Flex AI & Machine Learning Microtrack

This module provides an overview of machine learning (ML) and artificial intelligence (AI) concepts. It covers the role of ML and AI in various industries and introduces popular ML algorithms and their applications. The module also discusses the importance of data preprocessing and exploration in preparing data for ML models.

In this module, the focus is on data cleaning techniques such as handling missing values and detecting outliers. It also covers exploratory data analysis (EDA) and feature engineering, which involve analyzing and visualizing the data to gain insights and creating new features to improve model performance.

Supervised learning algorithms are introduced in this module, including linear regression and logistic regression for regression and classification tasks respectively. The module covers decision trees and random forests as well as evaluation metrics used to assess the performance of regression and classification models.

This module explores unsupervised learning techniques, starting with clustering algorithms such as k-means and hierarchical clustering for grouping data based on similarities. It also covers dimensionality reduction techniques like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for reducing the number of features in high-dimensional data. Evaluation methods for clustering algorithms are also discussed.

The fundamentals of deep learning are covered in this module. It introduces neural networks and explains the process of building and training them using popular frameworks such as TensorFlow or PyTorch. The module also explores convolutional neural networks (CNNs) specifically designed for image classification tasks.

This module focuses on techniques for evaluating and fine-tuning ML models. It covers cross-validation methods for robust model evaluation, hyperparameter tuning, and regularization techniques to optimize model performance. Strategies for handling overfitting and underfitting issues are also discussed.

This module delves into real-world applications of ML and AI across various industries. It explores case studies in fields like healthcare and finance, showcasing how ML is utilized to solve complex problems. The module encourages identifying opportunities for ML integration in different domains.

Ethical considerations play a vital role in AI and ML development. This module addresses topics such as bias and fairness in ML models, privacy and security considerations, and responsible AI practices. It emphasizes the importance of developing ML models that are unbiased, secure, and adhere to ethical standards.

ML models need to be integrated into existing workflows and systems for practical use. This module explores methods for integrating ML models, including utilizing cloud services and APIs for ML deployment. It also covers model deployment considerations and best practices.

The final module focuses on applying ML techniques to a real-world problem through a capstone project. It emphasizes building a portfolio showcasing ML projects to demonstrate skills and expertise. Additionally, the module explores various career opportunities in AI and ML, providing insights into potential paths and roles in the field.