CourseClub ME AppliedAICourse Applied Machine Learning Course |
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[CourseClub.ME] AppliedAICourse - Applied Machine Learning Course |
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Total Size |
27.2 GB |
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Total Files |
4809 |
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Last Seen |
2025-05-08 23:41 |
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Hash |
470B1FF5490A3567307A728DB3FDC0360F484FD8 |
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/1.1 - How to Learn from Appliedaicourse/ |
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/1.2 - How the Job Guarantee program works/ |
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/10.1 - Why learn it/ |
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/10.10 - Hyper Cube,Hyper Cuboid/ |
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/10.11 - Revision Questions/ |
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/10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector/ |
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10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector.mp4 |
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/10.3 - Dot Product and Angle between 2 Vectors/ |
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/10.4 - Projection and Unit Vector/ |
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/10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane/ |
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/10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces/ |
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10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces.mp4 |
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/10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)/ |
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10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D).mp4 |
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/10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)/ |
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10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D).mp4 |
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/10.9 - Square ,Rectangle/ |
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/11.1 - Introduction to Probability and Statistics/ |
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/11.10 - How distributions are used/ |
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/11.11 - Chebyshev’s inequality/ |
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/11.12 - Discrete and Continuous Uniform distributions/ |
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/11.13 - How to randomly sample data points (Uniform Distribution)/ |
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11.13 - How to randomly sample data points (Uniform Distribution).mp4 |
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/11.14 - Bernoulli and Binomial Distribution/ |
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/11.15 - Log Normal Distribution/ |
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/11.16 - Power law distribution/ |
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/11.17 - Box cox transform/ |
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/11.18 - Applications of non-gaussian distributions/ |
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/11.19 - Co-variance/ |
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/11.2 - Population and Sample/ |
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/11.20 - Pearson Correlation Coefficient/ |
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/11.21 - Spearman Rank Correlation Coefficient/ |
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/11.22 - Correlation vs Causation/ |
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/11.23 - How to use correlations/ |
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/11.24 - Confidence interval (C.I) Introduction/ |
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/11.25 - Computing confidence interval given the underlying distribution/ |
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11.25 - Computing confidence interval given the underlying distribution.mp4 |
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/11.26 - C.I for mean of a normal random variable/ |
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/11.27 - Confidence interval using bootstrapping/ |
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/11.28 - Hypothesis testing methodology, Null-hypothesis, p-value/ |
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11.28 - Hypothesis testing methodology, Null-hypothesis, p-value.mp4 |
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/11.29 - Hypothesis Testing Intution with coin toss example/ |
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11.29 - Hypothesis Testing Intution with coin toss example.mp4 |
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/11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)/ |
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11.3 - GaussianNormal Distribution and its PDF(Probability Density Function).mp4.mkv |
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/11.30 - Resampling and permutation test/ |
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/11.31 - K-S Test for similarity of two distributions/ |
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/11.32 - Code Snippet K-S Test/ |
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/11.33 - Hypothesis testing another example/ |
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/11.34 - Resampling and Permutation test another example/ |
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/11.35 - How to use hypothesis testing/ |
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/11.36 - Proportional Sampling/ |
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/11.37 - Revision Questions/ |
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/11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution/ |
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11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution.mp4 |
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/11.5 - Symmetric distribution, Skewness and Kurtosis/ |
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/11.6 - Standard normal variate (Z) and standardization/ |
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/11.7 - Kernel density estimation/ |
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/11.8 - Sampling distribution & Central Limit theorem/ |
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/11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/ |
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11.9 - Q-Q plotHow to test if a random variable is normally distributed or not.mp4 |
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/12.1 - Questions & Answers/ |
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/13.1 - What is Dimensionality reduction/ |
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/13.10 - Code to Load MNIST Data Set/ |
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/13.2 - Row Vector and Column Vector/ |
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/13.3 - How to represent a data set/ |
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/13.4 - How to represent a dataset as a Matrix/ |
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/13.5 - Data Preprocessing Feature Normalisation/ |
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/13.6 - Mean of a data matrix/ |
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/13.7 - Data Preprocessing Column Standardization/ |
