Engaging Online Course Creator

Course Outline: I. Introduction to Machine Learning What is Machine Learning? Types of Machine Learning (Supervised, Unsupervised, Reinforcement) Applications of Machine Learning Examples of Machine Learning in Real-world scenarios II. Data Preprocessing and Exploration Data Collection and Preparation Data Cleaning Data Visualization and Exploration Feature Selection and Engineering III. Model Selection and Training Supervised Learning Algorithms (Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM) Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction) Model Selection and Cross-Validation Hyperparameter Tuning IV. Model Evaluation and Optimization Performance Metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC) Overfitting and Underfitting Regularization Techniques (L1, L2, Dropout) Ensemble Learning (Bagging, Boosting) V. Deep Learning and Neural Networks Introduction to Neural Networks Building Blocks of Neural Networks (Activation Functions, Layers, Optimizers) Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Transfer Learning Activities: Activity 1: Build a Machine Learning model for a classification task using scikit-learn. In this activity, students will be provided with a dataset and will be required to build a classification model using scikit-learn. They will be asked to preprocess the data, split it into training and testing sets, select an appropriate algorithm, train the model, and evaluate its performance using appropriate metrics. Activity 2: Perform data preprocessing and exploration on a real-world dataset. In this activity, students will be provided with a real-world dataset and will be required to perform data preprocessing and exploration on it. They will be asked to clean the data, visualize and explore it, and perform feature selection and engineering. They will also be required to write a report on their findings and present it to the class. Activity 3: Implement a deep neural network for image classification using TensorFlow. In this activity, students will be required to implement a deep neural network for image classification using TensorFlow. They will be provided with a dataset of images and will be asked to preprocess the data, build a CNN model, train the model, and evaluate its performance using appropriate metrics. They will also be required to write a report on their findings and present it to the class. Conclusion: By the end of this course, students will have gained a strong understanding of the fundamentals of Machine Learning and be able to apply them in real-world scenarios. They will have hands-on experience with building and training machine learning models, performing data preprocessing and exploration, and implementing deep neural networks for image classification.