Absolutely! Here’s the AI/ML training roadmap using Scikit-learn in paragraph form with clear headings for each week:
AI/ML Training Roadmap – Scikit-learn (12 Weeks)
Week 1: Introduction to Machine Learning
In Week 1, you will start with an introduction to machine learning, covering basic concepts, types of ML, and key terminology such as features, labels, training, and testing. On Friday, you will take a quiz and complete an assignment identifying real-life examples of machine learning applications.
Week 2: Python for Machine Learning
Week 2 focuses on Python essentials for ML, including NumPy and Pandas for handling data, working with dataframes, and performing basic operations. The Friday assignment will require you to load a dataset and summarize it using Python tools.
Week 3: Data Preprocessing
In Week 3, you will learn data preprocessing techniques, including handling missing values, encoding categorical variables, normalizing data, and splitting it into training and testing sets. On Friday, the assignment involves preprocessing a dataset of your choice and preparing it for modeling.
Week 4: Introduction to Scikit-learn
Week 4 introduces Scikit-learn, explaining estimators, transformers, and the fit/predict workflow. You will work on a simple dataset like Iris or Boston Housing, and Friday’s assignment will have you train a model and make predictions.
Week 5: Supervised Learning – Regression
During Week 5, you will learn regression algorithms, especially Linear Regression, and evaluation metrics such as MSE, RMSE, and R². Friday’s assignment will involve predicting house prices and evaluating the model’s performance.
Week 6: Supervised Learning – Classification
Week 6 focuses on classification algorithms such as Logistic Regression and K-Nearest Neighbors (KNN). You will also learn model evaluation metrics including accuracy, confusion matrix, precision, and recall. The Friday assignment will involve building a classification model to predict student pass/fail outcomes.
Week 7: Decision Trees and Random Forests
In Week 7, you will learn decision trees and random forests, including tree structures, splitting criteria, ensemble methods, and feature importance. The assignment for Friday will involve classifying the Iris dataset using both algorithms and comparing their performance.
Week 8: Support Vector Machines (SVM) and Hyperparameter Tuning
Week 8 introduces SVM algorithms and kernel functions, along with hyperparameter tuning using GridSearchCV. The Friday assignment will have you train an SVM on a dataset and optimize its parameters for better accuracy.
Week 9: Unsupervised Learning – Clustering
Week 9 covers unsupervised learning, focusing on clustering algorithms like K-Means and DBSCAN, along with visualizing clusters. The Friday assignment will involve clustering a customer dataset and analyzing the resulting groups.
Week 10: Dimensionality Reduction and PCA
In Week 10, you will learn about dimensionality reduction and Principal Component Analysis (PCA) to simplify high-dimensional data while retaining important information. On Friday, the assignment will involve applying PCA to a dataset and plotting the top components.
Week 11: Machine Learning Pipelines and Model Saving
Week 11 teaches how to build ML pipelines that combine preprocessing and modeling steps, and how to save and reload models using joblib or pickle. The Friday assignment involves building a pipeline, saving the model, and using it to make predictions.
Week 12: Real-World Project and Integration
In the final week, you will work on a real-world ML project, integrating all learned concepts into a complete solution, such as loan approval prediction or spam detection. Friday will serve as a final assessment, where you submit your code, predictions, and evaluation metrics.
Daily Class Structure
Each class day (Monday–Thursday) includes one hour of theory, one hour of coding demonstration, and one hour of hands-on practice. Friday is dedicated to testing and assignments to reinforce the week’s learning.
This format ensures that by the end of 12 weeks, you will have practical experience and the skills to develop ML projects using Scikit-learn.
I can also create detailed example exercises for each Friday assignment to make it ready for practice. Do you want me to do that next?
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