Machine Learning

In this course, students will delve into the world of machine learning, starting with data exploration and visualization, moving through various regression and classification techniques, and culminating with advanced methods like neural networks and transfer learning. The course is designed to offer a blend of theoretical knowledge and hands-on practice, enabling students to apply machine learning techniques to real-world problems effectively.

Course Outline:

• Data Exploration and Visualization:
  • Understanding data types and structures
  • Data cleaning and preprocessing
  • Using visualization tools to understand data distributions and patterns
• Gradient Descent
  • Introduction to optimization
  • Gradient descent algorithm
  • Variants of gradient descent (batch, stochastic, mini-batch)
• Linear & Polynomial Regression
  • Simple linear regression
  • Multiple linear regression
  • Polynomial regression and model complexity
• Train Test Split, K-Fold Cross-Validation, and Evaluation Metrics:
  • Importance of data splitting
  • Normalization
  • Train-test split methodology
  • K-Fold cross-validation
  • Evaluation metrics (accuracy, precision, recall, F1 score, confusion matrix)
• Logistic Regression:
  • Binary classification
  • Logistic function and hypothesis
  • Decision boundary and model evaluation
  • 1vs1, 1vsAll
• Naive Bayes
  • Probabilistic classification
  • Bayes’ theorem
  • Types of Naive Bayes classifiers (Gaussian, Multinomial, Bernoulli)
• Support Vector Machines (SVM):
  • introduction to SVM
  • Concept of margin and support vectors
  • Kernel trick and types of kernels (linear, polynomial, RBF)
• K-Nearest Neighbors (KNN):
  • Distance metrics (Euclidean, Manhattan)
• Multilayer Perceptron (MLP):
  • Architecture of MLP
• Basics of neural networks
  • Input, Hidden, Output Layers
  • Dropouts and regularizer
  • Backpropagation and activation functions
  • Loss Functions
• Convolutional Neural Networks (CNN)
  • Conv Layers
  • Padding
  • Stride
  • Filters
  • Pooling
• Transfer Learning
  • Concept of transfer learning
  • Pre-trained models and their applications
  • Fine-tuning and feature extraction

The Machine Learning Project course offers a robust foundation in machine learning, covering essential algorithms and techniques. By the end of the course, students will have gained the skills to tackle complex machine learning problems, evaluate their models, and apply advanced methods like transfer learning to enhance their projects. This course prepares students for further studies in machine learning and practical applications in various industries.