Understand the fundamentals of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
Explore the principles of feature engineering and feature selection for preparing input data for machine learning models.
Analyze the role of data preprocessing techniques such as normalization, standardization, and missing value imputation in improving model performance.
Understand the concepts of model evaluation, including performance metrics such as accuracy, precision, recall, and F1-score.
Explore the concepts of cross-validation and hyperparameter tuning for optimizing model performance and generalization.
Analyze the architecture of popular machine learning libraries and frameworks such as scikit-learn, TensorFlow, and PyTorch.
Understand the principles of linear regression and logistic regression for solving regression and classification problems, respectively.
Explore the concepts of decision trees, random forests, and gradient boosting for building ensemble learning models.
Analyze the principles of support vector machines (SVMs) for binary classification and kernel methods for nonlinear decision boundaries.
Understand the concepts of clustering algorithms such as k-means, hierarchical clustering, and density-based clustering for unsupervised learning tasks.
Explore the role of dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for visualizing and compressing high-dimensional data.
Analyze the principles of neural networks and deep learning architectures for solving complex machine learning problems.
Understand the concepts of convolutional neural networks (CNNs) for image classification and object detection tasks.
Explore the role of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks for sequence modeling and natural language processing tasks.
Analyze the principles of generative adversarial networks (GANs) for generating synthetic data and images.
Understand the concepts of transfer learning and fine-tuning pre-trained models for domain adaptation and task-specific learning.
Explore the role of autoencoders and variational autoencoders (VAEs) for unsupervised feature learning and data generation.
Analyze the principles of reinforcement learning algorithms such as Q-learning and deep Q-networks (DQN) for learning optimal decision-making policies.