- Intro to Python
- Data Structures in Python (List, Tuple, Set, Dictionary)
- Control Statements (Decision and Loops)
- Functions and Modules
- K - Nearest Neighbours
- Linear Regression
- Logistic Regression
- Gradient Descent
- Decision Trees
- Support Vector Machines
- K - Means
- Principal component Analysis
- 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
- Deployment -> To learn more Click Here
- **NOTE - Above two website links are down for some time
- Introduction to Deep Learning
- Training a Deep Neural Network + TensorFlow.Keras
- Convolutional Neural Network + TensorFlow.Keras
- Recurrent Neural Network (Coming Soon)
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Task - 1 -> Make a Web Portfolio
- Use HTML and Bootstrap for frontend.
- Use Flask for backend.
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Task - 2 -> AMCAT Data Analysis
- Data Set Click Here
- Data Set Description Click Here
- Task Description - Analyse the data using pandas and come up with 5 observations.
- P.S - Use whatever we have already covered in the class. Also look into more pandas topic like pivot tables and cross tab for analysis.
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Task - 3 -> 10 Seaborn Plots
- Read the Seaborn Documentation
- Create 10 plots which we have not covered in the class.
- Work on same AMCAT data.