krishnakaushik25 / 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

1. Numpy
2. Pandas
3. Missing Value Treatment
4. Exploratory Data Analysis (Matplotlib, Seaborn and Plotly)

Module 3 - Statistics for Data Science

1. Normal Distribution
2. Central Limit Theorem
3. Hypothesis Testing
4. Chi Square Testing

Module 4 - Machine Learning

1. K - Nearest Neighbours
2. Linear Regression
3. Logistic Regression
4. Gradient Descent
5. Decision Trees
6. Support Vector Machines
7. K - Means
8. Principal component Analysis
9. Hyperparameter Tuning

Module 5 - 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 6 - Deep Learning

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