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lines changed- 1 - How to utilise Aplied Al Course/1.1 - How to Learn from Appliedaicourse
- 10 - Linear Algebra
- 10.11 - Revision Questions
- 10.3 - Dot Product and Angle between 2 Vectors
- 10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane
- 11 - Probability and Statistics
- 11.11 - Chebyshev’s inequality
- 11.13 - How to randomly sample data points (Uniform Distribution)
- 11.17 - Box cox transform
- 11.24 - Confidence interval (C.I) Introduction
- 11.27 - Confidence interval using bootstrapping
- 11.28 - Hypothesis testing methodology, Null-hypothesis, p-value
- 11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)
- 11.30 - Resampling and permutation test
- 11.31 - K-S Test for similarity of two distributions
- 11.32 - Code Snippet K-S Test
- 11.34 - Resampling and Permutation test another example
- 11.37 - Revision Questions
- 11.5 - Symmetric distribution, Skewness and Kurtosis
- 11.9 - Q-Q plotHow to test if a random variable is normally distributed or not
- 12 - Q and A/12.1 - Questions & Answers
- 13 - Dimensionally Reduction and Visualization
- 13.1 - What is Dimensionality reduction
- 13.10 - Code to Load MNIST Data Set
- 13.3 - How to represent a data set
- 13.8 - Co-variance of a Data Matrix
- 13.9 - MNIST dataset (784 dimensional)
- 14 - PCA (Principal Component Analysis)
- 14.10 - PCA for dimensionality reduction (not-visualization)
- 14.2 - Geometric intuition of PCA
- 14.3 - Mathematical objective function of PCA
- 14.4 - Alternative formulation of PCA Distance minimization
- 14.9 - PCA Code example
- 15 - T-SNE (T-Distributed Stochastic Neighbourhood Embedding)
- 15.5 - How to apply t-SNE and interpret its output
- 15.7 - Code example of t-SNE
- 15.8 - Revision Questions
- 16 - Q and A/16.1 - Questions & Answers
- 17 - Case Study 1 - Predict Rating given Product review on Amazon
- 17.1 - Dataset overview Amazon Fine Food reviews(EDA)
- 17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec
- 17.11 - Bag of Words( Code Sample)
- 17.12 - Text Preprocessing( Code Sample)
- 17.13 - Bi-Grams and n-grams (Code Sample)
- 17.14 - TF-IDF (Code Sample)
- 17.15 - Word2Vec (Code Sample)
- 17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)
- 17.17 - Assignment-2 Apply t-SNE
- 17.2 - Data Cleaning Deduplication
- 17.3 - Why convert text to a vector
- 17.4 - Bag of Words (BoW)
- 17.5 - Text Preprocessing Stemming
- 17.6 - uni-gram, bi-gram, n-grams
- 17.7 - tf-idf (term frequency- inverse document frequency)
- 17.8 - Why use log in IDF
- 17.9 - Word2Vec
- 18 - Classification and Regression Model - KNN
- 18.22 - How to build a kd-tree
- 18.31 - Code SampleCross Validation
- 18.32 - Revision Questions
- 19 - Q and A/19.1 - Questions & Answers
- 2 - Python For Data Science Introduction
- 2.1 - Python, Anaconda and relevant packages installations
- 2.10 - Control flow for loop
- 2.11 - Control flow break and continue
- 2.3 - Keywords and identifiers
- 2.4 - comments, indentation and statements
- 2.5 - Variables and data types in Python
- 2.6 - Standard Input and Output
- 2.7 - Operators
- 2.8 - Control flow if else
- 2.9 - Control flow while loop
- 20 - Classification Algo in Various Situations
- 20.19 - Intuitive understanding of bias-variance
- 20.20 - Revision Questions
- 21 - Performance Measurement of Model
- 21.10 - Revision Questions
- 21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC
- 21.9 - Assignment-3 Apply k-Nearest Neighbor
- 22 - Q and A/22.1 - Questions & Answers
- 23 - Naive Bayes
- 23.10 - Bias and Variance tradeoff
- 23.21 - Assignment-4 Apply Naive Bayes
- 23.22 - Revision Questions
- 23.4 - Exercise problems on Bayes Theorem
- 24 - Logistic Regression
- 24.14 - Real world cases
- 24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV
