11.74 GB | 28min 22s | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP.mp4 (7.22 MB)
003 Anaconda Python and Jupyter Install and Setup.mp4 (84.53 MB)
005 Environment Setup.mp4 (35.71 MB)
002 Python Crash Course - Part One.mp4 (29.74 MB)
003 Python Crash Course - Part Two.mp4 (57.63 MB)
004 Python Crash Course - Part Three.mp4 (32.01 MB)
005 Python Crash Course - Exercise Questions.mp4 (3.41 MB)
006 Python Crash Course - Exercise Solutions.mp4 (48.7 MB)
001 Machine Learning Pathway.mp4 (14.1 MB)
001 Introduction to NumPy.mp4 (3.37 MB)
002 NumPy Arrays.mp4 (99.45 MB)
003 NumPy Indexing and Selection.mp4 (39.63 MB)
004 NumPy Operations.mp4 (36.06 MB)
005 NumPy Exercises.mp4 (9.64 MB)
006 Numpy Exercises - Solutions.mp4 (34.88 MB)
001 Introduction to Pandas.mp4 (6.7 MB)
002 Series - Part One.mp4 (28.62 MB)
003 Series - Part Two.mp4 (26.12 MB)
004 DataFrames - Part One - Creating a DataFrame.mp4 (97.48 MB)
005 DataFrames - Part Two - Basic Properties.mp4 (40.28 MB)
006 DataFrames - Part Three - Working with Columns.mp4 (84.08 MB)
007 DataFrames - Part Four - Working with Rows.mp4 (72.59 MB)
008 Pandas - Conditional Filtering.mp4 (69.21 MB)
009 Pandas - Useful Methods - Apply on Single Column.mp4 (53.72 MB)
010 Pandas - Useful Methods - Apply on Multiple Columns.mp4 (85.32 MB)
011 Pandas - Useful Methods - Statistical Information and Sorting.mp4 (74.37 MB)
012 Missing Data - Overview.mp4 (27.24 MB)
013 Missing Data - Pandas Operations.mp4 (73.6 MB)
014 GroupBy Operations - Part One.mp4 (86.96 MB)
015 GroupBy Operations - Part Two - MultiIndex.mp4 (92.86 MB)
016 Combining DataFrames - Concatenation.mp4 (36.84 MB)
017 Combining DataFrames - Inner Merge.mp4 (40.27 MB)
018 Combining DataFrames - Left and Right Merge.mp4 (16.4 MB)
019 Combining DataFrames - Outer Merge.mp4 (22.17 MB)
020 Pandas - Text Methods for String Data.mp4 (45.12 MB)
021 Pandas - Time Methods for Date and Time Data.mp4 (80.19 MB)
022 Pandas Input and Output - CSV Files.mp4 (37.15 MB)
023 Pandas Input and Output - HTML Tables.mp4 (102.34 MB)
024 Pandas Input and Output - Excel Files.mp4 (25.87 MB)
025 Pandas Input and Output - SQL Databases.mp4 (95.98 MB)
026 Pandas Pivot Tables.mp4 (129.09 MB)
027 Pandas Project Exercise Overview.mp4 (39.43 MB)
028 Pandas Project Exercise Solutions.mp4 (172.55 MB)
001 Introduction to Matplotlib.mp4 (6.55 MB)
002 Matplotlib Basics.mp4 (31.07 MB)
003 Matplotlib - Understanding the Figure Object.mp4 (11.7 MB)
004 Matplotlib - Implementing Figures and Axes.mp4 (34.86 MB)
005 Matplotlib - Figure Parameters.mp4 (13.06 MB)
006 Matplotlib - Subplots Functionality.mp4 (96.57 MB)
007 Matplotlib Styling - Legends.mp4 (16.19 MB)
008 Matplotlib Styling - Colors and Styles.mp4 (44.27 MB)
009 Advanced Matplotlib Commands (Optional).mp4 (25.19 MB)
010 Matplotlib Exercise Questions Overview.mp4 (48.99 MB)
011 Matplotlib Exercise Questions - Solutions.mp4 (105.86 MB)
001 Introduction to Seaborn.mp4 (5.74 MB)
002 Scatterplots with Seaborn.mp4 (111.3 MB)
003 Distribution Plots - Part One - Understanding Plot Types.mp4 (15.03 MB)
004 Distribution Plots - Part Two - Coding with Seaborn.mp4 (59.21 MB)
005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 (15.98 MB)
006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 (51.65 MB)
007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 (44.96 MB)
008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 (84.57 MB)
