Machine Learning and AI ML Concepts, TensorFlow, PyTorch workshop 1 day

Workshop: Machine Learning and AI

Notes:

• This one-day workshop provides an introduction to machine learning and artificial intelligence, with a focus on TensorFlow and PyTorch.
• It includes lectures, demonstrations, and hands-on coding exercises.
• Participants should have access to their own laptops for hands-on exercises.
• Encourage participants to ask questions throughout the workshop.
• Provide additional resources and references for participants to continue their machine learning and AI learning journey after the workshop.
• Schedule breaks to keep participants refreshed and engaged.

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Machine Learning and AI

1 / 20

• AI Ethics: Discuss ethical considerations in AI, including transparency and fairness.

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2 / 20

• Bias in AI: Explore and mitigate bias in AI models and datasets.

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3 / 20

• Model Deployment: Deploy a trained ML model as a RESTful API for predictions.

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4 / 20

• Hyperparameter Tuning: Optimize hyperparameters of ML models using techniques like grid search or random search.

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5 / 20

• Sentiment Analysis: Build a sentiment analysis model for analyzing text sentiment.

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6 / 20

• Dimensionality Reduction: Apply dimensionality reduction techniques (e.g., PCA) to simplify data.

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7 / 20

• Neural Network Basics: Create a simple feedforward neural network for a specific task.

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8 / 20

• Convolutional Neural Networks (CNNs): Build a CNN for image classification.

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9 / 20

• Recurrent Neural Networks (RNNs): Implement an RNN for sequence data (e.g., text).

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10 / 20

• Text Preprocessing: Preprocess text data for NLP tasks (e.g., tokenization, stop-word removal).

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11 / 20

• Data Scaling: Scale and normalize data for use in ML models.

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12 / 20

• Linear Regression: Implement and evaluate a simple linear regression model.

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• Classification: Build a classification model (e.g., logistic regression, decision tree) for a given dataset.

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14 / 20

• Model Evaluation: Use metrics like accuracy, precision, and recall to evaluate classification models.

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15 / 20

• Clustering: Perform clustering using algorithms like K-Means or DBSCAN.

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16 / 20

• Understanding ML and AI: Explain the concepts of Machine Learning and Artificial Intelligence and their applications.

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17 / 20

• Types of ML: Differentiate between supervised, unsupervised, and reinforcement learning.

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• AI vs. ML vs. Deep Learning: Compare and contrast AI, ML, and Deep Learning.

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19 / 20

• Data Cleaning: Preprocess a dataset by handling missing values and outliers.

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20 / 20

• Feature Selection: Apply feature selection techniques to choose relevant features for modeling.

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