- 24.17 - Assignment-5 Apply Logistic Regression
- 24.18 - Extensions to Generalized linear models
- 24.5 - L2 Regularization Overfitting and Underfitting
- 24.7 - Probabilistic Interpretation Gaussian Naive Bayes
- 25 - Linear Regression
- 25.2 - Mathematical formulation
- 25.3 - Real world Cases
- 25.4 - Code sample for Linear Regression
- 26 - Solving Optimization Problems
- 26.13 - Revision questions
- 26.9 - Constrained Optimization & PCA
- 27 - Q and A/27.1 - Questions & Answers
- 28 - Support Vector Machines (SVM)
- 28.12 - SVM Regression
- 28.15 - Assignment-7 Apply SVM
- 28.16 - Revision Questions
- 28.4 - Loss function (Hinge Loss) based interpretation
- 28.5 - Dual form of SVM formulation
- 28.7 - Polynomial Kernel
- 29 - Q and A/29.1 - Questions & Answers
- 3 - Python For Data Science Data Stucture
- 3.1 - Lists
- 3.2 - Tuples part 1
- 3.3 - Tuples part-2
- 3.4 - Sets
- 3.5 - Dictionary
- 3.6 - Strings
- 30 - Decision Trees
- 30.15 - Assignment-8 Apply Decision Trees
- 30.16 - Revision Questions
- 30.2 - Sample Decision tree
- 30.4 - Building a decision TreeInformation Gain
- 31 - Q and A/31.1 - Questions & Answers
- 32 - Ensemble Models
- 32.10 - Residuals, Loss functions and gradients
- 32.19 - Assignment-9 Apply Random Forests & GBDT
- 32.20 - Revision Questions
- 32.9 - Boosting Intuition
- 34 - Miscellaneous Topics
- 34.10 - AB testing
- 34.12 - VC dimension
- 34.2 - Productionization and deployment of Machine Learning Models
- 34.3 - Calibration Plots
- 34.4 - Platt’s CalibrationScaling
- 34.5 - Isotonic Regression
- 34.6 - Code Samples
- 34.7 - Modeling in the presence of outliers RANSAC
- 34.8 - Productionizing models
- 34.9 - Retraining models periodically
- 35 - Unsupervised Learning - Clustering
- 35.11 - Determining the right K
- 35.14 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms
- 35.4 - Metrics for Clustering
- 35.8 - How to initialize K-Means++
- 36 - Hierarchical Clustering Technique
- 36.1 - Agglomerative & Divisive, Dendrograms
- 36.2 - Agglomerative Clustering
- 36.3 - Proximity methods Advantages and Limitations
- 36.4 - Time and Space Complexity
- 36.6 - Code sample
- 36.7 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms
- 37 - DBSCAN (Density Based Clustering) Technique
- 37.10 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms
- 37.11 - Revision Questions
- 37.3 - Core, Border and Noise points
- 37.7 - Advantages and Limitations of DBSCAN
- 37.8 - Time and Space Complexity
- 37.9 - Code samples
- 38 - Recommender System and Matrix Factorization
- 38.1 - Problem formulation Movie reviews
- 38.10 - Matrix Factorization for recommender systems Netflix Prize Solution
- 38.13 - Eigen-Faces
- 38.14 - Code example
- 38.15 - Assignment-11 Apply Truncated SVD
- 38.16 - Revision Questions
- 38.6 - Matrix Factorization for Collaborative filtering
- 38.8 - Clustering as MF
- 39 - Q and A/39.1 - Questions & Answers
- 4 - Python For Data Science Fuctions
- 4.1 - Introduction
- 4.10 - Debugging Python
- 4.2 - Types of functions
- 4.3 - Function arguments
- 4.4 - Recursive functions
- 4.5 - Lambda functions
- 4.6 - Modules
- 4.7 - Packages
- 4.8 - File Handling
- 4.9 - Exception Handling
- 40 - Case Study 2 - Stack Overflow Tag Predictor
- 40.1 - BusinessReal world problem
- 40.10 - Data Modeling Multi label Classification
- 40.11 - Data preparation
- 40.12 - Train-Test Split
- 40.13 - Featurization
- 40.14 - Logistic regression One VS Rest
- 40.15 - Sampling data and tags+Weighted models
- 40.16 - Logistic regression revisited
- 40.17 - Why not use advanced techniques
- 40.18 - Assignments
- 40.2 - Business objectives and constraints
- 40.3 - Mapping to an ML problem Data overview
- 40.4 - Mapping to an ML problemML problem formulation
- 40.5 - Mapping to an ML problemPerformance metrics
