Become job-ready by:

• Building AI systems (not just models)

• Using LLMs + RAG + APIs effectively

• Shipping deployable projects

⏱️ Timeline

Week 1–2 → Python + Data
Week 3–4 → ML Basics
Week 5–6 → Deep Learning
Week 7–9 → GenAI + RAG (Core Focus)
Week 10–12 → Production + Deployment

🟢 Phase 1 — Python + Data (Week 1–2)

What you need

• Python fundamentals

• Data handling

• Basic problem solving

Resources

• Python (freeCodeCamp)

• Corey Schafer Python Playlist https://www.youtube.com/c/Coreyms/playlists

• Pandas

Output

• 20–30 problems

• 3 scripts (API call, CSV processing, automation)

🟡 Phase 2 — ML Basics (Week 3–4)

What you need

• Regression, classification

• Bias/variance

• Evaluation metrics

Resources

• Andrew Ng ML https://www.coursera.org/learn/machine-learning

• StatQuest (clear explanations) https://www.youtube.com/c/joshstarmer

Output

• Spam classifier

• Price predictor

🔵 Phase 3 — Deep Learning (Week 5–6)

What you need

• Neural networks (intuition)

• Loss, training loop

Resources

• DeepLearning.AI https://www.coursera.org/specializations/deep-learning

• PyTorch (hands-on)

Output

• Train a simple NN (MNIST or text classifier)

🔥 Phase 4 — GenAI + RAG (Week 7–9) [MOST IMPORTANT]

What you need

• LLM usage (APIs)

• Prompt engineering

• RAG systems

• Evaluation

🧠 1. LLM Fundamentals

Learn:

• Tokens, context window

• Temperature, top-p

• Function calling

Resources

• OpenAI Cookbook https://github.com/openai/openai-cookbook

• Andrej Karpathy LLM Intro

✍️ 2. Prompt Engineering (Practical, not hype)

Learn:

• Few-shot prompting

• System prompts

• Structured outputs (JSON)

Resources

• Prompt Engineering Guide

https://www.promptingguide.ai

• OpenAI Prompting Best Practices https://platform.openai.com/docs/guides/prompt-engineering

📚 3. RAG (CORE SKILL)

Learn:

• Embeddings

• Chunking strategies

• Retrieval vs generation

Resources

• LangChain RAG Guide https://python.langchain.com/docs/use_cases/question_answering

• LlamaIndex RAG Docs https://docs.llamaindex.ai/en/stable/use_cases/q_and_a/

• Pinecone RAG Explained https://www.pinecone.io/learn/retrieval-augmented-generation/

⚙️ 4. Build Real RAG System

Stack:

• OpenAI API

• LangChain / LlamaIndex

• FAISS / Pinecone

Project

• Chat with PDFs

• Chat with company docs

📊 5. Evaluation (Most people skip this)

Learn:

• Hallucination detection

• Response quality checks

• Prompt iteration

Resources

• OpenAI Evals https://github.com/openai/evals

Output

• 2 production-quality AI apps

• Proper RAG pipeline

🟣 Phase 5 — Production + Deployment (Week 10–12)

What you need

• APIs (FastAPI)

• Deployment

• Logging

Resources

• FastAPI

https://fastapi.tiangolo.com

• Docker

• Deployment (Render / AWS basics) https://render.com/docs

Output

• 3–4 deployed apps

• Public URLs

🧠 Math (Minimal, Just-in-Time)

Learn only when needed:

• Vectors

• Gradients

• Probability basics

Resources

• 3Blue1Brown

Rule

• Max 2–3 days

• Only when confused

🧩 Final Skill Stack

You should have:

• Python

• ML basics

• LLM + Prompting

• RAG systems

• APIs + backend

• Deployment

• 3–5 real projects

⚠️ Mistakes to Avoid

• Overfocusing on ML theory

• Ignoring GenAI stack

• Not evaluating outputs

• Not deploying apps

🔥 Reality

Most AI jobs today require:

• LLM usage

• RAG pipelines

• API integration

• System thinking

NOT:

• Training models from scratch

“If you skip Phase 4 or do it superficially, you are not job-ready.”

Because that’s the real filter right now.

Follow Pratham Kohli for more Insights 🙌🏻

Instagram link: Link

Youtube Link: Link

Why Is Every Company Laying Off ? : Link

Want to Switch 10x faster as a Software Engineer? : Link

This is Day 2 of

Ship It 🚢 or Skip It ❌

where I try the latest Viral AI trends for you, so that you don’t waste your time on them 🙌🏻

The Absolute Beginner’s Guide to Running Local AI Models on Your Mac or PC for Free

Running artificial intelligence directly on your own computer used to require a degree in computer science. Today, tools like Ollama and Claude Code make it as simple as downloading an app and typing a few commands—completely private, powerful, and offline-ready.

Here is your revised guide to getting both the “engine” (Ollama) and the “pilot” (Claude Code) ready for action.

