├── .gitignore ├── .ipynb_checkpoints └── Important Links-checkpoint.ipynb ├── 0_Prerequisites ├── 00_Python Crash Course Workshop │ ├── .ipynb_checkpoints │ │ ├── 1_Python-checkpoint.ipynb │ │ ├── 2_Python -checkpoint.ipynb │ │ └── 3_Day - Notes-checkpoint.ipynb │ ├── 1_Python.ipynb │ ├── 2_Python .ipynb │ ├── 3_Day - Notes.ipynb │ ├── 3_Modules and Packages │ │ ├── food.py │ │ ├── indore.py │ │ ├── main.py │ │ └── statsforpython │ │ │ ├── __init__.py │ │ │ ├── descriptive.py │ │ │ └── inferential.py │ └── Project │ │ ├── .gitignore │ │ ├── application.py │ │ └── templates │ │ └── index.html ├── 01_NumPy │ ├── .ipynb_checkpoints │ │ └── 01_NumPy Understanding-checkpoint.ipynb │ ├── 01_NumPy Understanding.ipynb │ └── NumPy Resources │ │ ├── NumPy Books │ │ └── NumPy Cookbook, Second Edition.pdf │ │ ├── NumPy Cheat Sheet │ │ ├── Numpy_Python_Cheat_Sheet.pdf │ │ ├── numpy-cheat-sheet.pdf │ │ └── numpy.pdf │ │ ├── NumPy Practice Questions │ │ └── 1-100 NumPy Exercises for Data Analysis.pdf │ │ └── NumPy Reference or Documentation │ │ ├── numpy-reference 1.18.pdf │ │ └── numpy-user 1.18.pdf ├── 02_Pandas │ ├── .ipynb_checkpoints │ │ ├── 02_Pandas-checkpoint.ipynb │ │ └── 03_Pandas and EDA Project-checkpoint.ipynb │ ├── 02_Pandas.ipynb │ ├── 03_Pandas and EDA Project.ipynb │ ├── Pandas Books │ │ ├── Learning pandas.pdf │ │ └── Mastering Pandas.pdf │ ├── goa.xlsx │ ├── indore.csv │ ├── nyc_weather.csv │ ├── stock_data.csv │ ├── stocks_weather.xlsx │ ├── titanic.csv │ ├── weather_by_cities.csv │ ├── weather_data.csv │ ├── weather_data.xlsx │ ├── weather_data2.csv │ └── weather_datamissing.csv └── 03_Matplotlib │ ├── .ipynb_checkpoints │ └── 04_Matplotlib-checkpoint.ipynb │ ├── 04_Matplotlib.ipynb │ └── Matplotlib Resources │ └── hands-matplotlib-plotting-visualizations.pdf ├── 1_Module - Mathematics in Machine Learning ├── 01_Chapter - Linear Algebra │ ├── 05_Vectors and Matrix.ipynb │ ├── 06_Eigenvalues Eigenvectors Factorization SVD PCA.ipynb │ ├── 07_Tensor.ipynb │ ├── t1.png │ ├── t2.png │ ├── t3.png │ ├── t4.png │ ├── v1.png │ ├── v2.png │ ├── v3.png │ └── v4.gif ├── 02_Chapter - Statistics │ ├── 08_Descriptive Statistics.ipynb │ ├── 09_Skewness Sampling and Central Limit Theorem.ipynb │ ├── 10_Hypothesis Testing.ipynb │ ├── 11_Regression Analysis │ │ ├── 1_Linear Regression.ipynb │ │ ├── 2_Linear Regression Complete │ │ │ ├── 2_LR complete.ipynb │ │ │ ├── MBASSData.csv │ │ │ ├── e.png │ │ │ ├── five.png │ │ │ ├── four.png │ │ │ ├── one.png │ │ │ ├── q.png │ │ │ ├── r.png │ │ │ ├── seven.png │ │ │ ├── six.png │ │ │ ├── t.png │ │ │ ├── three.png │ │ │ ├── two.png │ │ │ ├── w.png │ │ │ └── winequality-red.csv │ │ ├── 3_Multiple