- 40.6 - Hamming loss
- 40.7 - EDAData Loading
- 40.8 - EDAAnalysis of tags
- 40.9 - EDAData Preprocessing
- 41 - Case Study 3 - Quora Question Pair Similarity Problem
- 41.1 - BusinessReal world problem Problem definition
- 41.10 - EDA Feature analysis
- 41.11 - EDA Data Visualization T-SNE
- 41.12 - EDA TF-IDF weighted Word2Vec featurization
- 41.13 - ML Models Loading Data
- 41.14 - ML Models Random Model
- 41.15 - ML Models Logistic Regression and Linear SVM
- 41.16 - ML Models XGBoost
- 41.17 - Assignments
- 41.2 - Business objectives and constraints
- 41.3 - Mapping to an ML problem Data overview
- 41.4 - Mapping to an ML problem ML problem and performance metric
- 41.5 - Mapping to an ML problem Train-test split
- 41.6 - EDA Basic Statistics
- 41.7 - EDA Basic Feature Extraction
- 41.8 - EDA Text Preprocessing
- 41.9 - EDA Advanced Feature Extraction
- 42 - Case Study 4 - Amazon Fashion Discovery Engine
- 42.1 - Problem Statement Recommend similar apparel products
- 42.10 - Text Pre-Processing Tokenization and Stop-word removal
- 42.11 - Stemming
- 42.12 - Text based product similarity Converting text to an n-D vector bag of words
- 42.13 - Code for bag of words based product similarity
- 42.14 - TF-IDF featurizing text based on word-importance
- 42.15 - Code for TF-IDF based product similarity
- 42.16 - Code for IDF based product similarity
- 42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)
- 42.18 - Code for Average Word2Vec product similarity
- 42.19 - TF-IDF weighted Word2Vec
- 42.2 - Plan of action
- 42.20 - Code for IDF weighted Word2Vec product similarity
- 42.21 - Weighted similarity using brand and color
- 42.22 - Code for weighted similarity
- 42.23 - Building a real world solution
- 42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts
- 42.25 - Using Keras + Tensorflow to extract features
- 42.26 - Visual similarity based product similarity
- 42.27 - Measuring goodness of our solution AB testing
- 42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color
- 42.3 - Amazon product advertising API
- 42.4 - Data folders and paths
- 42.5 - Overview of the data and Terminology
- 42.6 - Data cleaning and understandingMissing data in various features
- 42.7 - Understand duplicate rows
- 42.8 - Remove duplicates Part 1
- 42.9 - Remove duplicates Part 2
- 43 - Case Study 5 - Microsoft Malwere Detection
- 43.1 - Businessreal world problem Problem definition
- 43.10 - ML models – using byte files only Random Model
- 43.11 - k-NN
- 43.12 - Logistic regression
- 43.13 - Random Forest and Xgboost
- 43.14 - ASM Files Feature extraction & Multiprocessing
- 43.15 - File-size feature
- 43.16 - Univariate analysis
- 43.17 - t-SNE analysis
- 43.18 - ML models on ASM file features
- 43.19 - Models on all features t-SNE
- 43.2 - Businessreal world problem Objectives and constraints
- 43.20 - Models on all features RandomForest and Xgboost
- 43.21 - Assignments
- 43.3 - Machine Learning problem mapping Data overview
- 43.4 - Machine Learning problem mapping ML problem
- 43.5 - Machine Learning problem mapping Train and test splitting
- 43.6 - Exploratory Data Analysis Class distribution
- 43.7 - Exploratory Data Analysis Feature extraction from byte files
- 43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files
- 43.9 - Exploratory Data Analysis Train-Test class distribution
- 44 - Case Study 6 - Netflix Movie Recommendation System
- 44.1 - BusinessReal world problemProblem definition
- 44.10 - Exploratory Data AnalysisCold start problem
- 44.11 - Computing Similarity matricesUser-User similarity matrix
- 44.12 - Computing Similarity matricesMovie-Movie similarity
- 44.13 - Computing Similarity matricesDoes movie-movie similarity work
- 44.14 - ML ModelsSurprise library
- 44.15 - Overview of the modelling strategy
- 44.16 - Data Sampling
