I'm always excited to take on new projects and collaborate with innovative minds.

Mail

say@bplbmki.in

Website

https://www.bplbmki.in/

hey

Plan A) 12AM To 12AM  Everyday Plan Till 2035

 

 

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Saturday Sunday :  

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March June Sept Dec:  4xSat + 4xSun → TOTAL ~120 HRS In a Month

→ 4Hrs Coding: https://niteshsynergy.com/code. | ResearchProblemsCoding1CrPlus | Code1CrPlusMission

→ 4Hrs DevOps SRE: DevOps With MultiCloud → distributed-systems-interview-notes

→ 4Hrs JAVAMS Kafka React : javams | kafka | ui | SQL

→ 2Hrs Spring Boot: SpringBoot/Security/Batch/Cloud/SQL/NoSQL/JPA/Hibernate/RestAPI/gRPC/GraphQL/SpringAI

→ 2Hrs Java Tech: JavaBasics/FC/WC/IC/GC/String/EH/Collection/Map/Java8-17-21/JVM/MultiThread/OOP/LLD/DP/Solid/HLD

      OR   
→ japanese / chinese / russian
→ splunk
 

  OR  

Python AI Track:

 => Python Track :Python → SQLAlchemy → Data Wrangling → Math for AI

  • Python Advanced (decorators, generators, async/await, type hints, design patterns).
  • SQLAlchemy ORM (relationships, migrations with Alembic, query optimization).
  • Data Wrangling (NumPy, Pandas, JSON handling, CSV parsing, data cleaning).
  • Math for AI (linear algebra, probability, statistics, calculus) → https://niteshsynergy.com/math    

=> AI Product Engineering Track

FastAPI → RAG → Fine-tuning LLMs → Multi-agent Systems → Speech AI → + More Skills

1. Core AI Backend

  • FastAPI Advanced (streaming AI responses via WebSockets, background tasks, auth).
  • LangChain (Chains, agents, tools, custom retrievers, memory).
  • LangGraph (for advanced workflow orchestration).

2. AI Retrieval & Customization

  • RAG (Retrieval-Augmented Generation) with FAISS, ChromaDB, Pinecone.
  • Fine-tuning LLMs with LoRA, PEFT for your coding assistant persona.
  • Embedding Models for code + documentation search.

3. AI Agent Intelligence

  • Multi-agent Systems (CrewAI, AutoGen) — one agent for coding help, one for explanations.
  • Tool Integration (e.g., code execution, GitHub API, StackOverflow search).
  • Context Management so ChampaChat remembers past conversations.

4. AI Modalities

  • Speech AI → Whisper (speech-to-text), Coqui TTS / ElevenLabs (text-to-speech).
  • Code Parsing & Analysis → Tree-sitter, Pygments for syntax-aware responses.

5. AI Deployment & Scaling

  • Self-hosting Small LLMs (Mistral, LLaMA, DeepSeek) with quantization.
  • Docker & Docker Compose for environment portability.
  • Redis / Memcached for AI response caching.
  • Async Concurrency & Streaming to handle multiple users smoothly.

6. AI Monetization & Analytics

  • User Authentication & Role Management (FastAPI + JWT/OAuth).
  • Billing Integration (Stripe / Razorpay) for premium API access.
  • Usage Analytics (PostHog, Mixpanel) to track popular features.

 

 

                                                                          OR

→ Python + AI →

Phase 1: Foundation in Python, AI/ML/DL

1. Python for AI/ML/DL

  • Basic Python Concepts
    • Variables, data types, loops, functions, classes
    • Libraries: NumPy, Pandas, Matplotlib, Seaborn
    • File handling, data I/O (CSV, JSON, Excel, SQL databases)
    • Virtual environments, package management (pip, conda)

2. Data Preprocessing and Feature Engineering

  • Data cleaning (handling missing values, duplicates)
  • Data transformation (scaling, normalization)
  • Encoding categorical variables (One-hot encoding, label encoding)
  • Feature selection and extraction techniques

