Artificial Intelligence & Machine Learning Essential - AMLE
Description
A thorough foundation in artificial intelligence and machine learning, including important ideas and techniques, is provided by AI and ML Essentials (AMLE). This entails comprehending neural networks, supervised and unsupervised learning, and real-world uses like computer vision and natural language processing. Being proficient in AMLE is essential for creating intelligent systems and using datadriven insights in a variety of fields.
Course Curriculum
- Understanding AI and ML
- Definition and Scope of Artificial Intelligence (AI) and Machine Learning (ML)
- Historical developments and real-world applications
- Distinction between AI and ML
- Basic Concepts in Machine Learning
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
- Feature engineering and data preprocessing
- Model training, evaluation, and prediction
- Introduction to Neural Networks
- Basics of neural networks
- Layers, neurons, and activation functions
- Training neural networks with backpropagation
- Data Collection and Cleaning
- Gathering and cleaning data for ML projects
- Handling missing values and outliers
- Data quality and validation
- Exploratory Data Analysis (EDA)
- Visualizing data distributions and patterns
- Correlation analysis
- Feature selection and dimensionality reduction
- Project: Data Preparation and EDA
- Applying Data Preparation and EDA techniques to a real-world dataset
- Regression
- Understanding Regression Analysis
- Linear and non-linear regression models
- Evaluation metrics for regression
- Classification
- Basics of classification problems
- Popular Classification Algorithms (e.g., Decision Trees, SVM)
- Model evaluation and metrics for classification
- Project: Building a Supervised Learning Model
- Implementing a supervised learning model on a provided dataset
- Clustering
- Introduction to Unsupervised Learning
- Types of Clustering Algorithms (e.g., K-Means, Hierarchical)
- Applications of Clustering
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Use cases for dimensionality reduction
- Project: Unsupervised Learning Project
- Applying clustering and dimensionality reduction on a real-world dataset
- Introduction to Deep Learning
- Deep neural networks and architectures
- Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
- Transfer learning
- Practical Applications of AI and ML
- Use cases and applications in various industries
- Ethical considerations and responsible AI
- Future Trends in AI and ML
- Capstone Project: Real-world AI and ML Application
- Developing a comprehensive AI or ML project, integrating various concepts learned throughout the course