- 44.17 - Google drive with intermediate files
- 44.18 - Featurizations for regression
- 44.19 - Data transformation for Surprise
- 44.2 - Objectives and constraints
- 44.20 - Xgboost with 13 features
- 44.21 - Surprise Baseline model
- 44.22 - Xgboost + 13 features +Surprise baseline model
- 44.23 - Surprise KNN predictors
- 44.24 - Matrix Factorization models using Surprise
- 44.25 - SVD ++ with implicit feedback
- 44.26 - Final models with all features and predictors
- 44.27 - Comparison between various models
- 44.28 - Assignments
- 44.3 - Mapping to an ML problemData overview
- 44.4 - Mapping to an ML problemML problem formulation
- 44.5 - Exploratory Data AnalysisData preprocessing
- 44.6 - Exploratory Data AnalysisTemporal Train-Test split
- 44.7 - Exploratory Data AnalysisPreliminary data analysis
- 44.8 - Exploratory Data AnalysisSparse matrix representation
- 44.9 - Exploratory Data AnalysisAverage ratings for various slices
- 45 - Case Study 7 - Personalized Cancer Diagnosis
- 45.1 - BusinessReal world problem Overview
- 45.10 - Univariate AnalysisVariation Feature
- 45.11 - Univariate AnalysisText feature
- 45.12 - Machine Learning ModelsData preparation
- 45.13 - Baseline Model Naive Bayes
- 45.14 - K-Nearest Neighbors Classification
- 45.15 - Logistic Regression with class balancing
- 45.16 - Logistic Regression without class balancing
- 45.17 - Linear-SVM
- 45.18 - Random-Forest with one-hot encoded features
- 45.19 - Random-Forest with response-coded features
- 45.2 - Business objectives and constraints
- 45.20 - Stacking Classifier
- 45.21 - Majority Voting classifier
- 45.22 - Assignments
- 45.3 - ML problem formulation Data
- 45.4 - ML problem formulation Mapping real world to ML problem
- 45.5 - ML problem formulation Train, CV and Test data construction
- 45.6 - Exploratory Data AnalysisReading data & preprocessing
- 45.7 - Exploratory Data AnalysisDistribution of Class-labels
- 45.8 - Exploratory Data Analysis “Random” Model
- 45.9 - Univariate AnalysisGene feature
- 46 - Case Study 8 - Taxi Demand Prediction In City
- 46.1 - BusinessReal world problem Overview
- 46.10 - Data Cleaning Speed
- 46.11 - Data Cleaning Distance
- 46.12 - Data Cleaning Fare
- 46.13 - Data Cleaning Remove all outlierserroneous points
- 46.14 - Data PreparationClusteringSegmentation
- 46.15 - Data PreparationTime binning
- 46.16 - Data PreparationSmoothing time-series data
- 46.18 - Data Preparation Time series and Fourier transforms
- 46.19 - Ratios and previous-time-bin values
- 46.2 - Objectives and Constraints
- 46.20 - Simple moving average
- 46.21 - Weighted Moving average
- 46.22 - Exponential weighted moving average
- 46.23 - Results
- 46.24 - Regression models Train-Test split & Features
- 46.25 - Linear regression
- 46.26 - Random Forest regression
- 46.27 - Xgboost Regression
- 46.28 - Model comparison
- 46.29 - Assignment
- 46.3 - Mapping to ML problem Data
- 46.4 - Mapping to ML problem dask dataframes
- 46.5 - Mapping to ML problem FieldsFeatures
- 46.6 - Mapping to ML problem Time series forecastingRegression
- 46.7 - Mapping to ML problem Performance metrics
- 46.8 - Data Cleaning Latitude and Longitude data
- 46.9 - Data Cleaning Trip Duration
- 47 - Deep Learning - Neural Networks
- 47.11 - Activation functions
- 47.12 - Vanishing Gradient problem
- 47.14 - Decision surfaces Playground
- 47.7 - Training a single-neuron model
- 48 - Deep Learning - Deep Multi-Level Perceptrons
- 48.10 - Nesterov Accelerated Gradient (NAG)
- 48.11 - OptimizersAdaGrad
- 48.12 - Optimizers Adadelta andRMSProp
- 48.13 - Adam
- 48.14 - Which algorithm to choose when
- 48.18 - Auto Encoders
- 48.2 - Dropout layers & Regularization
- 48.21 - Word2Vec Algorithmic Optimizations
- 48.3 - Rectified Linear Units (ReLU)
- 48.5 - Batch Normalization