Step 1: Download and Install Ollama

Think of Ollama as the “engine room” for local AI. It manages the models and sets up a background service so other tools can talk to it. [1]

1. Head over to the official Ollama Download Page.

2. Click the download button for your operating system (Mac, Windows, or Linux).

3. Open the installer and follow the on-screen prompts.

4. Once installed, a small llama icon will appear in your system tray or menu bar—this means the engine is ready!

Step 2: Install Claude Code (The Missing Piece!)

Before we can run Claude, we need to install the software itself. Claude Code is a professional tool that lives in your terminal. [1]

The Easy Way (Recommended):
Open your terminal and paste the command below that matches your computer.

• For Mac or Linux:

curl -fsSL https://claude.ai/install.sh | sh

• This is the best method because it auto-updates in the background so you always have the latest version.

• For Windows:
  Run this command in PowerShell or CMD (you don’t even need to be an Administrator):

# Note: Ensure you have Node.js installed first for the best experience
npm install -g @anthropic-ai/claude-code

Quick Verification:
After the install finishes, type claude --version in your terminal. If you see a version number, you’re good to go!

Step 3: Login to Claude

On your first run, Claude needs to know who you are. Type claude in your terminal. This will automatically open a browser window for you to log in with your Claude account.

• Pro Tip: If you have a Claude Pro or Max subscription, logging in here connects your plan directly to the terminal tool.

Step 4: Pull Your First Local AI Model

Now we need to download a “brain” for Ollama to use. For coding, qwen2.5-coder is the star of the show. Paste this into your terminal:

ollama pull qwen2.5-coder:7b

Ollama will download the model file (about 4.7 GB). Grab a coffee while the progress bar finishes!

Step 5: Start Claude with Your Local Model

Now for the magic. Navigate to your project folder (using cd your-project-folder) and launch Claude to use your local Ollama engine:

ollama launch claude --model qwen2.5-coder:7b

Claude is now running entirely locally on your machine, using your own files directly!

Which Model Should You Use? (The Hardware Cheat-Sheet)

Don’t scare your computer! Choose a model that fits your hardware:

🍏 For Apple Silicon MacBooks (M1, M2, M3, M4)

• Base Chips (8GB–16GB RAM):

  • 🏆 Best Choice: Phi-4 Mini (3.8B) — Fast and very smart for its size.

  • ⚖️ Okayish Alternative: Llama 3.1 8B — Good, but might make an 8GB Mac feel a bit sluggish.

• Pro Chips (18GB–36GB RAM):

  • 🏆 Best Choice: DeepSeek R1 14B — Incredible reasoning for complex code.

• Max & Ultra Tiers (36GB–128GB+ RAM):

  • 🏆 Best Choice: Qwen3 Coder 30B — The ultimate “local expert” experience.

🖥️ For Windows & Linux PCs

• Entry-Level (4GB–8GB RAM): Phi-4 Mini (3.8B)

• Mid-Range (8GB VRAM GPU): Qwen3 8B

• High-End (24GB VRAM/RTX 4090): Qwen3 Coder 30B (MoE)

The Reality Check: Is Local Right For You?

Running models locally is an amazing way to keep your data private and avoid monthly API costs.

However, if you are using a base-model MacBook (like an M1 or M2 MacBook Air), local AI is heavy work. Your laptop might get warm, and complex code can take time to generate.

My advice?

Local is great for learning and small fixes. But for professional, daily software development on a base-tier machine, don’t rely on it- pay the $20 a month for the cloud version of Claude. The extra speed and elite power are worth the investment for any serious developer unless you have a monstrous computer, then yes run the best open source models - Kimi K3 is the one leading right now!

📸 Profile Essentials

• [ ] Professional headshot: Clear, well-lit photo with neutral background (no selfies). Use ChatGPT for creating a professional headshot if you don’t have one already.

• [ ] Custom background banner: Feature code, tech stack icons, or a clean abstract design. Tons of examples on Canva.

• [ ] SEO-optimized headline: Include job title, key skills, and value proposition

  • Example: “Full Stack Software Engineer | React | Node.js | AWS | Building scalable web applications that solve real-world problems” Keywords help in search.

• [ ] Custom URL: www.linkedin.com/in/yourname (no random numbers)

• [ ] “Open to work” setting: Enable visibility to recruiters only. Don’t enable it for all. It will do more harm and make you look desperate.

• [ ] Location: Set to your target job market (even if remote)

💼 About Section

• [ ] First 3 lines: Compelling hook and unique value proposition. What do you do? Why should someone look at your profile ? What are your skills ?

• [ ] Keyword optimization: Include technical skills and relevant terms to your Industry. Hugely underrated

• [ ] Career narrative: Brief story about your engineering journey. How you started, where are you now.

• [ ] Quantifiable achievements: Use numbers/percentages to demonstrate impact. Numbers have a psychological impact!

• [ ] Clear Specialization: Define your technical niche (e.g., “Backend specialist with focus on microservices”)

• [ ] Call to action: Invite connections or outline what roles you’re seeking. Backend, frontend, marketing, influencer relations, anything!

