WILL LEARN IN AI ML COURSE

Learning Objectives

Programming Languages & Development Environments

Work hands-on with Python 3 core language for use cases like (ML, DL, LLMs, scripting). Use Jupyter, Colab, VS Code, and the terminal for writing, testing, and organizing code.

Data Handling & Processing

Use Pandas and NumPy to clean, shape, and analyze data efficiently. OpenCV handles images, while TimeSeriesGenerator helps prepare sequences for deep learning.

Visualization & EDA

Plot with Matplotlib and Seaborn for quick insights using charts, heatmaps, and distributions. Optionally explore interactive visuals with Plotly or view model attention with AttentionVisualizer.

Statistics & Scientific Computing

Apply SciPy for statistical tests and optimization. For deeper statistical modeling, StatsModels can be added to the toolkit if needed.

Prerequisites for AI ML Training

Prerequisites and Eligibility

Basic coding (any language) is a plus, but not mandatory.
No ML background needed—perfect for ambitious beginners.
STEM background who are looking to transition into ML Roles.
Software Engineers wanting to move into ML/AI roles.
Data Scientists looking to transition into production ML systems.
MLOps Engineers targeting end-to-end ML Engineering roles.
Researchers (MS/PhD holders) exploring applied ML/AI career.
IT/ Tech Roles (DevOps, Backend, Cloud) upskilling into AI/ML.

WHO CAN ATTEND THE AI ML COURSE

Who This Course Is For

Software Developers Ready for AI/ML
IT Pros Craving AI Innovation
Anyone from Tech Background
Anyone from STEM Background
Data Scientists Scaling Up ML Skills

GET THE AI/ML CERTIFICATION

Earn the Coveted AI/ML Credential

INDUSTRY-RELEVANT AI ML TOOLS

Master the Tools That Power Modern AI

Trusted by Leading Companies to Upskill Their Workforce

Group Discounts Available

Transform your team

Gain proficiency in industry-standard tools

Learn to manage and deploy models

Empower yourself with best practices and the latest tools.

Boost in-role productivity with new skills.

AI ML TRAINING PROGRAM

Curriculum

Module 1: Python, SQL & Statistics (8 Weeks)

Learning Objectives:

Build strong foundations in Python programming, SQL querying, and key statistical concepts. Learn how to write clean code, work with data structures, and apply statistics for data analysis.

Topics :

Week 1: Python Basics & Programming Logic

1.Python setup and IDEs

Installing Python and setting up Anaconda
Using Jupyter, VS Code, or PyCharm

2.Basic syntax and indentation

Code structure and whitespace importance

3.Variables and data types

Integer, float, string, boolean
Type checking with type()

4.Type casting and conversions

Using int(), float(), str()

5.Basic operators

Arithmetic: +, -, *, /, %, //, **
Comparison: ==, !=, >, <, >=, <=
Logical: and, or, not

6.Conditional statements

Simple if
if-else, elif
Nested conditions

Week 2: Loops & Functions

1.for and while loops

Syntax and flow control
Looping over ranges, lists, strings

2.Loop control

break, continue, pass

3.Function definition and syntax

def keyword, indentation, naming

4.Parameters and return values

Positional and keyword arguments

5.Lambda functions

Anonymous functions
When and how to use

6.Recursion

Concept and examples
Base and recursive cases

Week 3: Python Data Structures Deep Dive

1.Lists

Creation, indexing, slicing
Methods: append, extend, insert, remove

2.Tuples

Immutability and packing/unpacking

3.Sets

Unique values, operations: union, intersection

4.Dictionaries

Key-value access, get(), items(), values()

5.List comprehensions

Syntax and performance benefits

6.Iteration tools

enumerate(), zip(), map(), filter()

Week 4: File Handling, Modules & Error Handling

1.File I/O

Reading and writing text files
Working with CSV using csv module
Reading JSON using json module

2.Working with OS and paths

os, pathlib

3.Importing modules

Standard libraries: math, datetime, random
Installing third-party packages

4.Custom modules

Creating and importing user-defined modules

5.Error types

Syntax vs. runtime errors

6.Try-except blocks

Handling exceptions gracefully

7.Finally, raise, and custom exceptions

Week 5: SQL for Data Science – Part 1

1.Relational database concepts

Tables, rows, columns, keys

2.SELECT queries

Syntax, selecting multiple columns

3.Filtering with WHERE

Operators: =, >, <, BETWEEN, IN, LIKE

4.Sorting with ORDER BY

ASC, DESC

5.Grouping with GROUP BY and HAVING

Aggregate functions: COUNT, SUM, AVG, MIN, MAX

Week 6: SQL: Joins, Subqueries, Window Functions – Part 2

1.Joins

INNER JOIN
LEFT, RIGHT, FULL OUTER JOIN

2.Subqueries

Inline subqueries
Correlated subqueries

3.Window functions

ROW_NUMBER, RANK, DENSE_RANK
LAG, LEAD for time-based analytics

4.Indexing and optimization

Primary vs. secondary indexes
Query performance tips

Week 7: Descriptive Statistics & Probability

1.Measures of central tendency

Mean, median, mode

2.Measures of dispersion

Range, variance, standard deviation, IQR

3.Probability basics

Sample space, events
Probability rules

4.Conditional probability

Bayes’ Theorem

5.Law of Large Numbers

6.Central Limit Theorem

Week 8: Distributions, Hypothesis Testing & Inference

1.Probability distributions

Normal, Binomial, Poisson

2.Sampling techniques

Random, stratified, systematic

3.Hypothesis testing

Null vs. alternative hypothesis

4.Statistical tests

t-tests, z-tests
ANOVA, chi-square

5.Significance and p-values

6.Confidence intervals

Module 2: Data Processing – (3 Weeks)

Module 3: Core ML – Supervised & Unsupervised (4 Weeks)

Module 4: Unsupervised Learning & Recommenders (3 Weeks)

Module 5: Deep Learning (9 Weeks)

Module 6: LLMs & Generative AI (4 Weeks)

AI ML FAQs

Frequently Asked Questions

Machine Learning Interview rounds

Sample Interview Questions

Career Path & Salaries

What is the Interview Process Like for Machine Learning Roles at MAANG+ and Tier-1 Companies?

Focus Areas:

Basic understanding of Machine Learning concepts
Discussion of past ML projects
Coding problems (often LeetCode medium to hard level)

2. Core Technical Rounds (On-site or Virtual – 3 to 8 Rounds)

a. Coding Rounds (1–2 rounds)

Data Structures and Algorithms
ML-specific coding tasks
In-depth ML project discussions

b. System Design Rounds (1–2 rounds)

Scalable software system design
ML system design (e.g., building a recommendation engine)
Note: For candidates with <3 years of experience, ML system design may be replaced with an additional ML fundamental round.

c. ML Technical Rounds (1–3 rounds)

Breadth and depth of ML understanding
Topics include:

Linear/Logistic Regression
Decision Trees, SVM, Neural Networks
Optimization techniques (e.g., Gradient Descent)
Loss functions (e.g., Cross-Entropy Loss)
Tools and deployment pipelines

3. Behavioral Round

Experience and project-based discussion
Open-ended questions to assess cultural and team fit

Summary

Candidates should prepare for:

Strong coding and DSA skills
A solid grasp of ML algorithms and system-level thinking
The ability to explain and discuss past ML projects
Clear communication and alignment with company values during behavioral interviews