Machine Learning Fundamentals A Python-Based Course
File List
- Module 4 Supervised Learning Fundamentals/013. Lesson 4.13 Supervised Learning in Python.mp4 194.0 MB
- Module 2 Mathematical Concepts of Machine Learning/003. Lesson 2.3 Matrices Basics.mp4 170.2 MB
- Module 4 Supervised Learning Fundamentals/001. Lesson 4.1 Supervision in Machine Learning.mp4 165.8 MB
- Module 4 Supervised Learning Fundamentals/014. Lesson 4.14 Introduction to Hyperparameter Optimization.mp4 163.6 MB
- Module 5 Unsupervised Learning Fundamentals/005. Lesson 5.5 Unsupervised Learning in Python.mp4 163.3 MB
- Module 4 Supervised Learning Fundamentals/002. Lesson 4.2 Introduction to Feature Engineering.mp4 161.7 MB
- Module 4 Supervised Learning Fundamentals/012. Lesson 4.12 Decision Trees Basics.mp4 156.7 MB
- Module 2 Mathematical Concepts of Machine Learning/001. Lesson 2.1 Introduction to the Mathematical Principles of Machine Learning.mp4 154.1 MB
- Module 4 Supervised Learning Fundamentals/010. Lesson 4.10 Class Imbalance Problems.mp4 153.7 MB
- Module 4 Supervised Learning Fundamentals/009. Lesson 4.9 Classification Metrics.mp4 146.6 MB
- Module 4 Supervised Learning Fundamentals/007. Lesson 4.7 Linear Regression in Python - Testing the Assumptions.mp4 141.8 MB
- Module 5 Unsupervised Learning Fundamentals/003. Lesson 5.3 k-Means Clustering.mp4 141.4 MB
- Module 6 Machine Learning Applications/001. Lesson 6.1 Introduction to Natural Language Processing (NLP).mp4 133.5 MB
- Module 4 Supervised Learning Fundamentals/004. Lesson 4.4 Linear Regression Basics - Closed-form solution.mp4 129.7 MB
- Module 3 Statistical Concepts of Machine Learning/005. Lesson 3.5 Statistical Inference and Modeling Basics.mp4 129.3 MB
- Module 3 Statistical Concepts of Machine Learning/001. Lesson 3.1 Introduction to the Statistical Principles of Machine Learning.mp4 127.5 MB
- Module 2 Mathematical Concepts of Machine Learning/002. Lesson 2.2 Vectors.mp4 126.5 MB
- Module 4 Supervised Learning Fundamentals/006. Lesson 4.6 Linear Regression in Python - Simple and Multivariate Regression.mp4 125.4 MB
- Module 6 Machine Learning Applications/003. Lesson 6.3 Text Representation Methods - Part 2 TF-IDF Method.mp4 123.6 MB
- Module 5 Unsupervised Learning Fundamentals/004. Lesson 5.4 Principal Component Analysis (PCA) Basics.mp4 119.9 MB
- Module 3 Statistical Concepts of Machine Learning/004. Lesson 3.4 Descriptive Statistics Basics for Machine Learning - Measures of Dispersion.mp4 117.2 MB
- Module 3 Statistical Concepts of Machine Learning/002. Lesson 3.2 Probability Distributions.mp4 116.8 MB
- Module 4 Supervised Learning Fundamentals/011. Lesson 4.11 Logistic Regression Basics.mp4 111.5 MB
- Module 3 Statistical Concepts of Machine Learning/003. Lesson 3.3 Descriptive Statistics Basics for Machine Learning - Measures of Central Tendency.mp4 108.8 MB
- Module 5 Unsupervised Learning Fundamentals/001. Lesson 5.1 Unsupervised Machine Learning.mp4 106.8 MB
- Module 6 Machine Learning Applications/002. Lesson 6.2 Text Representation Methods - Part 1 Bag-of-Words & N-Grams.mp4 104.5 MB
- Module 2 Mathematical Concepts of Machine Learning/004. Lesson 2.4 Matrices Decomposition.mp4 104.3 MB
- Module 4 Supervised Learning Fundamentals/003. Lesson 4.3 Intro to Regression and Regression Metrics.mp4 99.4 MB
- Module 1 Getting Started with Machine Learning/002. Lesson 1.2 Artificial Intelligence Principles.mp4 90.3 MB
- Module 1 Getting Started with Machine Learning/001. Lesson 1.1 Biggest Milestones in AI History.mp4 77.1 MB
- Module 6 Machine Learning Applications/004. Lesson 6.4 Text Representation Methods - Part 3 Word Embeddings.mp4 76.9 MB
- Module 4 Supervised Learning Fundamentals/005. Lesson 4.5 Linear Regression Basics - Multivariate Regression.mp4 69.7 MB
- Introduction/001. Course Overview.mp4 69.4 MB
- Module 5 Unsupervised Learning Fundamentals/002. Lesson 5.2 Clustering Basics.mp4 62.0 MB
- Module 4 Supervised Learning Fundamentals/008. Lesson 4.8 Intro to Classification.mp4 56.7 MB