3. Supervised Learning Algorithms

  • Linear Regression, Logistic Regression
  • Decision Trees, Random Forests, Gradient Boosting
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (KNN)
  • Naive Bayes Classifier
  • Model evaluation techniques: Accuracy, Precision, Recall, F1-Score, ROC-AUC

4. Unsupervised Learning Algorithms

  • K-Means Clustering, Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Dimensionality reduction techniques
  • Anomaly detection algorithms (Isolation Forest, DBSCAN)

5. Deep Learning Basics

  • Neural Networks (Perceptrons, Feedforward Neural Networks)
  • Backpropagation and Gradient Descent
  • Activation functions: Sigmoid, ReLU, Tanh
  • Loss functions: Mean Squared Error, Cross-Entropy
  • Optimizers: Stochastic Gradient Descent (SGD), Adam

 

 

Phase 2: Advanced Machine Learning & NLP

1. Neural Networks and Deep Learning

  • Convolutional Neural Networks (CNNs) for image classification and object detection
  • Recurrent Neural Networks (RNNs) and LSTM (Long Short-Term Memory) for sequence processing
  • Generative Adversarial Networks (GANs) for data generation
  • Transfer learning and pre-trained models (VGG16, ResNet, Inception)

2. Natural Language Processing (NLP)

  • Text Preprocessing: Tokenization, Stemming, Lemmatization
  • Bag of Words (BoW), TF-IDF (Term Frequency-Inverse Document Frequency)
  • Word Embeddings: Word2Vec, GloVe, FastText
  • Sequence Models: RNNs, LSTMs, GRUs
  • Transformers: Understanding attention mechanisms, self-attention
  • BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformers)
  • Text classification, sentiment analysis, Named Entity Recognition (NER)

3. Reinforcement Learning (RL)

  • Introduction to Q-Learning
  • Deep Q Networks (DQN)
  • Policy Gradient Methods: REINFORCE, Actor-Critic methods
  • Applications in robotics, game theory, and optimization problems

 

Phase 3: Advanced AI Architectures

1. Transformer Models & Attention Mechanism

  • Self-attention and Multi-head attention mechanisms
  • Transformer architecture breakdown: Encoder and Decoder
  • GPT (Generative Pre-trained Transformers) architecture
  • BERT (Bidirectional Encoder Representations from Transformers) and its applications
  • Fine-tuning pre-trained models for specific tasks (classification, translation, etc.)

2. Advanced Deep Learning Topics

  • Attention-based models: Transformer, GPT, BERT
  • Pre-trained models (GPT-2, GPT-3, T5, BERT) and fine-tuning them for downstream tasks
  • GPT-3: Understanding its architecture, capabilities, and API usage
  • Techniques for training large models (Gradient Clipping, Mixed Precision Training, etc.)

3. Large-Scale Data Handling & Big Data Tools

  • Apache Hadoop for distributed storage (HDFS)
  • Apache Spark for large-scale data processing
  • Real-time data processing with Apache Kafka
  • Distributed data storage solutions: NoSQL (MongoDB, Cassandra) and SQL-based storage

 

 

Phase 4: Deployment & Real-World Applications

1. Model Deployment Techniques

  • Deploying ML models using Flask/Django or FastAPI
  • Docker: Containerizing AI models for deployment
  • Kubernetes for orchestrating containerized applications
  • Building REST APIs for AI models (using FastAPI or Flask)

2. Cloud Deployment

  • Using AWS, GCP, Azure to deploy AI models
  • Serverless computing (AWS Lambda, Google Cloud Functions)
  • Model versioning and deployment management (e.g., with TensorFlow Serving)

3. Scalable AI Architectures

  • Setting up multi-GPU training for large AI models
  • Implementing model parallelism and data parallelism
  • Real-time inference for production AI applications
  • Implementing load balancing and caching strategies for fast inference

4. AI in Real-Time Applications

  • Chatbots and conversational agents (e.g., using Rasa or Dialogflow)
  • Implementing real-time language models like ChatGPT
  • Personal assistants (voice recognition, question-answering systems)

 

 

