Introduction to Machine Learning and AI
- Overview of Machine Learning and AI: Get an understanding of what machine learning and AI are all about.
- Historical Context and Evolution: Dive into the origins and growth of these fields.
- Key Terminologies and Concepts: Familiarize yourself with the essential vocabulary and ideas.
Supervised Learning
- Linear Regression and Classification: Explore the basics of making predictions and categorizing data.
- Decision Trees and Random Forests: Learn how these models split data into more understandable chunks.
- Support Vector Machines (SVM): Understand how SVMs separate data with maximum margin.
- Model Evaluation and Performance Metrics: Assess the accuracy and effectiveness of your models.
Unsupervised Learning
- Clustering Algorithms (K-means, Hierarchical): Group data points without predefined labels.
- Dimensionality Reduction Techniques (PCA, LDA): Simplify data while retaining its essential characteristics.
- Anomaly Detection: Spot unusual data points that could indicate important trends or errors.
Neural Networks and Deep Learning
- Introduction to Neural Networks: Discover how artificial neurons mimic the brain’s workings.
- Deep Learning Fundamentals: Learn the building blocks of deep learning and how they stack up.
- Convolutional Neural Networks (CNN): Get to grips with CNNs, especially for image processing.
- Recurrent Neural Networks (RNN): Explore how RNNs handle sequences and time-series data.
- Transfer Learning: Learn how to apply pre-trained models to new tasks.
Natural Language Processing (NLP)
- Text Preprocessing and Tokenization: Break down text data into manageable pieces.
- Sentiment Analysis: Analyze and determine the mood behind a piece of text.
- Topic Modeling: Uncover the hidden themes in a body of text.
- Sequence Models and LSTM: Work with Long Short-Term Memory networks to handle sequential data.
- Transformer Models and BERT: Dive into the latest advancements in NLP with transformers and BERT.
Practical Machine Learning
- Working with Python and Jupyter Notebooks: Set up your environment and start coding.
- Using TensorFlow and Keras for Model Building: Build and train models using powerful libraries.
- Implementing PyTorch for Advanced Deep Learning: Tackle more complex models with PyTorch.
- Utilizing scikit-learn for Machine Learning Algorithms: Apply various machine learning techniques using scikit-learn.
Participant’s Eligibility
- Senior undergraduates and graduate students in Computer Science and related fields.
- Professionals in IT, data science, and software development who are eager to enhance their AI expertise.
Course Outcomes
- Solid Foundation: Gain a thorough understanding of machine learning and AI basics.
- Proficiency in Learning Techniques: Become skilled in both supervised and unsupervised learning methods.
- Hands-On Experience: Get practical experience with neural networks and deep learning models.
- NLP Applications: Learn to implement real-world NLP applications.
- Master Key Tools: Gain expertise in TensorFlow, Keras, PyTorch, and scikit-learn.
- Real-World Projects: Complete projects that showcase your ability to apply what you’ve learned.
- Certificate of Completion: Earn a certificate recognized by industry leaders, bolstering your professional profile.