Data Science and Machine Learning Fundamentals [Theory Only]

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Theorical Course for Data Science, Machine Learning, Deep Learning to understand the logic of Data Science algorithms

1 - Introduction to Machine Learning

1 - What is Machine Learning

2 - What are Machine Learning Terminologies

2 - Evaluation Metrics in Machine Learning

3 - Classification vs Regression in Machine Learning

4 - Evaluating Performance Classification Error Metrics

5 - Evaluating Performance Regression Error Metrics

3 - Supervised Learning with Machine Learning

6 - What is Supervised Learning in Machine Learning

4 - Supervised Learning Algorithms

7 - Linear Regression Algorithm Theory

8 - What is Bias Variance TradeOff

9 - Logistic Regression Algorithm Theory

10 - KFold CrossValidation Theory

11 - Hyperparameter Optimization Theory

12 - K Nearest Neighbors Algorithm Theory

13 - Decision Tree Algorithm Theory

14 - Random Forest Algorithm Theory

15 - Support Vector Machine Algorithm Theory

5 - Unsupervised Learning with Machine Learning

16 - What is unsupervised Learning in Machine Learning

6 - Unsupervised Learning Algorithms

17 - K Means Clustering Algorithm Theory

18 - Hierarchical Clustering Algorithm Theory

19 - Principal Component Analysis PCA Theory

20 - What is the Recommender System Part 1

21 - What is the Recommender System Part 2

7 - Extra

22 - Data Science and Machine Learning Fundamentals Theory Only.html

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تولید کننده: Udemy-Training