009 Seaborn - Comparison Plots - Understanding the Plot Types.mp4 (10.57 MB)
010 Seaborn - Comparison Plots - Coding with Seaborn.mp4 (51.16 MB)
011 Seaborn Grid Plots.mp4 (87.01 MB)
012 Seaborn - Matrix Plots.mp4 (61.47 MB)
013 Seaborn Plot Exercises Overview.mp4 (47.88 MB)
014 Seaborn Plot Exercises Solutions.mp4 (105.72 MB)
001 Capstone Project Overview.mp4 (31.11 MB)
002 Capstone Project Solutions - Part One.mp4 (110.61 MB)
003 Capstone Project Solutions - Part Two.mp4 (106.18 MB)
004 Capstone Project Solutions - Part Three.mp4 (137.39 MB)
001 Introduction to Machine Learning Overview Section.mp4 (13.17 MB)
002 Why Machine Learning.mp4 (21.04 MB)
003 Types of Machine Learning Algorithms.mp4 (18.08 MB)
004 Supervised Machine Learning Process.mp4 (33.53 MB)
005 Companion Book - Introduction to Statistical Learning.mp4 (5.11 MB)
001 Introduction to Linear Regression Section.mp4 (2.58 MB)
002 Linear Regression - Algorithm History.mp4 (54.82 MB)
003 Linear Regression - Understanding Ordinary Least Squares.mp4 (86.37 MB)
004 Linear Regression - Cost Functions.mp4 (16.63 MB)
005 Linear Regression - Gradient Descent.mp4 (29.21 MB)
006 Python coding Simple Linear Regression.mp4 (70.14 MB)
007 Overview of Scikit-Learn and Python.mp4 (31.44 MB)
008 Linear Regression - Scikit-Learn Train Test Split.mp4 (61.42 MB)
009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 (53.4 MB)
010 Linear Regression - Residual Plots.mp4 (44.02 MB)
011 Linear Regression - Model Deployment and Coefficient Interpretation.mp4 (81.14 MB)
012 Polynomial Regression - Theory and Motivation.mp4 (22.25 MB)
013 Polynomial Regression - Creating Polynomial Features.mp4 (40.09 MB)
014 Polynomial Regression - Training and Evaluation.mp4 (36.3 MB)
015 Bias Variance Trade-Off.mp4 (36.18 MB)
016 Polynomial Regression - Choosing Degree of Polynomial.mp4 (55.68 MB)
017 Polynomial Regression - Model Deployment.mp4 (23.22 MB)
018 Regularization Overview.mp4 (15.52 MB)
019 Feature Scaling.mp4 (24.34 MB)
020 Introduction to Cross Validation.mp4 (32.97 MB)
021 Regularization Data Setup.mp4 (20.16 MB)
022 L2 Regularization - Ridge Regression Theory.mp4 (61.3 MB)
023 L2 Regularization - Ridge Regression - Python Implementation.mp4 (89.37 MB)
024 L1 Regularization - Lasso Regression - Background and Implementation.mp4 (94.65 MB)
025 L1 and L2 Regularization - Elastic Net.mp4 (66.4 MB)
026 Linear Regression Project - Data Overview.mp4 (16.94 MB)
002 Introduction to Feature Engineering and Data Preparation.mp4 (36.11 MB)
003 Dealing with Outliers.mp4 (103.32 MB)
004 Dealing with Missing Data   Part One - Evaluation of Missing Data.mp4 (19.05 MB)
005 Dealing with Missing Data   Part Two - Filling or Dropping data based on Rows.mp4 (117.56 MB)
006 Dealing with Missing Data   Part 3 - Fixing data based on Columns.mp4 (105.22 MB)
007 Dealing with Categorical Data - Encoding Options.mp4 (58.87 MB)
001 Section Overview and Introduction.mp4 (5.61 MB)
002 Cross Validation - Test   Train Split.mp4 (46.86 MB)
003 Cross Validation - Test   Validation   Train Split.mp4 (59.41 MB)
004 Cross Validation - cross val score.mp4 (44.46 MB)
005 Cross Validation - cross validate.mp4 (45.01 MB)
006 Grid Search.mp4 (73.19 MB)
007 Linear Regression Project Overview.mp4 (23.63 MB)
008 Linear Regression Project - Solutions.mp4 (91.23 MB)
002 Introduction to Logistic Regression Section.mp4 (13.93 MB)