Phase 5: Ethics, Security, and Bias Mitigation

1. Ethics in AI

  • Fairness, accountability, and transparency in AI
  • Understanding and mitigating bias in AI models
  • AI for Social Good: Leveraging AI to solve real-world problems (e.g., healthcare, climate change)

2. Security in AI Models

  • Securing AI models against adversarial attacks
  • Model robustness and evaluation
  • Protecting data privacy in machine learning (e.g., differential privacy, Federated Learning)

3. AI Regulation & Governance

  • Adhering to AI regulations (GDPR, CCPA, etc.)
  • Privacy concerns in AI model deployment
  • Transparent decision-making models and explainability

 

Phase 6: Research and Innovation

1. Continuous Learning & Experimentation

  • Reading and implementing recent AI papers (e.g., transformer models, BERT, GPT)
  • Experiment with the latest state-of-the-art models like GPT-3, BERT, etc.
  • Contribution to open-source AI projects

2. Building a Portfolio of AI Projects

  • Start working on AI tools similar to ChatGPT or DeepSeek (e.g., a chatbot, question-answering model, etc.)
  • Participate in AI/ML competitions like Kaggle to gain practical experience

 

 

Practical AI Projects

  1. Chatbot Development: Build a question-answering chatbot using BERT or GPT-2/GPT-3.
  2. Image Recognition : Develop a facial recognition or object detection model using CNNs.
  3. Text Generation: Use GPT-2/3 to generate human-like text based on a prompt.
  4. Recommendation System: Build a movie or product recommendation system using collaborative filtering or deep learning.

Phase 1: Foundations in AI, Machine Learning & Deep Learning

1. Python for AI & Machine Learning

Python Basics & Data Handling

Libraries for AI & ML

File Handling & Databases

 

2. Data Preprocessing & Feature Engineering

Data Cleaning & Handling

Feature Engineering

 

3. Supervised Learning Algorithms

Regression Models

Tree-Based Models

Other ML Algorithms

Model Evaluation

 

4. Unsupervised Learning Algorithms

Clustering

Dimensionality Reduction

Anomaly Detection

 

5. Deep Learning Fundamentals

Neural Networks & Backpropagation

Optimization Algorithms

 

Phase 2: Advanced Machine Learning & NLP

1. Convolutional Neural Networks (CNNs)

 

2. Natural Language Processing (NLP)

Text Preprocessing

Feature Engineering in NLP

Deep Learning for NLP

Pre-trained NLP Models

NLP Tasks

 

Phase 3: Large Language Models (LLMs) & AI Architectures

1. Transformer-Based Models

LLM Training & Fine-Tuning

 

Phase 4: AI Deployment & Scaling

1. AI Model Deployment

2. Cloud AI Deployment

3. Real-time AI Applications

 

Phase 5: Ethics, Security & AI Governance

1. Ethics in AI

2. AI Security

3. AI Regulations

 

Phase 6: Research, Innovation & Practical Projects

1. Research in AI

2. Practical AI Projects

 

📘 MASTER SYLLABUS: DevOps with Azure

🔹 1. DevOps Core Fundamentals

🔹 2. CI/CD with Azure DevOps

🔹 3. Infrastructure as Code (IaC)

🔹 4. Containerization & Orchestration

🔹 5. Monitoring, Logging & Alerts

🔹 6. Security, Governance & Cost Management

🔹 7. DevSecOps & Quality Gates

 

🔹 8. GitHub Actions (for hybrid Azure+GitHub workflow)

🔹 9. Real Projects (Must Include)

 

 

📙 MASTER SYLLABUS: DevOps with AWS

🔸 1. DevOps Core & AWS Basics

🔸 2. CI/CD with AWS DevOps Tools

🔸 3. IaC on AWS

🔸 4. Containers and Orchestration

🔸 5. Monitoring & Logging

🔸 6. Security & Governance

🔸 7. DevSecOps on AWS

🔸 8. Cost Optimization & Governance

🔸 9. Alternative CI/CD Tools

 

🔸 10. Real Projects (Must Include)

 

🛠 But ONLY IF You Also:

✅ Do real-world projects (not just tutorials)  
✅ Document your setups (readmes, architecture diagrams)  
✅ Debug and troubleshoot issues (build failures, rollout issues)  
✅ Automate end-to-end: Git → Build → Test → Deploy → Monitor  
✅ Push projects to GitHub with clear commits, pipelines, infra code  
✅ Stay updated with new tools (like GitHub Actions, Bicep, Karpenter for K8s, etc.)  
 

