Machine Learning Notebooks

Datasets analyzed

• Fisher Iris
• Wine Data
• MNIST Digits
• CIFAR 10 and CIFAR-100
• Random datasets

Algorithms used

- KMeans
- Naive Bayes Classifer 
- Gaussian Mixture Model
- Scree Test
- Linear Regression
- Multiclass Support Vector Machine
- Neural Network
- Convolutional Neural Network

Frameworks used

- Keras with TensorFlow backend
- NumPy
- Matplotlib
- SciPy
- Scikit Learn

Capstone

- Convolutional Neural Network 
	vs 
		Support Vector Machine

on CIFAR-100 and CIFAR-10 datasets

Experiment Report:

Computer Vision: Implementing and studying performance of two different algorithms for Object Recognition on CIFAR-10 and CIFAR-100 datasets.

Outcome

- Deep understanding of Machine Learning and Deep Learning algorithms
- Data Analysis
- Data Visualization
- Linear Algebra
- Probability
- Sample generation