Linear Regression │ │ │ ├── 3_Multiple LR.ipynb │ │ │ ├── IPL IMB381IPL2013.csv │ │ │ ├── e.png │ │ │ ├── ee.png │ │ │ ├── q.png │ │ │ ├── rr.png │ │ │ └── w.png │ │ ├── areas.csv │ │ └── homeprices.csv │ ├── 12_Logistic Regression │ │ ├── 12_Logistic Regression.ipynb │ │ ├── Titanic Evaluation.ipynb │ │ ├── insurance_data.csv │ │ └── titanic.csv │ ├── 13_ANOVA Chi Square.ipynb │ └── datasets │ │ ├── .DS_Store │ │ ├── AmesHousing.csv │ │ ├── Birthweight_reduced_kg_R.csv │ │ ├── CarPrice_Assignment.csv │ │ ├── Crime_R.csv │ │ ├── Health_insurance.csv │ │ ├── SP_500_1987.csv │ │ ├── data_loan.csv │ │ └── forbes.csv ├── 03_Chapter - Probability │ ├── 14_Probability and Naive Bayes.ipynb │ ├── Mushroom Subset.xlsx │ ├── SMSSpamCollection+(1) │ └── example_train1.csv └── 05_Chapter - Regularization │ ├── 22_Regularization.ipynb │ └── diamonds.csv ├── 2_Module - ML Concepts Algorithms ├── 010_Activation Functions │ ├── 21_Activation Functions.ipynb │ ├── a1.png │ ├── a2.png │ ├── a3.png │ ├── a5.png │ ├── a6.png │ ├── a7.jpg │ └── s4.png ├── 011_Clustering │ ├── 23_Clustering.ipynb │ ├── Live.csv │ ├── k1.png │ ├── k2.png │ └── k3.jpg ├── 012_Anomaly Detection │ ├── 24_Anomaly Detection Analysis.ipynb │ └── Superstore.xls ├── 04_Decision Tree │ ├── 15_Decision Tree.ipynb │ ├── datasets │ │ ├── adult_dataset.csv │ │ ├── diabetes_dataset.csv │ │ └── salaries.csv │ └── dt.png ├── 05_Random Forest │ ├── 16_Random Forest Ensemble Bagging Boosting.ipynb │ └── credit-card-default.csv ├── 06_Neural Network │ ├── 17_Neural Networks.ipynb │ ├── n1.png │ ├── n10.png │ ├── n11.jpeg │ ├── n2.png │ ├── n3.png │ ├── n4.png │ ├── n5.png │ ├── n6.png │ ├── n7.png │ ├── n8.png │ └── n9.png ├── 07_Support Vector Machine │ ├── 18_Support Vector Machine.ipynb │ ├── pulsar_data_train.csv │ ├── s1.png │ ├── s2.png │ ├── s3.png │ └── s4.png ├── 08_Adaboost │ ├── 19_AdaBoost.ipynb │ ├── a1.png │ ├── a2.png │ ├── a3.png │ └── heart.csv └── 09_XGBoost │ ├── 20_XGBoost.ipynb │ ├── adult.csv │ ├── desktop.ini │ ├── g1.png │ ├── g2.png │ └── g3.png ├── 4_Module - NLP GenAI CV - Deep Learning ├── 1_Natural Language Processing │ ├── 1_NLP Fundamentals and PyTorch.ipynb │ ├── 2_Amazon Lex Building Chatbot.ipynb │ ├── Tensors.png │ ├── n1.png │ ├── n2.png │ ├── n3.png │ ├── test.csv │ └── train.csv ├── 2_Generative AI │ ├── 01_Fundamentals of GenerativeAI.ipynb │ ├── 02_ChatGPT_API_Project │ │ └── ChatGPT API Clone │ │ │ ├── .gitignore │ │ │ ├── app.py │ │ │ ├── config.py │ │ │ ├── requirements.txt │ │ │ └── templates │ │ │ └── index.html │ ├── 02_Project Notes.ipynb │ ├── 03_Transformer.ipynb │ ├── 04_Generative Adversarial Networks.ipynb │ ├── code.html │ ├── data │ │ └── MNIST │ │ │ └── raw │ │ │ ├── t10k-images-idx3-ubyte │ │ │ ├── t10k-images-idx3-ubyte.gz │ │ │ ├── t10k-labels-idx1-ubyte │ │ │ ├── t10k-labels-idx1-ubyte.gz │ │ │ ├── train-images-idx3-ubyte │ │ │ ├── train-images-idx3-ubyte.gz │ │ │ ├── train-labels-idx1-ubyte │ │ │ └── train-labels-idx1-ubyte.gz │ ├── g1.jpg │ ├── g2.png │ ├── g3.png │ ├── t1.gif │ ├── t10.png │ ├── t11.png │ ├── t12.png │ ├── t13.png │ ├── t14.png │ ├── t15.png │ ├── t16.png │ ├── t17.png │ ├── t2.gif │ ├── t3.png │ ├── t4.gif │ ├── t5.gif │ ├── t6.gif │ ├── t7.gif │ ├── t8.png │ └── t9.png └── 3_Computer Vision │ ├── 1_Convolutional Neural Networks.ipynb │ ├── 2_CNN with Cifar10.ipynb │ ├── 3_Recurrent Neural Network.ipynb │ ├── 4_LSTM and GRU.ipynb │ ├── c1.png │ ├── c10.png │ ├── c11.png │ ├── c12.png │ ├── c13.png │ ├── c14.png │ ├── c15.png │ ├── c16.png │ ├── c2.png │ ├── c4.png │ ├── c5.png │ ├── c6.png │ ├── c7.png │ ├── c8.png │ ├── c9.png │ ├── l1.png │ ├── l2.png │ ├── l3.gif │ ├── l4.gif │ ├── l5.gif │ ├── l6.gif │ ├── l7.png │ ├── r1.png │ ├── r2.png │ └── r3.png ├── 5_Module - MLOps ├── 1_MLOps Azure MLFlow │ ├── 1_2_3_MLOps Fundamentals.ipynb │ ├── Data_Processing.ipynb │ ├── dataset │ │ └── weather_dataset_raw.csv │ └── mlflow.jpg ├── 2_3_MLOps Lab Code │ └── ml-pipeline.ipynb ├── 4_5_Deploying ML Models to Flask │ ├── .gitignore │ ├── 4_Serializating ML Models.ipynb │ ├── 5_Deploying ML Models for Prediction.ipynb │ ├── CarPrice_Assignment.csv │ ├── models │ │ ├── joblib_model.joblib │ │ ├── pickle_model.pkl │ │ ├── regressor_parameters.txt │ │ ├── retrained_rfr_model_checkpoint.joblib │ │ ├── rfr_model_checkpoint.joblib │ │ └── text_classification_model.joblib │ ├── sentiment_analysis.csv │ └── server.py ├── 6_GCP Serverless ML model deployment │ ├── 6_ML models on Google Cloud Serverless.ipynb │ ├── models │ │ ├── decision_classifier │ │ │ └── decision_model.pkl │ │ ├── logistic_classifier │ │ │ └── logistic_model.pkl │ │ └── support_classifier │ │ │ └── support_model.pkl │ ├── request.py │ ├── sentiment_analysis.csv │ └── test_data_with_model.json └── 7_Deep Learning Model Deployment using AWS SageMaker │ ├── SageMaker Deployment.ipynb │ ├── mnist.py │ └── s1.png ├── 6_Module - ML System Design ├── 01_02_Fundamentals of ML Systems.ipynb ├── 03_Visual Search System.ipynb ├── 04_Video Search System YouTube.ipynb ├── 05_06_Video Recommendation System.ipynb ├── v41.png ├── v42.png ├── v43.jpg ├── v44.png ├── v45.png ├── v46.png ├── vr1.png ├── vr2.png ├── vr3.png ├── vr4.png ├── vr5.jpg └── vr6.png ├── 7_Module - ML Interview Profile Building Portfolio └── 2_Resume.ipynb ├── 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