What will you in Introduction to Machine Learning for Data Science Course
Grasp core machine learning concepts: supervised vs. unsupervised learning, overfitting, and model evaluation
Implement algorithms such as linear regression, logistic regression, decision trees, and k-means clustering
Preprocess data: handling missing values, feature scaling, encoding categorical variables, and dimensionality reduction
Evaluate model performance using metrics (MSE, accuracy, precision, recall, F1-score) and cross-validation
Deploy trained models with simple pipelines and understand basic considerations for productionization
Program Overview
Module 1: Introduction & Environment Setup
⏳ 30 minutes
Installing Python, Jupyter Notebook, and key libraries (scikit-learn, pandas, matplotlib)
Overview of the ML workflow and dataset exploration
Module 2: Data Preprocessing & Feature Engineering
⏳ 1 hour
Handling missing data, outliers, and normalization/standardization
Creating new features, encoding categoricals, and dimensionality reduction (PCA)
Module 3: Supervised Learning – Regression
⏳ 1 hour
Implementing linear and polynomial regression with scikit-learn
Assessing model fit, regularization techniques (Ridge, Lasso), and bias-variance trade-off
Module 4: Supervised Learning – Classification
⏳ 1 hour
Training logistic regression, k-nearest neighbors, and decision tree classifiers
Hyperparameter tuning with grid search and evaluating with confusion matrices
Module 5: Unsupervised Learning
⏳ 45 minutes
Applying k-means clustering and hierarchical clustering for segmentation
Using Gaussian mixture models and silhouette scores for cluster validation
Module 6: Ensemble Methods & Advanced Models
⏳ 1 hour
Boosting (AdaBoost, Gradient Boosting) and bagging (Random Forest) techniques
Understanding feature importance and improving model robustness
Module 7: Model Evaluation & Validation
⏳ 45 minutes
Cross-validation strategies, learning curves, and ROC/AUC analysis
Addressing class imbalance with resampling and metric selection
Module 8: Deployment & Best Practices
⏳ 30 minutes
Building a simple prediction pipeline and saving models with joblib
Key considerations for production: latency, monitoring, and data drift
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Job Outlook
Machine learning expertise is highly sought for roles such as Data Scientist, ML Engineer, and AI Specialist
Applicable in industries from finance and healthcare to tech and e-commerce for predictive analytics
Foundation for advanced topics: deep learning, NLP, computer vision, and big-data frameworks
Opens pathways to research, product development, and leadership in data-driven organizations
Specification: Introduction to Machine Learning for Data Science
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