├── .DS_Store ├── .gitignore ├── 5 SQL Case Studies ├── Data_Science_Jobs.ipynb ├── Session 1 - SQL Case Study - Data Science Jobs.md ├── mydatabase.db └── salaries.csv ├── Building AI Agents ├── 1_CampusX_GPT4o_Google_Models.ipynb ├── 2_autoRagPhiData │ ├── app.py │ ├── docker.txt │ ├── requirements.txt │ └── sample.pdf ├── 2_multimodal_Chatbot │ ├── main.py │ └── requirements.txt ├── 2_phidata_Assisstant │ ├── data_analyst.py │ ├── finance.py │ ├── instruction.py │ ├── ragassistant.py │ └── requirements.txt ├── 3_LlamaIndexAgenticRag │ ├── NIPS-2017-attention-is-all-you-need-Paper.pdf │ ├── main.py │ └── requirements.txt ├── 3_llamaindexChainlitApp │ ├── app_chainlit.py │ └── requirements.txt ├── 3_taskingai.ipynb ├── 4_CrewAi.ipynb ├── CampusX_Autogen.ipynb ├── README.md ├── Session 1 - Introduction to AI Agents.md ├── Session 2 - Working with Agent Frameworks.md ├── Session 3 - Agentic RAG with Llama Index.md └── Session 4 - Crew AI and Transformer Agents.md ├── Credit Risk Modeling using Machine Learning ├── .DS_Store ├── Credit-risk-session-01-Notes-collection.ipynb ├── Credit_risk_notebook.ipynb ├── Features_Target_Description.md ├── Flask Deployment │ ├── EPS_Dataset.xlsx │ ├── deploy.py │ ├── eps_v1.sav │ ├── main.py │ ├── requirements.txt │ └── templates │ │ └── index.html ├── Numericals.xlsx ├── README.md ├── Session 1 - Business Overview of the Project.md ├── Session 2 - EDA and Feature Engneering.md ├── Session 3 - Model Selection.md ├── Session 4 - Model Serving and Deployment.md ├── dataset │ ├── case_study1.xlsx │ └── case_study2.xlsx ├── image.png └── src │ └── __init__.py ├── Data Analysis using MS Excel ├── Session 1 - Introduction to MS Excel.md ├── README.md └── Session 2 - Case study - Boston Crime Analytics using Excel.md ├── Data Science Interview Preparation ├── README.md ├── Session 1 - Foundation and Technical Skills.md └── Session 2 - Programming and Technical Skills.md ├── Deep-Learning-Pytorch ├── .DS_Store ├── Session-01-Introduction-to-PyTorch.ipynb ├── data │ └── raw │ │ └── .gitkeep └── images │ └── computational-graph.png ├── E-KYC using Computer Vision ├── .DS_Store ├── README.md ├── app.py ├── config.yaml ├── db_opeations.py ├── face_verification.py ├── models │ └── haarcascade_frontalface_alt.xml ├── mysqldb_operations.py ├── ocr_and_face_operations.ipynb ├── ocr_engine.py ├── openv_all.ipynb ├── openv_all.pdf ├── postprocess.py ├── preprocess.py └── utils.py ├── Emotion Detection using Deep Learning ├── CNN_Notebook.ipynb ├── Cats_vs_dogs_image_classification_from_scratch.ipynb ├── Emotion_Detection_Complete_Project.ipynb ├── Emotion_Detection_Complete_Project_(1).ipynb ├── README.md ├── Session 1 - Introduction to CNN.md └── Session 2 - Building CNN from scratch.md ├── End-to-End ML Project using MLOps ├── Images │ ├── crispdm.jpg │ ├── happy.png │ ├── jupter_vscode.jpg │ ├── mlops.png │ ├── mlops_lifecycle.jpg │ ├── model pipeline.png │ └── pillars_of_mlops.png ├── README.md ├── Session 1 - Introduction to MLOPs and Version Control.md ├── Session 1 - Introduction to MLOps.ipynb ├── Session 2 - Data and Model Versioning through DVC.md ├── Session 3 - Data to Model Pipeline through DVC.md ├── Session-5_Session 5 - Experimentation Tracking and FAST API.md ├── Version_Control.ipynb └── demo.ipynb ├── Fine Tuning Transformers ├── Fundamentals of Transformers | Part 1.md ├── Fundamentals of Transformers | Part 2.md ├── HuggingFace.ipynb ├── Images │ └── Encoder_Decoder.png └── README.md ├── Flight Price