Hey guys i am a software developer of 20 years old . I had done all that top level stuff ( Using LLM’s as api’s , rag , embeddings , chatbots etc ) . I did not find those interesting to build coz it’s like i am just doing some conventional programming using llm or rag or some other gen ai tool . My motive is to build something and learn something which is unique . i don’t wanna be the one in a group of sheeps . For that we need to learn deeper , gain a lot of knowledge around the domain you are interested in . so the domain i chosen is this . i am currently in the course 2 of deep learning specialization and my final goal is to become an entrepreneur . I want to build a product . I am not fit for this 9 to 5 job at all but i need experience to achieve my final goal . so atleast i want to do this 9 to 5 in a very big company doing the work i am interested in ( regarding ML and DL ) and getting a good amount of salary ( may be 50 LPA ) . So i had put a deadline of Feb 2026 for that to get the desired job but it is not my final goal as i had said this is a stepping stone . so for my first step i had created a detailed roadmap until feb 2026
ROAD MAP
Phase 1: Deep Foundations & LLM Mastery (Weeks 1-12)
Weeks 1-2: Finish Foundations (Aug 21 - Sep 3)
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Weekly Goal: Complete the deep learning Specialization and establish a strong DSA routine. -
ML & DL Theory:-
Complete Course 2: Hyperparameter Tuning, Regularization and Optimization.
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Complete Course 3: Structuring Machine Learning Projects.
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Complete Course 4: Convolutional Neural Networks.
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Complete Course 5: Sequence Models.
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Take detailed notes, focusing on why things work, not just what they are.
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Project & Practical Skills:- Ensure your Python/PyTorch environment is perfectly set up (Conda/venv, Jupyter, VSCode).
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DSA:-
Ramp up to 2 problems/day from NeetCode 150.
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Focus on Arrays & Hashing, Two Pointers, Sliding Window.
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Weeks 3-4: PyTorch from Scratch (Sep 4 - Sep 17)
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Weekly Goal: Build neural networks from scratch in PyTorch to gain a fundamental understanding.
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ML & DL Theory:
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Watch and CODE ALONG with Andrej Karpathy’s “makemore” series (Zero to Hero Playlist).
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Part 1: Bigram model.
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Part 2: MLP.
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Part 3: Activations & Gradients, BatchNorm.
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Part 4: Becoming a Backprop Ninja.
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Project & Practical Skills:
- Create a new GitHub repo named deep-learning-foundations and push your Karpathy code there.
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DSA:
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Maintain 2 problems/day.
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Focus on Stack, Binary Search, Linked Lists.
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Weeks 5-7: Deconstructing Transformers (Sep 18 - Oct 8)
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Weekly Goal: Understand and build the Transformer architecture from the ground up.
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ML & DL Theory:
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Read the paper: “Attention Is All You Need.” Don’t worry if you don’t get it all.
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Read Jay Alammar’s “The Illustrated Transformer.” This will make it click.
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The Main Event: Watch and CODE ALONG with Karpathy’s “Let’s build GPT.”
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Implement a bigram model, then self-attention, then multi-head attention, then a full Transformer block.
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Project & Practical Skills:
- Push your nanoGPT implementation to your GitHub repo. Add a detailed README explaining what you built.
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DSA:
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Ramp up to 3 problems/day. You’re in a rhythm now.
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Finish NeetCode 150. Start on the “NeetCode All” list, focusing on Trees & Tries.
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Weeks 8-12: The Spike Project - Setup & Fine-tuning (Oct 9 - Nov 12)
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Weekly Goal: Define, set up, and begin fine-tuning a state-of-the-art LLM for a unique task.
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ML & DL Theory:
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Research fine-tuning techniques: Read blogs/papers on LoRA and QLoRA.
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Understand concepts like quantization and parameter-efficient fine-tuning (PEFT).
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Project & Practical Skills:
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Week 8: Define your project. (e.g., “Fine-tune Mistral-7B to be a Python Design Pattern expert”).
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Week 8: Find and curate your dataset. This might involve writing a web scraper.
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Week 9: Set up your cloud environment (AWS EC2, GCP, or Lambda Labs). Learn to use ssh and tmux.
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Week 10: Data preprocessing and tokenization using the Hugging Face datasets and tokenizers libraries.
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Week 11-12: Write and run your fine-tuning script using the Hugging Face transformers library and PEFT.
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Week 12: Set up experiment tracking with MLflow or Weights & Biases to log your loss curves and results.
