Module #1 Introduction to Machine Learning Overview of machine learning, its applications, and importance in automation
Module #2 Machine Learning Fundamentals Mathematical foundations, types of machine learning, and key concepts
Module #3 Data Preprocessing for Automation Importance of data preprocessing, data cleaning, feature scaling, and normalization
Module #4 Supervised Learning for Automation Introduction to supervised learning, regression, and classification
Module #5 Unsupervised Learning for Automation Introduction to unsupervised learning, clustering, and dimensionality reduction
Module #6 Reinforcement Learning for Automation Introduction to reinforcement learning, Markov decision processes, and Q-learning
Module #7 Automated Feature Engineering Techniques for automatic feature engineering, including feature extraction and selection
Module #8 Model Evaluation Metrics for Automation Metrics for evaluating machine learning models, including accuracy, precision, recall, and F1 score
Module #9 Hyperparameter Tuning for Automation Techniques for hyperparameter tuning, including grid search, random search, and Bayesian optimization
Module #10 Introduction to Deep Learning for Automation Introduction to deep learning, neural networks, and convolutional neural networks
Module #11 Deep Learning for Computer Vision in Automation Applications of deep learning in computer vision, including object detection and image classification
Module #12 Deep Learning for Natural Language Processing in Automation Applications of deep learning in natural language processing, including text classification and sentiment analysis
Module #13 Machine Learning for Robotics and Control Systems Applications of machine learning in robotics and control systems, including control algorithms and robotics vision
Module #14 Machine Learning for Predictive Maintenance Applications of machine learning in predictive maintenance, including anomaly detection and failure prediction
Module #15 Machine Learning for Quality Control Applications of machine learning in quality control, including defect detection and quality prediction
Module #16 Machine Learning for Supply Chain Optimization Applications of machine learning in supply chain optimization, including demand forecasting and inventory management
Module #17 Machine Learning for Automation in Industry 4.0 Applications of machine learning in Industry 4.0, including smart manufacturing and industrial IoT
Module #18 Case Studies in Machine Learning for Automation Real-world case studies of machine learning applications in automation
Module #19 Ethics and Explainability in Machine Learning for Automation Importance of ethics and explainability in machine learning for automation
Module #20 Deploying Machine Learning Models for Automation Best practices for deploying machine learning models in automation, including model serving and model monitoring
Module #21 Machine Learning for Automation in Python Hands-on experience with machine learning libraries in Python, including scikit-learn and TensorFlow
Module #22 Machine Learning for Automation in R Hands-on experience with machine learning libraries in R, including caret and dplyr
Module #23 Machine Learning for Automation with Big Data Handling big data in machine learning for automation, including Hadoop and Spark
Module #24 Machine Learning for Automation in Cloud and Edge Computing Deploying machine learning models in cloud and edge computing environments
Module #25 Machine Learning for Automation in Real-Time Systems Challenges and opportunities of machine learning in real-time systems for automation
Module #26 Cybersecurity for Machine Learning in Automation Importance of cybersecurity in machine learning for automation, including adversarial attacks and defense mechanisms
Module #27 Human-Machine Collaboration in Automation Role of human-machine collaboration in automation, including transparency, accountability, and trust
Module #28 Standards and Regulations for Machine Learning in Automation Standards and regulations for machine learning in automation, including explainability and transparency
Module #29 Future of Machine Learning in Automation Emerging trends and future directions of machine learning in automation
Module #30 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Automation career