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/13.8 - Co-variance of a Data Matrix/ |
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/13.9 - MNIST dataset (784 dimensional)/ |
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/14.1 - Why learn PCA/ |
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/14.10 - PCA for dimensionality reduction (not-visualization)/ |
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14.10 - PCA for dimensionality reduction (not-visualization).mp4 |
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/14.2 - Geometric intuition of PCA/ |
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/14.3 - Mathematical objective function of PCA/ |
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/14.4 - Alternative formulation of PCA Distance minimization/ |
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14.4 - Alternative formulation of PCA Distance minimization.mp4 |
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/14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction/ |
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14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction.mp4 |
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/14.6 - PCA for Dimensionality Reduction and Visualization/ |
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14.6 - PCA for Dimensionality Reduction and Visualization.mp4 |
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/14.7 - Visualize MNIST dataset/ |
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/14.8 - Limitations of PCA/ |
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/14.9 - PCA Code example/ |
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/15.1 - What is t-SNE/ |
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/15.2 - Neighborhood of a point, Embedding/ |
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/15.3 - Geometric intuition of t-SNE/ |
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/18.1 - How “Classification” works/ |
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/18.4 - K-Nearest Neighbours Geometric intuition with a toy example/ |
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/20.1 - Introduction/ |
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/20.14 - Feature Importance and Forward Feature selection/ |
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/20.17 - curse of dimensionality/ |
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/43.9 - Exploratory Data Analysis Train-Test class distribution/ |
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/44.1 - BusinessReal world problemProblem definition/ |
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/44.10 - Exploratory Data AnalysisCold start problem/ |
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/44.11 - Computing Similarity matricesUser-User similarity matrix/ |
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44.11 - Computing Similarity matricesUser-User similarity matrix.mp4 |
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44.12 - Computing Similarity matricesMovie-Movie similarity.mp4 |
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44.13 - Computing Similarity matricesDoes movie-movie similarity work.mp4 |
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/44.14 - ML ModelsSurprise library/ |
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/44.15 - Overview of the modelling strategy/ |
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/44.16 - Data Sampling/ |
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/44.18 - Featurizations for regression/ |
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/44.2 - Objectives and constraints/ |
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/44.20 - Xgboost with 13 features/ |
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/44.22 - Xgboost + 13 features +Surprise baseline model/ |
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/44.23 - Surprise KNN predictors/ |
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/44.24 - Matrix Factorization models using Surprise/ |
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/44.25 - SVD ++ with implicit feedback/ |
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/44.26 - Final models with all features and predictors/ |
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/44.27 - Comparison between various models/ |
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/44.28 - Assignments/ |
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/44.3 - Mapping to an ML problemData overview/ |
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/44.4 - Mapping to an ML problemML problem formulation/ |
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/44.5 - Exploratory Data AnalysisData preprocessing/ |
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/44.6 - Exploratory Data AnalysisTemporal Train-Test split/ |
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44.6 - Exploratory Data AnalysisTemporal Train-Test split..mp4 |
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/44.7 - Exploratory Data AnalysisPreliminary data analysis/ |
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44.7 - Exploratory Data AnalysisPreliminary data analysis..mp4 |
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/44.8 - Exploratory Data AnalysisSparse matrix representation/ |
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44.8 - Exploratory Data AnalysisSparse matrix representation.mp4 |
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/44.9 - Exploratory Data AnalysisAverage ratings for various slices/ |
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44.9 - Exploratory Data AnalysisAverage ratings for various slices.mp4 |
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/45.1 - BusinessReal world problem Overview/ |
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/45.10 - Univariate AnalysisVariation Feature/ |
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/45.11 - Univariate AnalysisText feature/ |
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/45.12 - Machine Learning ModelsData preparation/ |
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/45.13 - Baseline Model Naive Bayes/ |
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/45.14 - K-Nearest Neighbors Classification/ |
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/45.15 - Logistic Regression with class balancing/ |
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/45.20 - Stacking Classifier/ |
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/45.21 - Majority Voting classifier/ |
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/45.3 - ML problem formulation Data/ |
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/45.4 - ML problem formulation Mapping real world to ML problem#/ |
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45.4 - ML problem formulation Mapping real world to ML problem..mp4 |
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/45.5 - ML problem formulation Train, CV and Test data construction/ |
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45.5 - ML problem formulation Train, CV and Test data construction.mp4 |
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/45.6 - Exploratory Data AnalysisReading data & preprocessing/ |
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45.6 - Exploratory Data AnalysisReading data & preprocessing.mp4 |
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/45.7 - Exploratory Data AnalysisDistribution of Class-labels/ |
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45.7 - Exploratory Data AnalysisDistribution of Class-labels.mp4 |
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/45.8 - Exploratory Data Analysis “Random” Model/ |
