Course content
1. Introduction to Machine Learning: Gain a solid understanding of the fundamentals of machine learning, including supervised and unsupervised learning techniques, regression, classification, clustering, and dimensionality reduction. Explore the mathematical foundations behind machine learning algorithms and learn how to apply them to real-world datasets.
2. Data Preprocessing and Feature Engineering: Learn the essential techniques for preprocessing raw data and engineering features to improve the performance of machine learning models. Dive into data cleaning, normalization, feature scaling, and transformation methods, ensuring the data is ready for model training and evaluation.
3. Supervised Learning Algorithms: Explore a variety of supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and ensemble methods. Understand how to select the appropriate algorithm for different types of tasks and evaluate model performance using cross-validation techniques.
4. Unsupervised Learning Algorithms: Delve into unsupervised learning methods such as clustering and dimensionality reduction. Learn how to apply algorithms like K-means clustering, hierarchical clustering, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) to discover patterns and structure within data.
5. Deep Learning and Neural Networks: Discover the principles of deep learning and neural networks, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Explore advanced topics such as transfer learning, regularization, and optimization techniques.
6. Natural Language Processing (NLP) and Text Mining: Explore techniques for processing and analyzing textual data, including tokenization, stemming, lemmatization, and sentiment analysis. Learn how to build and train NLP models for tasks such as text classification, named entity recognition, and text generation.
7. Hands-on Projects and Case Studies: Apply your knowledge and skills to real-world projects and case studies, tackling challenges across various domains such as healthcare, finance, e-commerce, and social media. Work on industry-relevant projects under the guidance of experienced instructors and mentors.
8. Capstone Project: Demonstrate your proficiency in machine learning by completing a capstone project that showcases your ability to solve a complex problem using machine learning techniques. Apply all the concepts and skills learned throughout the program to address a real-world challenge and present your findings to peers and industry experts.
9. Career Preparation and Support: Receive career guidance and support throughout the program, including resume building, interview preparation, and networking opportunities. Gain access to industry events, job fairs, and internship opportunities to jumpstart your career in machine learning.