AI and Machine Learning Program
Software engineers like you join the self-paced Outcome School AI and ML Program to achieve the outcome that is a high-paying tech job in AI and ML.
Designed for Outcome
A recorded program to help developers get a high-paying job by learning the internals of how things work and mastering system design, at their own pace.
100 hours of recordings
40 recorded classes, each 2 to 3 hours long. Simple explanations for complex topics.
Source code and notes
Complete source code of all the projects, along with the class notes.
Learn at your own pace
Join anytime from anywhere in the world and watch the classes on your own schedule.
We teach Internals
To get a high-paying tech job in AI and ML, you must know the internals and be great at system design. Knowledge comes to those who crave for it.
Eligibility
Working tech professionals who want to learn AI from scratch and transition into high-paying AI roles through a strong understanding of AI, Machine Learning and System Design.
Prerequisite
Any programming language. Even without Python experience, if you know any other language, you can learn Python while building the projects.
Roles
With your effort, you can transition to
Curriculum
We will learn all of these in-depth:
- AI Engineering
- AI Engineering Overview
- LLM Fundamentals
- LLM Internals
- Tokenization
- Positional Encodings
- Q, K, V Matrices
- Attention Mechanism
- Self-Attention and Multi-Head Attention
- Causal Masked Attention
- Transformer Architecture
- KV Cache
- Paged Attention
- Speculative Decoding
- Continuous Batching
- Prompt Caching
- Mixture of Experts (MoE)
- Cross Attention
- Grouped Query Attention
- vLLM
- Prompt Engineering
- Prompt Chaining
- Chain of Thought (CoT) Prompting
- Context Engineering
- AI Security
- Prompt Injection
- RAG
- Vector Databases
- Fine-tuning
- Parameter-Efficient Fine-Tuning (PEFT)
- Low-Rank Adaptation (LoRA), QLoRA
- Quantization and Optimizations
- Model Compression
- Knowledge Distillation
- SLMs and Model Distillation
- ReAct Pattern
- AI Agent
- Agentic AI
- Tool use in Agents
- Memory in Agents
- Model Context Protocol (MCP)
- Agent Architecture
- Subagent
- Multi-Agent Systems
- Orchestration and Routing
- Evaluation of LLMs and Agents
- LLM as a Judge
- Multimodal AI
- Build Your Own (From Scratch)
- Large Language Model (LLM)
- AI Tutor
- AI Coding Agent
- ChatGPT
- Memory for a Personal AI Assistant
- Neural Network
- AI Coding Agent
- Using Claude Code and Codex Effectively
- Loop Engineering
- Graph Engineering
- Harness Engineering
- AI and ML System Design
- Numbers Every AI Engineer Should Know
- Design ChatGPT: Training to Serving (End to End)
- Design a RAG System (Chat with Your Documents)
- Design Memory for a Personal AI Assistant
- Design a Deep Research Agent
- Design a Real-Time Voice AI Agent
- Design an LLM Inference Platform (vLLM-as-a-Service)
- Tools and Libraries
- PyTorch
- TensorFlow
- Keras
- Ollama
- Hugging Face
- LangChain
- LangGraph
- Data Analysis and Visualization
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Machine Learning Fundamentals
- Machine Learning Overview
- Supervised and Unsupervised Learning
- Self-supervised Learning
- Contrastive Learning
- Linear Regression
- Logistic Regression
- Gradient Descent
- Maths behind Gradient Descent
- Loss Functions
- Hyperparameter Tuning
- Epoch, Batch, Batch Size, and Iteration
- Embeddings
- Logits, Cross-Entropy
- Deep Learning
- Neural Networks
- Feed Forward Neural Networks (FFNNs)
- Activation Functions
- Backpropagation
- Dropout
- Layer Normalization and Batch Normalization
- Optimizers
- Generative AI
- Variational Autoencoders (VAEs)
- Autoregressive Models
- Diffusion Models
- Transformers
- Attention Mechanism
- Large Language Models (LLMs)
- Reasoning Models
- Encoder–Decoder Architecture
- Multimodal Generative Models
- Optimization and Scaling Techniques
- MLOps and LLMOps
- LLM Inference Engineering
- LLM Inference Bottleneck Analysis
- LLM Inference Performance Metrics
- Data Management and Versioning
- Model Development and Training
- Evaluation and Testing
- Model Deployment and Serving
- Cloud vs On-device Deployment
- Monitoring and Logging
- Infrastructure and Platform
- Reinforcement Learning
- Exploration vs Exploitation
- Proximal Policy Optimization (PPO)
- Reward Models
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
- RLHF (Reinforcement Learning from Human Feedback)
- Research Papers
- Attention Is All You Need
- Distilling the Knowledge in a Neural Network
Process
- 1
Watch the Demo Session Recording
Watch the demo session recording to see what you'll learn and how we break down the internal workings of complex concepts into simple, clear explanations. This video covers all the questions you may have.
- 2
Chat with us on WhatsApp to complete the enrollment
Message us on WhatsApp to enroll. Once the enrollment is complete, you get instant access to all the recordings, the project source code, and the class notes.
