001 Introduction - Why do this course Why Apply Data Science to Business.mp4 (149.56 MB)
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002 Why Data is the new Oil and what most Businesses are doing wrong.mp4 (86.47 MB)
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003 Defining Business Problems for Analytic Thinking & Data Driven Decision Making.mp4 (13.27 MB)
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005 10 Data Science Projects every Business should do!.mp4 (42.71 MB)
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006 Making Sense of Buzz Words, Data Science, Big Data, Machine & Deep Learning.mp4 (17.85 MB)
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007 How Deep Learning is Changing Everything!.mp4 (25.05 MB)
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008 The Roles in the Data World - Analyst, Engineer, Scientist, Statistician, DevOps.mp4 (17.46 MB)
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009 How Data Scientists Approach Problems.mp4 (22.84 MB)
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001 Course Approach - Different Options for Different Students.mp4 (3.98 MB)
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002 Setup Google Colab for your iPython Notebooks (Download Course Code + Slides).mp4 (21.91 MB)
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001 Why use Python for Data Science.mp4 (9.39 MB)
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002 Python - Basic Variables.mp4 (15.52 MB)
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003 Python - Variables (Lists and Dictionaries).mp4 (36.34 MB)
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004 Python - Conditional Statements.mp4 (18.57 MB)
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006 Python - Loops.mp4 (23.84 MB)
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007 Python - Functions.mp4 (11.34 MB)
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008 Python - Classes.mp4 (25.3 MB)
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001 Introduction to Pandas.mp4 (7.44 MB)
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002 Pandas 1 - Data Series.mp4 (19.73 MB)
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003 Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering.mp4 (124.99 MB)
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004 Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering.mp4 (35.72 MB)
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005 Pandas 3A - Data Cleaning - Alter ColomnsRows, Missing Data & String Operations.mp4 (49.04 MB)
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006 Pandas 3B - Data Cleaning - Alter ColomnsRows, Missing Data & String Operations.mp4 (135.34 MB)
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007 Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggreate Functions.mp4 (92.15 MB)
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008 Pandas 5 - Feature Engineer, Lambda and Apply.mp4 (24.85 MB)
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009 Pandas 6 - Concatenating, Merging and Joinining.mp4 (77.79 MB)
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010 Pandas 7 - Time Series Data.mp4 (30.49 MB)
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011 Pandas 7 - ADVANCED Operations - Iterows, Vectorization and Numpy.mp4 (85.14 MB)
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012 Pandas 8 - ADVANCED Operations - More Map, Zip and Apply.mp4 (16.95 MB)
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013 Pandas 9 - ADVANCED Operations - Parallel Processing.mp4 (24.26 MB)
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014 Map Visualizations with Plotly - Cloropeths from Scratch - USA and World.mp4 (91.69 MB)
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015 Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines.mp4 (46.98 MB)
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001 Introdution to Statistics.mp4 (17.14 MB)
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002 Descriptive Statistics - Why Statistical Knowledge is so Important.mp4 (11.25 MB)
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003 Descriptive Statistics 1 - Exploratory Data Analysis (EDA) & Visualizations.mp4 (76.19 MB)
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004 Descriptive Statistics 2 - Exploratory Data Analysis (EDA) & Visualizations.mp4 (40.15 MB)
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005 Sampling, Averages & Variance And How to lie and Mislead with Statistics.mp4 (8.43 MB)
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006 Sampling - Sample Sizes & Confidence Intervals - What Can You Trust.mp4 (34.36 MB)
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007 Types of Variables - Quantitive and Qualitative.mp4 (35.69 MB)
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008 Frequency Distributions.mp4 (11.41 MB)
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009 Frequency Distributions Shapes.mp4 (16.77 MB)
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010 Analyzing Frequency Distributions - What is the Best Type of WIne Red or White.mp4 (56.01 MB)
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011 Mean, Mode and Median - Not as Simple As You'd Think.mp4 (76.47 MB)
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012 Variance, Standard Deviation and Bessel's Correction.mp4 (57.02 MB)
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013 Covariance & Correlation - Do Amazon & Google know you better than anyone else.mp4 (73.18 MB)
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014 Lying with Correlations - Divorce Rates in Maine caused by Margarine Consumption.mp4 (4.08 MB)
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015 The Normal Distribution & the Central Limit Theorem.mp4 (11.45 MB)
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016 Z-Scores.mp4 (38.79 MB)
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001 Probability - An Introduction.mp4 (3.89 MB)
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002 Estimating Probability.mp4 (11.91 MB)
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003 Addition Rule.mp4 (22.75 MB)