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/45.9 - Univariate AnalysisGene feature/ |
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/46.1 - BusinessReal world problem Overview/ |
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/46.10 - Data Cleaning Speed/ |
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/46.12 - Data Cleaning Fare/ |
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/46.13 - Data Cleaning Remove all outlierserroneous points/ |
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46.13 - Data Cleaning Remove all outlierserroneous points.mp4 |
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/46.14 - Data PreparationClusteringSegmentation/ |
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/46.15 - Data PreparationTime binning/ |
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/46.16 - Data PreparationSmoothing time-series data/ |
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/46.17 - Data PreparationSmoothing time-series data cont/ |
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46.17 - Data PreparationSmoothing time-series data cont...mp4 |
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/46.18 - Data Preparation Time series and Fourier transforms/ |
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46.18 - Data Preparation Time series and Fourier transforms..mp4 |
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/46.19 - Ratios and previous-time-bin values/ |
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/46.2 - Objectives and Constraints/ |
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/46.20 - Simple moving average/ |
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/46.21 - Weighted Moving average/ |
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/46.22 - Exponential weighted moving average/ |
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/46.23 - Results/ |
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/53.1 - Self Driving Car Problem definition/ |
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/53.10 - NVIDIA’s end to end CNN model/ |
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/53.14 - Assignment/ |
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/53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN/ |
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/57.1 - Introduction to Databases/ |
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/57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM/ |
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/57.15 - GROUP BY/ |
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/57.16 - HAVING/ |
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/57.17 - Order of keywords#/ |
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/57.18 - Join and Natural Join/ |
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/57.19 - Inner, Left, Right and Outer joins/ |
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/57.2 - Why SQL/ |
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/57.20 - Sub QueriesNested QueriesInner Queries/ |
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/57.21 - DMLINSERT/ |
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/57.22 - DMLUPDATE , DELETE/ |
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/57.23 - DDLCREATE TABLE/ |
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/57.24 - DDLALTER ADD, MODIFY, DROP/ |
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/57.25 - DDLDROP TABLE, TRUNCATE, DELETE/ |
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/57.26 - Data Control Language GRANT, REVOKE/ |
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/57.27 - Learning resources/ |
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/57.3 - Execution of an SQL statement/ |
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/57.4 - IMDB dataset/ |
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/57.5 - Installing MySQL/ |
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/57.6 - Load IMDB data/ |
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/57.7 - USE, DESCRIBE, SHOW TABLES/ |
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/57.8 - SELECT/ |
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/57.9 - LIMIT, OFFSET/ |
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/58.1 - AD-Click Predicition/ |
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/out_files/ |
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/59.1 - Revision Questions/ |
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/59.2 - Questions/ |
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/59.3 - External resources for Interview Questions/ |
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/6.1 - Getting started with Matplotlib/ |
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/7.1 - Getting started with pandas/ |
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/7.2 - Data Frame Basics/ |
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/7.3 - Key Operations on Data Frames/ |
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/8.1 - Space and Time Complexity Find largest number in a list/ |
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8.1 - Space and Time Complexity Find largest number in a list.mp4 |
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/8.2 - Binary search/ |
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/8.3 - Find elements common in two lists/ |
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/8.4 - Find elements common in two lists using a HashtableDict/ |
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8.4 - Find elements common in two lists using a HashtableDict.mp4 |
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/9.1 - Introduction to IRIS dataset and 2D scatter plot/ |
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9.1 - Introduction to IRIS dataset and 2D scatter plot.mp4.mkv |
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/9.10 - Percentiles and Quantiles/ |
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/9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)/ |
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9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation).mp4 |
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/9.12 - Box-plot with Whiskers/ |
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/9.13 - Violin Plots/ |
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/9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis/ |
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9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis.mp4 |
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/9.15 - Multivariate Probability Density, Contour Plot/ |
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/9.16 - Exercise Perform EDA on Haberman dataset/ |
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/9.2 - 3D scatter plot/ |
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/9.3 - Pair plots/ |
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/9.4 - Limitations of Pair Plots/ |
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/9.5 - Histogram and Introduction to PDF(Probability Density Function)/ |
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9.5 - Histogram and Introduction to PDF(Probability Density Function).mkv |
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/9.6 - Univariate Analysis using PDF/ |
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/9.7 - CDF(Cumulative Distribution Function)/ |
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/9.8 - Mean, Variance and Standard Deviation/ |
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/9.9 - Median/ |
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