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• What is Machine Learning?
o Definition, types, and key concepts
o The role of data in machine learning
• Machine Learning in Business:
o Real-world applications of machine learning
o Identifying business problems that can be solved with AI
• Hands-on: Data Exploration and Preparation
• Regression Analysis:
o Linear regression, logistic regression
o Model evaluation and interpretation
• Classification Algorithms:
o Decision trees, random forests, support vector machines
o Model selection and hyperparameter tuning
• Hands-on: Building a Predictive Model
• Clustering Algorithms:
o K-means clustering, hierarchical clustering
o Anomaly detection
• Dimensionality Reduction:
o Principal Component Analysis (PCA)
o t-SNE
• Hands-on: Implementing Clustering and Dimensionality Reduction
• Neural Networks:
o Perceptrons and feedforward neural networks
o Backpropagation and gradient descent
• Convolutional Neural Networks (CNNs):
o Image classification and object detection
• Recurrent Neural Networks (RNNs):
o Natural language processing and time series analysis
• Hands-on: Building a Deep Learning Model
• Ethical Considerations in AI:
o Bias and fairness in AI algorithms
o Privacy and security concerns
o Responsible AI development
• Deploying Machine Learning Models:
o Model deployment strategies
o MLOps and model monitoring
• The Future of AI:
o Emerging trends and technologies
o The impact of AI on society and the workforce