Switchup: TRANSFORM YOUR CAREER

Transition to AI/ Machine Learning Engineering roles at Tier-1 companies

Designed and taught by FAANG+ AI/Machine Learning Engineers to help you transform your career and land your dream job.

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Students who chose to uplevel with IK got placed at

21,000+

Tech professionals trained

$1.28M

Highest offer received by an IK alum

53%

Average salary hike received by alums

Advanced AI/Machine Learning Course Curriculum

PART 1: Mastering Machine Learning

Foundations

1

Python Fundamentals

  • Variables, if-else, loops, Functions, lists, strings, etc. related coding examples

  • Tuples, Set, Dict, map, filter, reduce etc. related coding examples

  • OOP in Python, File and Exception handling

  • Numpy, Pandas, etc.

  • Visualisation with Python

2

Software Development Essentials

  • Scripting, Git & GitHub

  • Client-Server Architecture, HTTP & REST APIs

  • Databases & Intro to SQL
Essential Mathematics for Machine Learning

1

Essentials of Probability

2

Probability Distribution

3

Essentials of Statistics

4

Hypothesis Testing

5

Basic and Linear Algebra

6

Calculus

7

Vectors and Matrices

8

Regression

Deep Dive into Machine Learning Engineering

1

Foundational Machine Learning Concepts

  • Data Pipelines for ML: Techniques and Best Practices

  • Supervised Machine Learning Techniques and Applications

  • Unsupervised Machine Learning Techniques and Applications

  • Introduction to Neural Networks and Deep Learning Architectures such as RNN, LSTM, CNN etc

  • AI Development through Multiple Mini Projects: Hands-on Techniques and Applications
 

2

Advanced Machine Learning Framework: Techniques and Best Practices for Successful Development

  • NLP: Techniques and Applications of Natural Language Processing using Embeddings, Autoencoders, VAE, GANs etc.

  • Generative AI : BERT, Transformers, and LLMs for Advanced AI Development

  • Computer Vision Techniques and Applications in AI for Image and Video Analysis : Object Detection, Boundary Detection, Image Segmentation etc

  • Reinforcement Learning through Human Feedback (RLHF) : Introduction and Applications in Generative AI.

  • Deep Learning Mini Projects: Hands-on Techniques and Applications to build a system from scratch.

3

ML Development and Deployment: Advanced ML and MLOps Techniques

  • Software System Design Fundamentals: Principles and Best Practices for AI-based Development

  • ML Design Principles: Guiding principles for developing effective ML systems.

  • Scoping ML Projects: Defining goals, objectives, and boundaries of ML projects

  • Distributed Training: Data & Model Parallelism using GPUs

  • Model Deployment: Best Practices for Successful Implementation and Maintenance at Scale

  • Improving Model Performance: Techniques and Strategies for Retraining and Model Decay

  • AI Model Monitoring and Maintenance: Best Practices for Ensuring Optimal Performance

  • Failure Analysis: Techniques and Strategies for Diagnosing Production Issues

  • Ensuring ML Model Stability: Techniques and Best Practices

4

Capstone Project: Real-world Applications and Hands-on Experience in Machine Learning

  • Industry-Relevant AI Projects: Techniques and Best Practices for Developing Real-world Solutions

  • AI Mentorship at FAANG Companies: Techniques and Best Practices for Career Development

  • Capstone Presentation: Best Practices for Presenting Machine Learning Solutions to Stakeholders

PART 2: Interview Preparation

Data Structures and Algorithms Interview Preparation

1

Trees

2

Graphs

3

Greedy Algorithms

4

Dynamic Programming

Software System Design Interview Preparation

1

Online Processing Systems

2

Batch Processing Systems

3

Stream Processing Systems and Object Modeling

AI/Machine Learning Interview Preparation

1

Supervised Learning I - Rank Relevant Search Results

2

Supervised Learning II - Design a YouTube Video Recommendation System

3

Unsupervised Learning - Detect Fraud Transactions for Airbnb

4

Deep Learning I - Detect and Process Objects in a Scene

5

Deep Learning II - Build a Tech Support Chatbot

6

Additional Topics:

  • Comprehensive, Step-by-Step Approach to ML System Design Interviews

  • Modern ML Architectures

  • Reinforcement Learning
Career & Behavioral Sessions

1

Interview strategy and success

2

Behavioral interview prep

3

Offers and negotiation

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Meet your instructors

Our highly experienced instructors are active hiring managers and employees at FAANG+ companies and know exactly what it takes to ace tech and managerial interviews.

Built for working professionals

Here’s what a typical week would look like:

Sunday

4-hour Live Classes in the morning

Thursday

2-hour session in the evening to discuss assignments and problem solutions

Once a week

1-hour technical coaching session to discuss any additional doubts

Best suited for

Why choose this course?

Program by FAANG+ ML Engineers

360° course designed and taught by FAANG+ experts to help you become an ML Engineer

Individualized teaching and 1:1 help

Technical coaching, homework assistance, solutions discussion, and individual sessions

Capstone project

Exposure to real-life machine learning projects

Interview prep modules

Dedicated interview prep classes focused on helping you get 100% interview-ready

Mock interviews with FAANG+ ML Engineers

Live interview practice in real-life simulated environments with FAANG and top-tier interviewers

Career skills development

Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops

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Practice and track progress on UpLevel

Designed and taught by FAANG+ AI/Machine Learning Engineers to help you transform your career and land your dream job.

Mock interviews suite

On-demand timed tests

In-browser online judge

10,000 interview questions

100,000 hours of video explanations

Class schedules & activity alerts

Real-time progress update

11 programming languages

Get upto 15 mock interviews with

hiring managers

What makes our mock Interviews the best:

Hiring managers from Tier-1 companies like Google & Apple

Interview with the best. No one will prepare you better!

Domain-specific interviews

Practice for your target domain - Machine Learning

Detailed personalized feedback

Identify and work on your improvement areas

Transparent, non-anonymous interviews

Get the most realistic experience possible

1. Flexible schedule

Pick timings convenient to you

4. Technical and behavioral interviews

Uplevel your technical and behavioral interview skills

2. Remote interview experience

Mirrors the current format of remote interviews

5. Level-specific interviews

Because an L4 at Google can be quite different from an E7 at Meta

3. Feedback documentation

All the feedback you’ve ever wanted, recorded and documented

6. Interviewer of your choice

Choose based on domain

Career impact

Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.

How to enroll for the Advanced Machine Learning Course

Learn more about Interview Kickstart and the Advanced Machine Learning Course by joining the free pre-enrollment webinar.

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