• [ ] Contact info: Professional email address. No dummy addresses like pk@gmail.com, stonecold23@gmail.com please!

🏆 Experience Section

• [ ] Reverse chronological order: Most recent roles first. Trate this section like your Resume only now you have more space and you don’t need it to be 1 pager!

• [ ] Action verbs: Begin bullet points with “Developed,” “Engineered,” “Architected”.

• [ ] Technical achievements: Highlight specific projects/challenges

• [ ] Metrics: Include numbers (e.g., “Reduced load time by 40%”)

• [ ] Tech stack detail: List technologies used for each role

• [ ] Problem-solution format: Describe problem, solution, and impact

• [ ] Promotion history: Show growth within companies

🛠️ Skills & Endorsements

• [ ] Top 5 skills: Pin most relevant technical skills

• [ ] 50+ skills: Complete the maximum allowed skills

• [ ] Industry-specific keywords: Include both programming languages and frameworks

• [ ] Endorsements: Get 10+ endorsements for top skills. Ask your coworkers to endorse you and you do it in return. Yes this may look cringe but adds a lot more value to your profile. So what do you want your career ? or thinking that you may look cringe to someone you work with for a month ?

• [ ] Skill assessment badges: Complete LinkedIn’s skill assessments. Easy way to top up your profile!

• [ ] Regular updates: Add new skills as you learn them. Feeling AI yet ?

🎓 Education & Certifications

• [ ] Formal education: All relevant degrees and bootcamps

• [ ] Technical certifications: AWS, Azure, Google Cloud, etc.

• [ ] Online courses: Udemy, Coursera, Pluralsight achievements

• [ ] Self-taught skills: Highlight personal learning journey

• [ ] GitHub Learning Lab or other technical course completions

🏗️ Featured Section

• [ ] GitHub projects: Link to 2-3 best repositories

• [ ] Technical blog posts: Article about solving coding challenges

• [ ] Portfolio website: Link to personal site showcasing work

• [ ] Demo videos: App demonstrations or coding tutorials

• [ ] SlideShare presentations: Technical talks or presentations

• [ ] Relevant case studies: Detailed write-ups of major projects

🔍 Activity & Engagement

• [ ] Regular posting: Share technical content weekly

• [ ] Comment on industry posts: Engage with thought leaders

• [ ] Share project updates: Post about what you’re building

• [ ] Participate in groups: Join software development communities

• [ ] Create original content: Write articles about technical topics

• [ ] Consistent engagement: Weekly interaction with network

• [ ] Celebrate others: Acknowledge colleagues’ achievements

👥 Recommendations

• [ ] 6+ recommendations: Both from supervisors and peers. Get as many as you can, they act like proof for recruiters and strangers on the Internet.

• [ ] Diverse perspectives: From different roles (PM, designer, fellow engineers)

• [ ] Technical specificity: Recommendations that mention your skills

• [ ] Request specific focus: Ask recommenders to highlight key strengths

• [ ] Give recommendations: Recommend others to encourage reciprocation

🌟 Additional Elements

• [ ] Follow target companies: Stay updated on potential employers

• [ ] Showcase volunteering: Technical mentorship or open source contributions

• [ ] Language proficiency: List programming languages and human languages

• [ ] Set job preferences: Customize your career interests

• [ ] Accomplishments section: Patents, publications, or special projects

• [ ] Apply #OpenToWork frame: Only if actively job searching

• [ ] Creator mode: Consider activating if posting technical content

• [ ] Profile in English: Ensure main profile is in English (with translations if needed)

📱 Mobile Optimization

• [ ] Preview on mobile: Ensure readability on smaller screens

• [ ] Check responsiveness: Text should be skimmable

• [ ] Enable notifications: Don’t miss recruiter messages

🤖 Backend Optimization

• [ ] All-Star status: Complete 100% of profile sections

• [ ] 30+ connections: Minimum for good visibility

• [ ] Regular profile updates: Refresh content monthly

• [ ] Custom job alerts: Set up alerts for dream companies

• [ ] Privacy settings: Review what recruiters can see

• [ ] Activity broadcast control: Manage visibility of profile changes

Next Steps:

1. Engage with creators in your Niche on Linkedin

2. Then try to start posting as well

3. No you don’t have to become a creator, but post the projects you made

4. The learnings you gained while building things

5. Keep it professionals

How to Start Building a LinkedIn Presence:

What Should you Post?

Ques: Are you seriously looking to Switch?
Do you want all the resources and roadmaps at one place ?

Then check out this 100+ page guide I created with a proper roadmap and all the resources that you will need to make your Dream Switch in 2026 !!

This is the Guide I used to crack:

• Google

• Meta

• Microsoft [ Twice ]

• Amazon

• Salesforce

• ServiceNow

• Oracle and More
  

Note: This guide is Only for Working Professionals Looking to SWITCH, NOT for Students!

Link: https://superprofile.bio/vp/switch-10x-faster