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004 Permutations & Combinations.mp4 (20.51 MB)
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005 Bayes Theorem.mp4 (40.81 MB)
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001 Hypothesis Testing Introduction.mp4 (7.01 MB)
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002 Statistical Significance.mp4 (24.24 MB)
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003 Hypothesis Testing - P Value.mp4 (18.3 MB)
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004 Hypothesis Testing - Pearson Correlation.mp4 (18.38 MB)
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001 Introduction to Machine Learning.mp4 (12.11 MB)
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002 How Machine Learning enables Computers to Learn.mp4 (16.99 MB)
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003 What is a Machine Learning Model.mp4 (19.61 MB)
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004 Types of Machine Learning.mp4 (44.04 MB)
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005 Linear Regression - Introduction to Cost Functions and Gradient Descent.mp4 (27.3 MB)
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006 Linear Regressions in Python from Scratch and using Sklearn.mp4 (69.58 MB)
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007 Polynomial and Multivariate Linear Regression.mp4 (42.07 MB)
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008 Logistic Regression.mp4 (56.2 MB)
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009 Support Vector Machines (SVMs).mp4 (28.97 MB)
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010 Decision Trees and Random Forests & the Gini Index.mp4 (64.54 MB)
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011 K-Nearest Neighbors (KNN).mp4 (21.2 MB)
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012 Assessing Performance - Confusion Matrix, Precision and Recall.mp4 (83.9 MB)
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013 Understanding the ROC and AUC Curve.mp4 (34.79 MB)
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014 What Makes a Good Model Regularization, Overfitting, Generalization & Outliers.mp4 (90.53 MB)
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015 Introduction to Neural Networks.mp4 (9.01 MB)
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016 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs.mp4 (27.47 MB)
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001 Neural Networks Chapter Overview.mp4 (3.13 MB)
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002 Machine Learning Overview.mp4 (22.79 MB)
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003 Neural Networks Explained.mp4 (8.37 MB)
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004 Forward Propagation.mp4 (42.51 MB)
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005 Activation Functions.mp4 (29.63 MB)
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006 Training Part 1 - Loss Functions.mp4 (29.45 MB)
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007 Training Part 2 - Backpropagation and Gradient Descent.mp4 (35.87 MB)
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008 Backpropagation & Learning Rates - A Worked Example.mp4 (65.41 MB)
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009 Regularization, Overfitting, Generalization and Test Datasets.mp4 (78.78 MB)
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010 Epochs, Iterations and Batch Sizes.mp4 (19.35 MB)
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011 Measuring Performance and the Confusion Matrix.mp4 (34.61 MB)
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012 Review and Best Practices.mp4 (18.18 MB)
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001 Figuring Out Which Employees May Quit -Understanding the Problem & EDA.mp4 (108.69 MB)
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002 Data Cleaning and Preparation.mp4 (27.04 MB)
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003 Machine Learning Modeling + Deep Learning.mp4 (118.52 MB)
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001 Understanding the Problem.mp4 (24.1 MB)
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002 Exploratory Data Analysis & Visualizations.mp4 (38.76 MB)
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003 Data Preprocessing.mp4 (24.06 MB)
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004 Machine Learning Modeling + Deep Learning.mp4 (118.15 MB)
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001 Understanding the Problem.mp4 (13.49 MB)
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002 Exploratory Data Analysis and Visualizations.mp4 (43.14 MB)
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003 Preparing our Dataset for Machine Learning.mp4 (117.25 MB)
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004 Modeling using Grid Search for finding the best parameters.mp4 (32.55 MB)
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001 Understanding the Problem + Exploratory Data Analysis and Visualizations.mp4 (61.68 MB)
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002 Data Preparation and Machine Learning Modeling.mp4 (109.27 MB)
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001 Understanding the Problem + Exploratory Data Analysis and Visualizations.mp4 (161.41 MB)
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002 Machine Learning Modeling.mp4 (154.58 MB)
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003 Using our Model for Value Estimation for New Clients.mp4 (31.49 MB)
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001 Understanding our Dataset.mp4 (30.63 MB)
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002 Exploratory Analysis.mp4 (28.52 MB)
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003 Feature Extraction.mp4 (25.05 MB)
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004 Creating and Validating Our Model.mp4 (39.81 MB)
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001 Exploratory Analysis of Understanding Marketing Conversion Rates.mp4 (72.09 MB)
