Ecole 42 Professional Training on Artificial Intelligence/ Machine Learning

About This Course

The objectives of the course is to leverage on Ecole 42 unique peer-learning model where students learn through collaboration and self-paced progression. By working collaboratively on projects and integrating diverse ideas from their peers, course participants will 'learn to learn' and develop more creative solutions through personal research, exchange with peers, trial and error.

At basic level, students will gain hands-on experience on Simple Linear Regress, and progress to implementing and interpreting multiple linear regression models using libraries like pandas for data manipulation and seaborn for visualization. Students will have the skills to apply multiple linear regression techniques to real-world datasets, enhancing their understanding of predictive analytics and data-driven decision-making.

At intermediate level, students will apply their knowledge of Logistic Regression to tackle a binary classification problem and independently apply their understanding of Multinomial Logistic Regression to a multiclass classification problem. Students will also explore various classification models, focusing on Decision Trees and Extreme Gradient Boosting (XGBoost).

At the advanced level, students will construct and train a neural network, evaluate a Convolutional Neural Network (CNN) for image classification and apply Recurrent Neural Networks (RNNs) to weather prediction using time series data.