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DSA:
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Maintain 2-3 problems/day.
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Focus on Heap / Priority Queue, Backtracking, Graphs.
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Phase 2: Productionization & System Design (Weeks 13-18)
Weeks 13-15: Evaluation & High-Performance Inference (Nov 13 - Dec 3)
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Weekly Goal: Evaluate your fine-tuned model and optimize it for fast inference.
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ML & DL Theory:
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Learn about evaluation metrics for generative models (Perplexity, BLEU).
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Study inference optimization: Quantization (GPTQ, GGUF), Flash Attention.
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Project & Practical Skills:
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Week 13: Evaluate your model. Generate sample outputs. Compare it to the base model.
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Week 14: Build a simple demo UI with Gradio or Streamlit.
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Week 15: Apply quantization (e.g., using bitsandbytes) to your model. Benchmark the speed and VRAM difference.
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DSA:
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Maintain 2-3 problems/day.
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Focus on Advanced Graphs, 1-D & 2-D Dynamic Programming.
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Weeks 16-18: API, Deployment & ML System Design (Dec 4 - Dec 24)
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Weekly Goal: Turn your model into a robust API and start mastering ML System Design.
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Project & Practical Skills:
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Week 16: Build a production-ready API for your model using FastAPI.
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Week 17: Dockerize your FastAPI application. Write a Dockerfile.
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Week 18: Push your final project code to a new, polished GitHub repository.
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ML & DL Theory:
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Start Reading “Designing Machine Learning Systems” by Chip Huyen. This is your new bible.
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Week 16: Read Chapters 1-4.
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Week 17: Read Chapters 5-8.
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Week 18: Read Chapters 9-11.
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DSA:
- Switch to timed practice. Try to solve 2 Medium problems in 45-50 minutes.
Phase 3: Interview Blitz & Final Polish (Weeks 19-24)
Weeks 19-20: Portfolio Polish & Broadening Horizons (Dec 25 - Jan 7)
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Weekly Goal: Create your “marketing materials” and gain conversational fluency in other GenAI areas.
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Project & Practical Skills:
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Week 19: Write a detailed blog post on Medium or a personal blog about your Spike Project. Explain the problem, data, process, and results.
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Week 19: Create a killer README for your project’s GitHub repo. Include GIFs of the demo.
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Week 19: Publish your LoRA adapter to the Hugging Face Hub.
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Week 20: Rapidly build a simple app using the Hugging Face diffusers library and a pre-trained Stable Diffusion model.
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Week 20: Do the same for TTS using a pre-trained Bark or Coqui model.
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ML & DL Theory:
- Watch a few high-level YouTube explainers on Diffusion Models and modern TTS architectures. The goal is fluency, not implementation.
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DSA:
- Continue timed mock sessions. Start including 1 Hard problem in your rotation.
Weeks 21-23: Mock Interview (Jan 8 - Jan 28)
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Weekly Goal: Simulate the real interview experience relentlessly.
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Interview Prep:-
ML System Design: Start doing mock interviews. Find a peer or use a platform. Practice a new problem every other day (e.g., Design YouTube Recommendations, Design a Spam Filter). Use a structured approach.
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Behavioral: Prepare 5-6 detailed stories about your projects (Valuefy, CodeOwlAI, Spike Project) using the STAR method. Write them down.
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Resume: Finalize your resume. Quantify everything. Get feedback from multiple people.
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LinkedIn: Update your LinkedIn to reflect all your new skills and projects.
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DSA:
- Do at least 3 full-length (1-hour) mock coding interviews per week.
Week 24 & Onwards: GO TIME (Jan 29 - Feb)
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Weekly Goal: Start applying, networking, and acing interviews.
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Job Hunting:-
Finalize your target company list.
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Reach out to your network for referrals.
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Start applying. Aim for quality over quantity.
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Schedule your first-round interviews
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I had created a saas product too . you can take a look if you want to judge about my capacity
link : https://codeowlai.com
This was made by 3 members team over 4 months on 3 cycles . You can take a look as well . But we are thinking of making it opensource for some reasons . I want to build much much bigger projects than these . For that i want to connect with like minded people . you can take a look at my roadmap as well . If anyone is interested to connect i am dropping a discord link ( just created ) we can create a team . we can share ideas , we can learn together . In final we can create a product on our own . Let’s help each other . Let’s motivate each other . Thank you for reading this . See you in discord
{mentor edit: link removed - not allowed by the Code of Conduct}