 

📚 Core Mathematics for AI, ML, DL & LLMs

🧮 1. Linear Algebra (Foundational for all ML & DL)

Used in neural networks, word embeddings, transformations, PCA, etc.

📈 2. Calculus (For optimization & backpropagation)

Crucial for training models using gradient-based methods.

🎲 3. Probability & Statistics (For uncertainty, models, evaluation)

Used in probabilistic models, Bayesian reasoning, and evaluation metrics.

🔢 4. Discrete Mathematics

Used in logic, graph theory, and decision processes.

 

📊 Applied Math for ML, DL, LLMs & Agents

🔁 5. Optimization Techniques

Crucial for training all models efficiently.

🧠 6. Information Theory (For NLP, LLMs, model efficiency)

Used in text models, entropy-based systems, compression, etc.

 

🧮 7. Numerical Methods

Practical math for implementing models.

🧠 Advanced Topics for LLMs, Deep Learning & AI Agents

🧠 8. Advanced Statistics & Probabilistic Modeling

Used in generative models, Bayesian models, etc.

🧮 9. Functional Analysis & Advanced Linear Algebra

Used in deep theory, infinite-dimensional vector spaces, etc.

🌐 10. Differential Equations

Used in continuous modeling and some reinforcement learning.

🤖 11. Control Theory & Dynamic Programming

Used in reinforcement learning and AI agents.

🔁 12. Topology & Geometry

Emerging in some neural network research and data manifolds.

 

✳️ Specialized Math in Cutting-edge AI/LLMs

 

💡 13. Transformer-specific Math (Used in GPT, BERT, LLMs)

 

🔍 14. Large-Scale Systems / Training

 

 

📚 BOOK RECOMMENDATIONS BY TOPIC

🧮 1. Linear Algebra

🧠 Exercises:

📈 2. Calculus

🧠 Exercises:

🎲 3. Probability & Statistics

🧠 Exercises:

🔁 4. Optimization

🧠 Exercises:

📊 5. Information Theory

🧠 Exercises:

🧮 6. Numerical Methods

🧠 Exercises:

🤖 7. Advanced Topics (Bayesian Inference, PGMs, RL, LLMs)

🧠 Exercises:

📖 8. All-in-One Books (Highly Recommended)

 

📘 Phase 1: LLM Fundamentals (Theory + Concepts)

🔹 Basics of NLP & Deep Learning

🔹 Introduction to Transformers

🔹 Transformer Architecture in Depth

🔹 From Transformer to LLM

 

⚙️ Phase 2: Building and Using LLMs

🔹 HuggingFace Transformers

🔹 Fine-Tuning Basics

🔹 Prompt Engineering

 

🧪 Phase 3: LLM Evaluation, Testing, Safety

🔹 Evaluation Techniques

🔹 Dataset Construction

🔹 LLM Testing

🔹 Safety & Bias

🧱 Phase 4: Building Apps with LLMs (Production)

🔹 FastAPI + LLM Backend

🔹 LangChain + Agents

🔹 Vector Databases

🔹 Advanced Integration

🧠 Phase 5: Advanced Topics & Research

🔹 Model Internals

🔹 Training Your Own LLM (Optional)

🔹 Agent Systems

 

🧰 Phase 6: Toolkits & Ecosystem

ToolUse
HuggingFace 🤗Models, tokenizers, datasets
LangChain 🔗LLM app framework
OpenAI APIAccess GPT-4, Codex
DeepEvalLLM testing framework
OpenLLMLocal LLM inference
Weights & BiasesModel tracking
LlamaIndexDocument-based retrieval

 

 


 

 

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