**Requirements**

- Basic mathematical concepts of addition, multiplication and so on
- Knowing python beforehand would be handful

**Description**

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]]**

- Seasonal and Beginners Python developers who want to learn about different AI and ML algorithms
- Students who want to learn all the mathematics behind popular regression and classification models
- Students who want to learn to implement data science libraries to solve real world Machine Learning problems

- Understand and implement a Decision Tree in Python
- Understand about Gini and Information Gain algorithm
- Solve mathematical numerical related decision trees
- Learn about regression trees
- Learn about simple, multiple, polynomial and multivariate regression
- Learn about Ordinary Least Squares Algorithms
- Solve numerical related to Ordinary Least Squares algorithm
- Learn to create real world predictions and classification projects
- Learn about Gradient Descent
- Learn about Logistic Regression and hyper parameters