Machine Learning - Regression and Classification (math Inc.)
- Description
- Curriculum
- FAQ
- Reviews
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are ‘trained’ to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.
Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.
Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.
Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.
Topics covered in this course:
1. Lecture on Information Gain and GINI impurity [decision trees]
2. Numerical problem related to Decision Tree will be solved in tutorial sessions
3. Implementing Decision Tree Classifier in workshop session [coding]
4. Regression Trees
5. Implement Decision Tree Regressor
6. Simple Linear Regression
7. Tutorial on cost function and numerical implementing Ordinary Least Squares Algorithm
8. Multiple Linear Regression
9. Polynomial Linear Regression
10. Implement Simple, Multiple, Polynomial Linear Regression [[coding session]]
11. Write code of Multivariate Linear Regression from Scratch
12. Learn about gradient Descent algorithm
13. Lecture on Logistic Regression [[decision boundary, cost function, gradient descent…..]]
14. Implement Logistic Regression [[coding session]]
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6
Key terms in Machine Learning [supervised, unsupervised,, classification..] -
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Data Types in Machine Learning
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8
Structured Data sets used in Machine Learning
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9
Data Preprocessing Part 1
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10
Data Preprocessing Part 2
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11
Data Preprocessing Part 3
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12
Introduction to numpy module
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13
Introduction to pandas module
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14
Encoding Process in Machine Learning
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15
Train and Test Splitting of Data
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16
Key Terms used in Machine Learning [dimensionality, underfitting, overfitting]
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17
Lecture: Learn about Information Gain algorithm
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18
Lecture: Decision Tree Classifier, Which split is better?
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19
Tutorial: Implement decision tree numerical using Information Gain
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Tutorial continue: Implement decision Tree numerical
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21
Lecture: Learn about GINI impurity algorithm
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22
Workshop: Code Decision Tree Classifier
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Workshop continue: Code Decision Tree Classifier
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Coding: Implement DTC [Decision Tree Classifier]
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25
Lecture: Learn about regression Trees
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26
Lecture: Learn about creation of Regression Trees
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27
Lecture: Continue to built Regression Tree [Sum of Squared Residuals(Variance) ]
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28
Lecture: Find optimal decision using Regression Tree
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29
Workshop: Code Decision Tree Regressor
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30
Lecture: Intro to Linear Regression
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31
Lecture: Learn about OLS [Ordinary Least Squares] algorithm
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32
Lecture: Introduction to working of Linear Regression
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33
Lecture: Introduction to MSE, MAE, RMSE
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34
Lecture: Introduction to R squared
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35
Tutorial: Implement Simple linear regression numerical [calculate best fit line]
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36
Workshop: Implement Simple Linear Regression
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37
Lecture: Difference between Simple and Multiple Regression
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38
Workshop: Implement Multiple Linear Regression
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39
Workshop: Implement Multiple Linear Regression
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40
Workshop: Implement Polynomial Regression
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41
Workshop continue: Implement Polynomial Regression