003 Logistic Regression - Theory and Intuition - Part One  The Logistic Function.mp4 (17.31 MB)
004 Logistic Regression - Theory and Intuition - Part Two  Linear to Logistic.mp4 (8.03 MB)
005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 (36.04 MB)
006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 (54.91 MB)
007 Logistic Regression with Scikit-Learn - Part One - EDA.mp4 (62.45 MB)
008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 (32.57 MB)
009 Classification Metrics - Confusion Matrix and Accuracy.mp4 (21.72 MB)
010 Classification Metrics - Precison, Recall, F1-Score.mp4 (33.14 MB)
011 Classification Metrics - ROC Curves.mp4 (16.07 MB)
012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4 (57.03 MB)
013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4 (37.38 MB)
014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4 (105.09 MB)
015 Logistic Regression Exercise Project Overview.mp4 (24.29 MB)
016 Logistic Regression Project Exercise - Solutions.mp4 (161.29 MB)
001 Introduction to KNN Section.mp4 (3.65 MB)
002 KNN Classification - Theory and Intuition.mp4 (23.55 MB)
003 KNN Coding with Python - Part One.mp4 (61.55 MB)
004 KNN Coding with Python - Part Two - Choosing K.mp4 (102.86 MB)
005 KNN Classification Project Exercise Overview.mp4 (21.12 MB)
006 KNN Classification Project Exercise Solutions.mp4 (105.03 MB)
001 Introduction to Support Vector Machines.mp4 (2.79 MB)
002 History of Support Vector Machines.mp4 (15.54 MB)
003 SVM - Theory and Intuition - Hyperplanes and Margins.mp4 (47.74 MB)
004 SVM - Theory and Intuition - Kernel Intuition.mp4 (9.83 MB)
005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 (52.62 MB)
006 SVM with Scikit-Learn and Python - Classification Part One.mp4 (46.28 MB)
007 SVM with Scikit-Learn and Python - Classification Part Two.mp4 (90.63 MB)
008 SVM with Scikit-Learn and Python - Regression Tasks.mp4 (76.27 MB)
009 Support Vector Machine Project Overview.mp4 (34.84 MB)
010 Support Vector Machine Project Solutions.mp4 (93.36 MB)
001 Introduction to Tree Based Methods.mp4 (2.33 MB)
002 Decision Tree - History.mp4 (35.58 MB)
003 Decision Tree - Terminology.mp4 (7.29 MB)
004 Decision Tree - Understanding Gini Impurity.mp4 (19.45 MB)
005 Constructing Decision Trees with Gini Impurity - Part One.mp4 (17.69 MB)
006 Constructing Decision Trees with Gini Impurity - Part Two.mp4 (52.35 MB)
007 Coding Decision Trees - Part One - The Data.mp4 (98.72 MB)
008 Coding Decision Trees - Part Two -Creating the Model.mp4 (115.8 MB)
001 Introduction to Random Forests Section.mp4 (2.87 MB)
002 Random Forests - History and Motivation.mp4 (24 MB)
003 Random Forests - Key Hyperparameters.mp4 (8.27 MB)
004 Random Forests - Number of Estimators and Features in Subsets.mp4 (27.31 MB)
005 Random Forests - Bootstrapping and Out-of-Bag Error.mp4 (32.72 MB)
006 Coding Classification with Random Forest Classifier - Part One.mp4 (52.1 MB)
007 Coding Classification with Random Forest Classifier - Part Two.mp4 (130.37 MB)
008 Coding Regression with Random Forest Regressor - Part One - Data.mp4 (13.68 MB)
009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4 (85.01 MB)
010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4 (45.54 MB)
011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4 (50.67 MB)
001 Introduction to Boosting Section.mp4 (2.99 MB)
002 Boosting Methods - Motivation and History.mp4 (21.98 MB)
