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Manufacturing and Supply Chain: ML is used to optimise production processes, predict maintenance
needs, and streamline supply chain management. Predictive maintenance systems can foresee equipment
failures, reducing downtime and maintenance costs.
Gaming: In the gaming industry, ML is used to develop AI that can learn and adapt to player behaviour,
making games more challenging and engaging. For instance, reinforcement learning is used in video
games like AlphaGo to improve game strategies.
7 Basic Steps in Machine Learning
Different steps in Machine Learning are as follows:
1. Data Collection: Acquire relevant data from various sources, ensuring it is sufficient and appropriate for
the problem being addressed.
2. Data Preparation: Process and clean the collected data by handling missing values, outliers, and
inconsistencies, and format it to ensure it is ready for model training.
3. Model Selection: Select the most suitable machine learning algorithm or model based on the task (e.g.,
classification, regression, clustering) and the data's characteristics.
4. Model Training: Train the chosen model using the prepared data, enabling it to identify and learn patterns
and relationships.
5. Model Evaluation: Evaluate the model’s performance using relevant metrics like accuracy, precision, recall,
or F1-score to determine how well it performs.
6. Parameter Tuning: Fine-tune the model by adjusting hyperparameters to optimise its performance and
achieve better results.
7. Prediction: Apply the trained and optimised model to make predictions or decisions on new, unseen
data.
Prediction
Parameter Tuning
Model Evaluation
Model Training
Model Selection
Data Preparation
Data Collection
Emerging Trends 163