Prediction using AWS Sagemaker ├── README.md ├── Session_01_Introduction_to_sagemaker.md └── Session_02_Github_set_up_and_data_cleaning.md ├── Gen AI Projects using Langchain ├── .DS_Store ├── GEN_AI_CampusX.ipynb ├── GenAI_Images │ ├── CoT.png │ ├── DecoderArchitecture.png │ ├── GOOGLE_Translate.png │ ├── GPTEmbedding.png │ ├── GPTTrainingPrices.png │ ├── GPT_Parameters.png │ ├── Generative-Adversarial-Networks-Architecture-scaled.jpg │ ├── HOWWEREACHChatGPT.png │ ├── LanguageModelNextWord.png │ ├── NxtWordLanguageModel.png │ ├── SELFSUPERVISEDLEARNING.png │ ├── VAE.png │ ├── active_prompting.png │ ├── cot_self_consistency.png │ ├── creatingownlanguagemodel.png │ ├── differentactivation.png │ ├── finetuning.png │ ├── google_sent_complete.png │ ├── intro_LLM.png │ ├── keep_conversation_prompt.png │ ├── llama2 vs gpt.png │ ├── multi-head-self-attention-block-diagram.png │ ├── multiheadattention.png │ ├── neural network weights.jpg │ ├── neuralnetworkLM.png │ ├── nonlinearity.png │ ├── noofparameters.png │ ├── openai_announce.png │ ├── react_prompt.png │ ├── relu elu.png │ ├── review_prmpt.png │ ├── rlhf.png │ ├── rotatoryembedding.png │ ├── structure_prompt.png │ ├── swigluactivation.png │ ├── time_100M.png │ ├── tokenization.png │ ├── tot_prompt.png │ ├── transformers.jpg │ ├── tricks_prompt.png │ ├── voice_assistant.jpg │ ├── what_GenAI.png │ ├── what_prompt_engineering.png │ └── wordsprocessedbyllm.png ├── HuggingFace_Models.ipynb ├── README.md ├── Session 1 - Introduction to Gen AI.md ├── Session 3 - Hugging Face Models.md ├── Session 5 - Training Fine Tuning Peft, QLoRA, LLAMa 3.md ├── Session 6 - LangChain Hands On.md ├── content │ ├── CoT.png │ ├── DecoderArchitecture.png │ ├── GOOGLE_Translate.png │ ├── GPTEmbedding.png │ ├── GPTTrainingPrices.png │ ├── GPT_Parameters.png │ ├── Generative-Adversarial-Networks-Architecture-scaled.jpg │ ├── HOWWEREACHChatGPT.png │ ├── LanguageModelNextWord.png │ ├── NxtWordLanguageModel.png │ ├── SELFSUPERVISEDLEARNING.png │ ├── VAE.png │ ├── active_prompting.png │ ├── cot_self_consistency.png │ ├── creatingownlanguagemodel.png │ ├── differentactivation.png │ ├── finetuning.png │ ├── google_sent_complete.png │ ├── intro_LLM.png │ ├── keep_conversation_prompt.png │ ├── llama2 vs gpt.png │ ├── llama2training.webp │ ├── llmbenchmark.png │ ├── llmbenchmarkspicture.webp │ ├── multi-head-self-attention-block-diagram.png │ ├── multiheadattention.png │ ├── neural network weights.jpg │ ├── neuralnetworkLM.png │ ├── nonlinearity.png │ ├── noofparameters.png │ ├── openai_announce.png │ ├── react_prompt.png │ ├── relu elu.png │ ├── review_prmpt.png │ ├── rlhf.png │ ├── rotatoryembedding.png │ ├── structure_prompt.png │ ├── swigluactivation.png │ ├── time_100M.png │ ├── tokenization.png │ ├── tot_prompt.png │ ├── transformers.jpg │ ├── tricks_prompt.png │ ├── voice_assistant.jpg │ ├── what_GenAI.png │ ├── what_prompt_engineering.png │ └── wordsprocessedbyllm.png ├── langchain_hands_on.ipynb └── session5_genai.ipynb ├── Generative AI Projects using Llamaindex ├── README.md ├── Session 1 - Introduction to GEN AI and LLMs.md └── Session 2 - Introduction to Llama Index.md ├── Generative AI for Vision ├── README.md ├── Session 1 - introduction to GEN AI for Vision.md ├── Session 2 - Representation of Images.md ├── Session 3 - Basics of Diffusion Models.md ├── Session 5 - Project building and deployment.md ├── image_generation.ipynb ├── image_to_pixel.ipynb ├── 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