Professional Certificate in AI Fundamentals: Fundamentals Development

• Introduction to AI and Machine Learning: Understanding the basics of AI, Machine Learning, and Deep Learning. Exploring use cases, benefits, and challenges.
• Mathematics for AI: Linear algebra, calculus, probability, and statistics fundamentals required for AI development.
• Python Programming: Learning Python syntax, data structures, functions, and libraries important for AI development, such as NumPy, Pandas, and Matplotlib.
• Data Preprocessing: Data cleaning, normalization, transformation, and feature engineering techniques.
• Supervised Learning: Regression, decision trees, random forests, support vector machines, and ensemble methods.
• Unsupervised Learning: Clustering, dimensionality reduction, and anomaly detection techniques.
• Neural Networks: Introduction to artificial neural networks, perceptrons, and multilayer perceptrons.
• Convolutional Neural Networks (CNNs): Understanding CNN architecture, components, and applications in image and video processing.
• Recurrent Neural Networks (RNNs): Learning RNN fundamentals, long short-term memory (LSTM), and gated recurrent units (GRUs) and their applications in sequence data.
• Natural Language Processing (NLP): Text preprocessing, sentiment analysis, topic modeling, and sequence-to-sequence models.