construire modele machine_Learning.pptx

presentation des etapes de construire un modele de machine learning

1. Building a Machine Learning Mode l presented by : Youssef Asclou Année Universitaire:2023/2024

2. what is a model? 02 Steps to build a ml model 03 Machine Learning 01 TABLE OF CONTENTS conclusion 04

3. M achine learning 01

4. Machine learning is the process of teaching computers to learn from data and improve their performance without explicit programming. It's like training a computer to recognize patterns and make decisions based on those patterns. DEFINITION:

5. What is a model 02

6. model A model is a mathematical representation or algorithm that is trained on data to make predictions or decisions. It's essentially a computational framework that captures patterns and relationships within the data to perform a specific task, such as classification, regression, or clustering. Once trained, the model can be used to make predictions or decisions on new, unseen data.

7. Steps to build a ml model 03 Steps to build a ml model

8. Problem Definition Determine the objectives and success criteria to evaluate the model's performance. Identify the problem you want to solve. For example, is it a classification task (predicting categories), regression task (predicting numerical values), clustering task (identifying similarities between data), etc.

9. Data Collection Gather relevant data to solve the defined problem from the previous step. Ensure that the data is complete, high-quality, and representative of your domain. Utilize various sources such as public databases, APIs, etc. Example : Google Dataset Search, Kaggle , GitHub…

10. Exploratory Data Analysis (EDA): Analyze the data to understand its structure, distribution, relationships between variables, etc. Use visualization techniques such as histograms, scatter plots, correlation matrices, etc. Identify outliers, missing values, and other data quality issues.

11. 4. Data Preprocessing: Clean the data by removing outliers, handling missing values (by imputation or deletion), detecting and removing duplicates, etc. Normalize features to scale all variables to the same range. Encode categorical variables using techniques such as one-hot encoding or ordinal encoding

12. Model Selection: Choose the most appropriate type of machine learning model on the problem and available data. Explore different options such as linear models, decision trees, SVMs, neural networks, etc. Consider the strengths and weaknesses of each model and select the one that best suits your problem.

13. Data Division: Ensure that the data is split randomly to avoid bias. Typically, split the data into proportions such as 70% for training, 15% for validation, and 15% for testing. Divide your data into training, validation, and test sets.

14. Train your model on the training set using a specific learning algorithm. Adjust the model parameters to minimize a loss function or maximize a defined score function based on your problem (e.g., mean squared error for a regression task, accuracy for a classification task, etc.).. 7. Model Training

15. Evaluate the performance of your model on the validation set by adjusting the model's hyperparameters Use techniques such as cross-validation or grid search to find the best hyperparameters . Validation and hyperparameter tuning Avoid overfitting by monitoring the model's performance on the validation set and adjusting the hyperparameters accordingly.

16. Model evaluation : Use appropriate evaluation metrics for your problem (accuracy, recall, F1-score for classification, mean squared error for regression, etc.). Once you are satisfied with the model performance on the validation set, evaluate it on the test set to obtain an unbiased estimation of its real-world performance.

17. Interpreting the results: Draw conclusions on the relevance of your approach to solving the initial problem and propose recommendations for potential improvements. Examine feature weights to understand which variables are most important for the model predictions. Analyze the performance of your model and identify its strengths and weaknesses.

18. Integrate the model into existing data pipelines or develop an API to enable its use by other systems. Model deployment: If your model meets the requirements, deploy it in a production environment where it can be used to make real-time decisions.

19. Conclusion 04

20. Building a machine learning model involves essential steps, from defining the problem to deployment. Gathering relevant data, selecting the model, and tuning hyperparameters help create robust models. Evaluation and interpretation of results guide continuous improvement, while deployment provides concrete solutions for real-time decision-making.

21. Thank You