Course Overview
The Machine Learning and AI Fundamentals course provides a thorough introduction to the essential principles of machine learning and artificial intelligence. Tailored for professionals in the AI field, this course covers key topics including supervised and unsupervised learning, neural networks, deep learning, and natural language processing. Through a combination of engaging video lectures, hands-on coding sessions, and practical real-world projects, participants will gain the skills and experience necessary to thrive in the fast-evolving AI industry.
Course Goals
The primary aim of this program is to equip AI professionals with the fundamental skills and knowledge in machine learning and AI, enabling them to drive innovation and excel in their careers.
Program Structure
Introduction to Machine Learning and AI
- Overview of Machine Learning and AI.
- Historical Context and Evolution.
- Key Terminologies and Concepts.
Supervised Learning
- Linear Regression and Classification.
- Decision Trees and Random Forests.
- Support Vector Machines (SVM).
- Model Evaluation and Performance Metrics.
Unsupervised Learning
- Clustering Algorithms (K-means, Hierarchical).
- Dimensionality Reduction Techniques (PCA, LDA).
- Anomaly Detection.
Neural Networks and Deep Learning
- Introduction to Neural Networks.
- Deep Learning Fundamentals.
- Convolutional Neural Networks (CNN).
- Recurrent Neural Networks (RNN).
- Transfer Learning.
Natural Language Processing (NLP)
- Text Preprocessing and Tokenization.
- Sentiment Analysis.
- Topic Modeling.
- Sequence Models and LSTM.
- Transformer Models and BERT.
Practical Machine Learning
- Working with Python and Jupyter Notebooks.
- Using TensorFlow and Keras for Model Building.
- Implementing Advanced Deep Learning with PyTorch.
- Utilizing scikit-learn for Machine Learning Algorithms.
Eligibility
- This course is ideal for senior undergraduates and graduate students in Computer Science or related fields.
- IT professionals, data scientists, and software developers aiming to enhance their AI expertise.
Learning Outcomes
- Build a solid foundation in machine learning and AI principles.
- Develop expertise in both supervised and unsupervised learning techniques.
- Gain hands-on experience with neural networks and deep learning models.
- Learn to implement applications in natural language processing.
- Master the use of essential machine learning libraries and frameworks, including TensorFlow, Keras, PyTorch, and scikit-learn.
- Complete real-world projects that showcase your ability to apply machine learning concepts effectively.
- Earn a certificate of completion that is recognized by industry leaders, boosting your professional credentials.