NURCER/Machine_Learning_and_Deep_Learning

Getting started with Machine Learning and Deep Learning

Module 1 - Python Programming

1. Intro to Python
2. Data Structures in Python (List, Tuple, Set, Dictionary)
3. Control Statements (Decision and Loops)
4. Functions and Modules

Module 2 - Python for Data Science

5. Numpy
6. Pandas
7. Missing Value Treatment
8. Exploratory Data Analysis (Matplotlib, Seaborn and Plotly)

Module 3 - Machine Learning

9. K - Nearest Neighbours
10. Linear Regression
11. Logistic Regression
12. Gradient Descent
13. Decision Trees
14. Support Vector Machines
15. K - Means
16. Principal component Analysis
17. Hyperparameter Tuning

Module 4 - Case Studies

• Car Price Prediction (Regression)
• Airline Sentiment Analysis (NLP - Classification)
• [Spam Detection (NLP - Classification)]
• Adult Income Prediction (Classification)
• Web App Development + Serialization and Deserialization -> To check live website Click Here
• Streamlit Heroku Deployment
• Deployment -> To learn more Click Here
• **NOTE - Above two website links are down for some time

Module 5 - Deep Learning

• Introduction to Deep Learning
• Training a Deep Neural Network + TensorFlow.Keras
• Convolutional Neural Network + TensorFlow.Keras
• Recurrent Neural Network (Coming Soon)

Module 6 - Statistics for Data Science

• Normal Distribution
• Central Limit Theorem
• Hypothesis Testing
• Chi Square Testing