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001 Understanding the Problem + Exploratory Data Analysis and Visualizations.mp4 (48.84 MB)
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002 Data Preparation and Machine Learning Modeling.mp4 (55.72 MB)
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001 Understanding the Problem + Exploratory Data Analysis and Visualizations.mp4 (70.67 MB)
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002 AB Test Result Analysis.mp4 (30.98 MB)
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003 AB Testing a Worked Real Life Example - Designing an AB Test.mp4 (29.96 MB)
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004 Statistical Power and Significance.mp4 (25.93 MB)
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005 Analysis of AB Test Resutls.mp4 (41.67 MB)
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001 Problem and Plan of Attack.mp4 (70.38 MB)
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002 Sales and Revenue Analysis.mp4 (64.52 MB)
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003 Analysis per Country, Repeat Customers and Items.mp4 (83.78 MB)
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001 Understanding the Problem + Exploratory Data Analysis and Visualizations.mp4 (41.02 MB)
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002 Customer Lifetime Value Modeling.mp4 (40.17 MB)
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001 Introdution to Unsupervised Learning.mp4 (23.74 MB)
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002 K-Means Clustering.mp4 (35.11 MB)
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003 Choosing K - Elbow Method & Silhouette Analysis.mp4 (30.04 MB)
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004 K-Means in Python - Choosing K using the Elbow Method & Silhoutte Analysis.mp4 (61.59 MB)
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005 Agglomerative Hierarchical Clustering.mp4 (17.74 MB)
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006 Mean-Shift Clustering.mp4 (10.27 MB)
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007 DBSCAN (Density-Based Spatial Clustering of Applications with Noise).mp4 (16.58 MB)
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008 DBSCAN in Python.mp4 (11.26 MB)
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009 Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM).mp4 (21.58 MB)
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001 Principal Component Analysis.mp4 (64.56 MB)
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002 t-Distributed Stochastic Neighbor Embedding (t-SNE).mp4 (28.09 MB)
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003 PCA & t-SNE in Python with Visualization Comparisons.mp4 (26.41 MB)
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001 Data Exploration & Description.mp4 (48.99 MB)
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002 Simple Exploratory Data Analysis and Visualizations.mp4 (53.47 MB)
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003 Feature Engineering.mp4 (66.82 MB)
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004 K-Means Clustering of Customer Data.mp4 (107.17 MB)
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005 Cluster Analysis.mp4 (32.06 MB)
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001 Introduction to Recommendation Engines.mp4 (26.05 MB)
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002 Before recommending, how do we rate or review Items Thought Experiment.mp4 (30.43 MB)
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003 User Collaborative Filtering and ItemContent-based Filtering.mp4 (61.27 MB)
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004 The Netflix Prize, Matrix Factorization & Deep Learning as Latent-Factor Methods.mp4 (44.18 MB)
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001 Dataset Description and Data Cleaning.mp4 (27.84 MB)
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002 Making a Customer-Item Matrix.mp4 (25.32 MB)
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003 User-User Matrix - Getting Recommended Items for each Customer.mp4 (114.33 MB)
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004 Item-Item Collaborative Filtering - Finding the Most Similar Items.mp4 (51.25 MB)
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001 Introduction to Natural Language Processing.mp4 (12.9 MB)
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002 Modeling Language - The Bag of Words Model.mp4 (14.63 MB)
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003 Normalization, Stop Word Removal, LemmatizingStemming.mp4 (15.21 MB)
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004 TF-IDF Vectorizer (Term Frequency - Inverse Document Frequency).mp4 (5.41 MB)
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005 Word2Vec - Efficient Estimation of Word Representations in Vector Space.mp4 (21.77 MB)
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001 Convolutional Neural Networks Chapter Overview.mp4 (2.06 MB)
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002 Convolutional Neural Networks Introduction.mp4 (24.31 MB)
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003 Convolutions & Image Features.mp4 (66.1 MB)
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004 Depth, Stride and Padding.mp4 (22.78 MB)
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005 ReLU.mp4 (3.78 MB)
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006 Pooling.mp4 (14.52 MB)
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007 The Fully Connected Layer.mp4 (5.99 MB)
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008 Training CNNs.mp4 (12.42 MB)
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009 Design Your Own CNN.mp4 (8.84 MB)
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010 Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs - Promo.mp4 (53.49 MB)
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011 Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs - Introduction.mp4 (67.97 MB)
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Udemy - Data Science & Deep Learning for Businessâ„¢ 20 Case Studies
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