- Module 4 Supervised Learning Fundamentals/002. Lesson 4.2 Introduction to Feature Engineering.en.srt 48.2 KB
- Module 4 Supervised Learning Fundamentals/013. Lesson 4.13 Supervised Learning in Python.en.srt 47.0 KB
- Module 4 Supervised Learning Fundamentals/006. Lesson 4.6 Linear Regression in Python - Simple and Multivariate Regression.en.srt 40.5 KB
- Module 4 Supervised Learning Fundamentals/014. Lesson 4.14 Introduction to Hyperparameter Optimization.en.srt 40.2 KB
- Module 2 Mathematical Concepts of Machine Learning/001. Lesson 2.1 Introduction to the Mathematical Principles of Machine Learning.en.srt 39.1 KB
- Module 4 Supervised Learning Fundamentals/001. Lesson 4.1 Supervision in Machine Learning.en.srt 38.8 KB
- Module 2 Mathematical Concepts of Machine Learning/003. Lesson 2.3 Matrices Basics.en.srt 38.0 KB
- Module 4 Supervised Learning Fundamentals/012. Lesson 4.12 Decision Trees Basics.en.srt 36.7 KB
- Module 4 Supervised Learning Fundamentals/010. Lesson 4.10 Class Imbalance Problems.en.srt 35.4 KB
- Module 2 Mathematical Concepts of Machine Learning/002. Lesson 2.2 Vectors.en.srt 35.1 KB
- Module 3 Statistical Concepts of Machine Learning/005. Lesson 3.5 Statistical Inference and Modeling Basics.en.srt 35.1 KB
- Module 4 Supervised Learning Fundamentals/009. Lesson 4.9 Classification Metrics.en.srt 33.5 KB
- Module 5 Unsupervised Learning Fundamentals/005. Lesson 5.5 Unsupervised Learning in Python.en.srt 32.8 KB
- Module 5 Unsupervised Learning Fundamentals/003. Lesson 5.3 k-Means Clustering.en.srt 32.5 KB
- Module 4 Supervised Learning Fundamentals/007. Lesson 4.7 Linear Regression in Python - Testing the Assumptions.en.srt 31.6 KB
- Module 5 Unsupervised Learning Fundamentals/004. Lesson 5.4 Principal Component Analysis (PCA) Basics.en.srt 31.0 KB
- Module 5 Unsupervised Learning Fundamentals/001. Lesson 5.1 Unsupervised Machine Learning.en.srt 28.4 KB
- Module 3 Statistical Concepts of Machine Learning/001. Lesson 3.1 Introduction to the Statistical Principles of Machine Learning.en.srt 27.3 KB
- Module 6 Machine Learning Applications/001. Lesson 6.1 Introduction to Natural Language Processing (NLP).en.srt 26.8 KB
- Module 6 Machine Learning Applications/003. Lesson 6.3 Text Representation Methods - Part 2 TF-IDF Method.en.srt 26.0 KB
- Module 6 Machine Learning Applications/002. Lesson 6.2 Text Representation Methods - Part 1 Bag-of-Words & N-Grams.en.srt 25.9 KB
- Module 4 Supervised Learning Fundamentals/004. Lesson 4.4 Linear Regression Basics - Closed-form solution.en.srt 25.3 KB
- Module 4 Supervised Learning Fundamentals/011. Lesson 4.11 Logistic Regression Basics.en.srt 25.0 KB
- Module 3 Statistical Concepts of Machine Learning/003. Lesson 3.3 Descriptive Statistics Basics for Machine Learning - Measures of Central Tendency.en.srt 24.8 KB
- Module 3 Statistical Concepts of Machine Learning/002. Lesson 3.2 Probability Distributions.en.srt 24.7 KB
- Module 1 Getting Started with Machine Learning/002. Lesson 1.2 Artificial Intelligence Principles.en.srt 24.5 KB
- Module 3 Statistical Concepts of Machine Learning/004. Lesson 3.4 Descriptive Statistics Basics for Machine Learning - Measures of Dispersion.en.srt 22.5 KB
- Module 4 Supervised Learning Fundamentals/003. Lesson 4.3 Intro to Regression and Regression Metrics.en.srt 21.2 KB
- Module 2 Mathematical Concepts of Machine Learning/004. Lesson 2.4 Matrices Decomposition.en.srt 21.1 KB
- Module 6 Machine Learning Applications/004. Lesson 6.4 Text Representation Methods - Part 3 Word Embeddings.en.srt 18.2 KB
- Module 4 Supervised Learning Fundamentals/005. Lesson 4.5 Linear Regression Basics - Multivariate Regression.en.srt 17.9 KB
- Module 5 Unsupervised Learning Fundamentals/002. Lesson 5.2 Clustering Basics.en.srt 15.3 KB
- Module 4 Supervised Learning Fundamentals/008. Lesson 4.8 Intro to Classification.en.srt 14.9 KB
- Module 1 Getting Started with Machine Learning/001. Lesson 1.1 Biggest Milestones in AI History.en.srt 12.2 KB
- Introduction/001. Course Overview.en.srt 10.1 KB
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