003 AdaBoost Theory and Intuition.mp4 (41.53 MB)
004 AdaBoost Coding Part One - The Data.mp4 (42.25 MB)
005 AdaBoost Coding Part Two - The Model.mp4 (63.11 MB)
006 Gradient Boosting Theory.mp4 (22.96 MB)
007 Gradient Boosting Coding Walkthrough.mp4 (57.91 MB)
001 Introduction to Supervised Learning Capstone Project.mp4 (29.84 MB)
002 Solution Walkthrough - Supervised Learning Project - Data and EDA.mp4 (106.1 MB)
003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis.mp4 (130.14 MB)
004 Solution Walkthrough - Supervised Learning Project - Tree Models.mp4 (114.21 MB)
001 Introduction to NLP and Naive Bayes Section.mp4 (4.22 MB)
002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 (22.04 MB)
003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp4 (48.61 MB)
004 Feature Extraction from Text - Part One - Theory and Intuition.mp4 (29.4 MB)
005 Feature Extraction from Text - Coding Count Vectorization Manually.mp4 (62.89 MB)
006 Feature Extraction from Text - Coding with Scikit-Learn.mp4 (50.39 MB)
007 Natural Language Processing - Classification of Text - Part One.mp4 (28.26 MB)
008 Natural Language Processing - Classification of Text - Part Two.mp4 (34.77 MB)
009 Text Classification Project Exercise Overview.mp4 (30.54 MB)
010 Text Classification Project Exercise Solutions.mp4 (100.59 MB)
001 Unsupervised Learning Overview.mp4 (13.75 MB)
001 Introduction to K-Means Clustering Section.mp4 (3.55 MB)
002 Clustering General Overview.mp4 (24.86 MB)
003 K-Means Clustering Theory.mp4 (52.49 MB)
004 K-Means Clustering - Coding Part One.mp4 (97.9 MB)
005 K-Means Clustering Coding Part Two.mp4 (80.85 MB)
006 K-Means Clustering Coding Part Three.mp4 (59.77 MB)
007 K-Means Color Quantization - Part One.mp4 (80.57 MB)
008 K-Means Color Quantization - Part Two.mp4 (65.03 MB)
009 K-Means Clustering Exercise Overview.mp4 (59.48 MB)
010 K-Means Clustering Exercise Solution - Part One.mp4 (79.92 MB)
011 K-Means Clustering Exercise Solution - Part Two.mp4 (108.19 MB)
012 K-Means Clustering Exercise Solution - Part Three.mp4 (62.5 MB)
001 Introduction to Hierarchical Clustering.mp4 (1.67 MB)
002 Hierarchical Clustering - Theory and Intuition.mp4 (52.07 MB)
003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4 (114.98 MB)
004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4 (209.23 MB)
001 Introduction to DBSCAN Section.mp4 (1.8 MB)
002 DBSCAN - Theory and Intuition.mp4 (109.09 MB)
003 DBSCAN versus K-Means Clustering.mp4 (66.64 MB)
004 DBSCAN - Hyperparameter Theory.mp4 (13.86 MB)
005 DBSCAN - Hyperparameter Tuning Methods.mp4 (105.08 MB)
006 DBSCAN - Outlier Project Exercise Overview.mp4 (50.27 MB)
007 DBSCAN - Outlier Project Exercise Solutions.mp4 (127.93 MB)
001 Introduction to Principal Component Analysis.mp4 (5.08 MB)
002 PCA Theory and Intuition - Part One.mp4 (29.72 MB)
003 PCA Theory and Intuition - Part Two.mp4 (19.04 MB)
004 PCA - Manual Implementation in Python.mp4 (95.04 MB)
005 PCA - SciKit-Learn.mp4 (74.09 MB)
006 PCA - Project Exercise Overview.mp4 (52.77 MB)
007 PCA - Project Exercise Solution.mp4 (119.45 MB)
001 Model Deployment Section Overview.mp4 (4.16 MB)
002 Model Deployment Considerations.mp4 (18.31 MB)
003 Model Persistence.mp4 (109.76 MB)
004 Model Deployment as an API - General Overview.mp4 (17.48 MB)
006 Model API - Creating the Script.mp4 (67.27 MB)
007 Testing the API.mp4 (33.15 MB)]
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