Fee: ₹1,20,000 (INR) or $1,250 (USD)
You'll chat with a real person. We've chosen NOT to integrate AI.
- 3
Learn at Your Own Pace
Learn from 40 recorded classes, each 2 to 3 hours long, which is about 100 hours of learning. Watch them on your own schedule and rewatch any class whenever you need.
- 4
Learn the Internals
You will go beyond using tools and libraries to understand how things actually work under the hood. By learning the internals, you build the deep understanding that sets you apart and makes you a stronger engineer.
- 5
Learn System Design
System design is a key part of AI and Machine Learning interviews. You will learn the right patterns, develop a strong product mindset, and think like a true product engineer.
- 6
Build Your Own Projects From Scratch
You will build your own Large Language Model (LLM), AI Tutor, AI Coding Agent, ChatGPT, Memory for a Personal AI Assistant, and Neural Network from scratch. Building them yourself is the best way to truly understand how they work.
- 7
Revise with Class Notes
You also get the class notes, so you can quickly revise the concepts before your interviews without rewatching the full recordings.
- 8
Get a High-Paying Job
Finally, with your efforts, you will be able to get the job you desired.
Your Teacher
Amit Shekhar
Founder @ Outcome School • IIT 2010-14 • I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.
Our Students' Growth
Got Salary Hike
CTC Change: 4 LPA → 24 LPA, 9 LPA → 24 LPA, 13 LPA → 46 LPA, 20 LPA → 60 LPA
Notable Transition
Software Engineer → VP of Engineering, Software Engineer → Staff Engineer
Open Source
Worked on Open Source projects and received interview calls from Top Companies
Our students got placed in top companies thanks to their efforts.
What students are saying about us
Kiran Rao Chavan
Hi Amit, with every class that I see and hear from you, silently learning the techniques of system design skills in developing AI from scratch is phenomenal and I was impressed with "Attention is all you need" Paper explanation was next level. I have read YOLO Paper for image processing and writing python scripts after multiple attempts, but the way you give real time examples and the analogies that you give while explaining toughest things - you make it look like a cake walk. KVCache, Paged Attention, vLLM, Quantization, each and every concept that you teach is exceptional.
Aman Shekhar
Outcome School delivers deep, practical AI/ML mastery that standard degree programs simply cannot match. Under Amit’s expert supervision, the curriculum shifts you from an AI consumer to an AI builder, instead of just using tools like Claude, you build autonomous AI agents, build LLMs from scratch, playing with KVCache, code Transformer architectures line by line etc. The best part was understanding the paper "Attention is all you need", not only that now I can read and understand any whitepaper. This rigorous, code-first approach has completely transformed my career trajectory, elevating me to the official AI Focal Point in my organization and the go-to expert for all complex technical queries.
Khush Panchal
Amit has been an incredible mentor to me. Under his guidance, I navigated the world of open source, which took my journey to the next level. Amit's presence works as a catalyst in the journey of learning and growing. His insights were incredibly helpful, whether it was cracking firms like Microsoft and Blinkit, negotiating salaries, or making career decisions. His mentorship also enabled me to create major open-source libraries. I am grateful to have Amit as a lifelong mentor and look forward to creating a positive impact with him.
Fee
Includes lifetime access to 40 recorded classes (about 100 hours), the complete source code of all the projects, and the class notes. To enroll, chat with our team on WhatsApp.
You'll chat with a real person. We've chosen NOT to integrate AI.
Frequently asked Questions
Why Outcome School AI and Machine Learning Program?
Software engineers like you join Outcome School AI and Machine Learning Program to get a high-paying job in AI and Machine Learning. In the recorded classes, we go deep into the internals of how things work and master system design, building the strong foundation that leads to a successful outcome.
What are the eligibility requirements for this program?
Working tech professionals who want to learn AI from scratch and transition into high-paying AI roles through a strong understanding of AI, Machine Learning and System Design.
What are the prerequisites?
Any programming language. Even without Python experience, if you know any other language, you can learn Python while building the projects.
How are the classes delivered?
All the classes are recorded, so you can learn at your own pace from anywhere in the world. You can watch them on your own schedule and rewatch any class whenever you need.
What do I get in this program?
You get 40 recorded classes, each 2 to 3 hours long, which is about 100 hours of learning. Along with the recordings, you get the complete source code of all the projects and the class notes.
How long will I have access to the recordings?
You get lifetime access. The recordings, the project source code, and the class notes always stay available to you, so you can come back and revise them whenever you need.
When can I join the program?
You can join anytime. Once the enrollment is complete, you get access to the recordings and can start learning right away.
What is the time commitment for the program?
The program has about 100 hours of recorded classes. We recommend spending 5-10 hours per week, and you can distribute these hours across the week according to your work schedule.
What is the fee for this program?
The fee is ₹1,20,000 (INR) or $1,250 (USD). To enroll, please chat with our team on WhatsApp.
You'll chat with a real person. We've chosen NOT to integrate AI.