4.2 GB | 00:30:14 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English
Files Included :
001 Course Overview (69.36 MB)
001 Lesson 1 1 Biggest Milestones in AI History (77.14 MB)
002 Lesson 1 2 Artificial Intelligence Principles (90.33 MB)
001 Lesson 2 1 Introduction to the Mathematical Principles of Machine Learning (154.08 MB)
002 Lesson 2 2 Vectors (126.46 MB)
003 Lesson 2 3 Matrices Basics (170.21 MB)
004 Lesson 2 4 Matrices Decomposition (104.27 MB)
001 Lesson 3 1 Introduction to the Statistical Principles of Machine Learning (127.49 MB)
002 Lesson 3 2 Probability Distributions (116.83 MB)
003 Lesson 3 3 Descriptive Statistics Basics for Machine Learning - Measures of Central Tendency (108.8 MB)
004 Lesson 3 4 Descriptive Statistics Basics for Machine Learning - Measures of Dispersion (117.23 MB)
005 Lesson 3 5 Statistical Inference and Modeling Basics (129.3 MB)
001 Lesson 4 1 Supervision in Machine Learning (165.77 MB)
002 Lesson 4 2 Introduction to Feature Engineering (161.73 MB)
003 Lesson 4 3 Intro to Regression and Regression Metrics (99.35 MB)
004 Lesson 4 4 Linear Regression Basics - Closed-form solution (129.73 MB)
005 Lesson 4 5 Linear Regression Basics - Multivariate Regression (69.68 MB)
006 Lesson 4 6 Linear Regression in Python - Simple and Multivariate Regression (125.4 MB)
007 Lesson 4 7 Linear Regression in Python - Testing the Assumptions (141.76 MB)
008 Lesson 4 8 Intro to Classification (56.71 MB)
009 Lesson 4 9 Classification Metrics (146.58 MB)
010 Lesson 4 10 Class Imbalance Problems (153.74 MB)
011 Lesson 4 11 Logistic Regression Basics (111.54 MB)
012 Lesson 4 12 Decision Trees Basics (156.68 MB)
013 Lesson 4 13 Supervised Learning in Python (194.05 MB)
014 Lesson 4 14 Introduction to Hyperparameter Optimization (163.64 MB)
001 Lesson 5 1 Unsupervised Machine Learning (106.81 MB)
002 Lesson 5 2 Clustering Basics (62 MB)
003 Lesson 5 3 k-Means Clustering (141.44 MB)
004 Lesson 5 4 Principal Component Analysis (PCA) Basics (119.91 MB)
005 Lesson 5 5 Unsupervised Learning in Python (163.25 MB)
001 Lesson 6 1 Introduction to Natural Language Processing (NLP) (133.5 MB)
002 Lesson 6 2 Text Representation Methods - Part 1 Bag-of-Words & N-Grams (104.49 MB)
003 Lesson 6 3 Text Representation Methods - Part 2 TF-IDF Method (123.61 MB)
004 Lesson 6 4 Text Representation Methods - Part 3 Word Embeddings (76.88 MB)
001 Lesson 1 1 Biggest Milestones in AI History (77.14 MB)
002 Lesson 1 2 Artificial Intelligence Principles (90.33 MB)
001 Lesson 2 1 Introduction to the Mathematical Principles of Machine Learning (154.08 MB)
002 Lesson 2 2 Vectors (126.46 MB)
003 Lesson 2 3 Matrices Basics (170.21 MB)
004 Lesson 2 4 Matrices Decomposition (104.27 MB)
001 Lesson 3 1 Introduction to the Statistical Principles of Machine Learning (127.49 MB)
002 Lesson 3 2 Probability Distributions (116.83 MB)
003 Lesson 3 3 Descriptive Statistics Basics for Machine Learning - Measures of Central Tendency (108.8 MB)
004 Lesson 3 4 Descriptive Statistics Basics for Machine Learning - Measures of Dispersion (117.23 MB)
005 Lesson 3 5 Statistical Inference and Modeling Basics (129.3 MB)
001 Lesson 4 1 Supervision in Machine Learning (165.77 MB)
002 Lesson 4 2 Introduction to Feature Engineering (161.73 MB)
003 Lesson 4 3 Intro to Regression and Regression Metrics (99.35 MB)
004 Lesson 4 4 Linear Regression Basics - Closed-form solution (129.73 MB)
005 Lesson 4 5 Linear Regression Basics - Multivariate Regression (69.68 MB)
006 Lesson 4 6 Linear Regression in Python - Simple and Multivariate Regression (125.4 MB)
007 Lesson 4 7 Linear Regression in Python - Testing the Assumptions (141.76 MB)
008 Lesson 4 8 Intro to Classification (56.71 MB)
009 Lesson 4 9 Classification Metrics (146.58 MB)
010 Lesson 4 10 Class Imbalance Problems (153.74 MB)
011 Lesson 4 11 Logistic Regression Basics (111.54 MB)
012 Lesson 4 12 Decision Trees Basics (156.68 MB)
013 Lesson 4 13 Supervised Learning in Python (194.05 MB)
014 Lesson 4 14 Introduction to Hyperparameter Optimization (163.64 MB)
001 Lesson 5 1 Unsupervised Machine Learning (106.81 MB)
002 Lesson 5 2 Clustering Basics (62 MB)
003 Lesson 5 3 k-Means Clustering (141.44 MB)
004 Lesson 5 4 Principal Component Analysis (PCA) Basics (119.91 MB)
005 Lesson 5 5 Unsupervised Learning in Python (163.25 MB)
001 Lesson 6 1 Introduction to Natural Language Processing (NLP) (133.5 MB)
002 Lesson 6 2 Text Representation Methods - Part 1 Bag-of-Words & N-Grams (104.49 MB)
003 Lesson 6 3 Text Representation Methods - Part 2 TF-IDF Method (123.61 MB)
004 Lesson 6 4 Text Representation Methods - Part 3 Word Embeddings (76.88 MB)
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