- 48.7 - OptimizersHill descent in 3D and contours
- 48.9 - Batch SGD with momentum
- 49 - Deep Learning - Tensorflow and Keras
- 49.1 - Tensorflow and Keras overview
- 49.12 - MNIST classification in Keras
- 49.13 - Hyperparameter tuning in Keras
- 49.14 - Exercise Try different MLP architectures on MNIST dataset
- 49.3 - Google Colaboratory
- 49.4 - Install TensorFlow
- 49.5 - Online documentation and tutorials
- 49.6 - Softmax Classifier on MNIST dataset
- 49.7 - MLP Initialization
- 5 - Python For Data Science Numpy
- 5.1 - Numpy Introduction
- 5.2 - Numerical operations on Numpy
- 50 - Deep Learning - Convolutional Neural Nets
- 50.1 - Biological inspiration Visual Cortex
- 50.10 - Data Augmentation
- 50.11 - Convolution Layers in Keras
- 50.12 - AlexNet
- 50.14 - Residual Network
- 50.15 - Inception Network
- 50.16 - What is Transfer learning
- 50.17 - Code example Cats vs Dogs
- 50.18 - Code Example MNIST dataset
- 50.2 - ConvolutionEdge Detection on images
- 50.4 - Convolution over RGB images
- 50.5 - Convolutional layer
- 50.7 - CNN Training Optimization
- 50.8 - Example CNN LeNet [1998]
- 50.9 - ImageNet dataset
- 51 - Deep Learning - Long Short-Term Memory(LSTMS)
- 51.10 - Code example IMDB Sentiment classification
- 51.4 - Types of RNNs
- 51.5 - Need for LSTMGRU
- 51.6 - LSTM
- 51.7 - GRUs
- 52 - Q and A/52.1 - Questions and Answers
- 53 - Case Study 9 - Self Driving Car
- 53.10 - NVIDIA’s end to end CNN model
- 53.11 - Train the model
- 53.12 - Test and visualize the output
- 53.13 - Extensions
- 53.2 - Datasets
- 53.3 - Data understanding & Analysis Files and folders
- 54 - Case Study 10 - Music Generation Using Deep Learning
- 54.1 - Real-world problem
- 54.10 - MIDI music generation
- 54.11 - Survey blog
- 54.2 - Music representation
- 54.3 - Char-RNN with abc-notation Char-RNN model
- 54.4 - Char-RNN with abc-notation Data preparation
- 54.8 - Char-RNN with abc-notation Music generation
- 54.9 - Char-RNN with abc-notation Generate tabla music
- 55 - Case Study 11 - Human Activity Recognition
- 55.1 - Human Activity Recognition Problem definition
- 55.2 - Dataset understanding
- 56 - Facebook Friend Recommendation Using Graph Minning
- 56.1 - Problem definition
- 56.6 - EDABasic Stats
- 57 - Database SQL
- 57.20 - Sub QueriesNested QueriesInner Queries
- 57.27 - Learning resources
- 57.5 - Installing MySQL
- 57.6 - Load IMDB data
- 58 - Case Studies/58.1 - AD-Click Predicition
- out_files
- 59 - Interview Questions
- 59.1 - Revision Questions
- 59.2 - Questions
- 59.3 - External resources for Interview Questions
- 6 - Python For Data Science Matplotlib/6.1 - Getting started with Matplotlib
- 7 - Python For Data Science Pandas
- 7.1 - Getting started with pandas
- 7.2 - Data Frame Basics
- 7.3 - Key Operations on Data Frames
- 8 - Python For Data Science Computational Complexity
- 8.1 - Space and Time Complexity Find largest number in a list
- 8.2 - Binary search
- 8.3 - Find elements common in two lists
- 8.4 - Find elements common in two lists using a HashtableDict
- 9 - Plotting for Exploratory Data Analysis (EDA)
- 9.1 - Introduction to IRIS dataset and 2D scatter plot
- 9.10 - Percentiles and Quantiles
- 9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)
- 9.12 - Box-plot with Whiskers
- 9.13 - Violin Plots
- 9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis
- 9.15 - Multivariate Probability Density, Contour Plot
- 9.16 - Exercise Perform EDA on Haberman dataset
- 9.2 - 3D scatter plot
- 9.3 - Pair plots
- 9.4 - Limitations of Pair Plots
- 9.5 - Histogram and Introduction to PDF(Probability Density Function)
- 9.6 - Univariate Analysis using PDF
- 9.7 - CDF(Cumulative Distribution Function)
- 9.8 - Mean, Variance and Standard Deviation
- 9.9 - Median
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