Skill Development

Artificial Intelligence & ML

Build intelligent systems with real-world ML pipelines.

Hands-On Practical TrainingCertificate on Completion
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Weekly Outline

W1

Introduction to Artificial Intelligence and Machine Learning, types of machine learning, machine learning workflow, datasets, features and labels, data preprocessing, and train-test splitting.

W2

Data preprocessing, data cleaning, handling missing values, encoding categorical data, feature scaling, exploratory data analysis, and feature selection.

W3

Supervised learning, including linear regression, logistic regression, classification, regression, and basic model training.

W4

Classification algorithms, including decision trees, random forests, support vector machines, K-nearest neighbors, and model comparison.

W5

Unsupervised learning, including clustering, K-means clustering, dimensionality reduction, and basic anomaly detection.

W6

Model evaluation, including confusion matrix, accuracy, precision, recall, F1-score, ROC-AUC, MAE, MSE, RMSE, and cross-validation.

W7

Model improvement, including hyperparameter tuning, feature engineering, handling imbalanced datasets, ensemble methods, and model optimization.

W8

A complete machine learning project covering data preprocessing, exploratory analysis, model development, evaluation, comparison, optimization, and basic deployment.

Weekly Assessment

An assessment is conducted at the end of every week to evaluate concepts and practical skills covered during that week.

Final Assessment

A comprehensive AI and ML project covering data preprocessing, exploratory data analysis, machine learning model development, evaluation, optimization, and deployment.

Skills Developed

Artificial Intelligence and Machine Learning fundamentalsMachine learning workflowData preprocessing and cleaningExploratory data analysisFeature engineering and feature selectionSupervised learningRegression and classificationClassification algorithmsUnsupervised learningClustering and dimensionality reductionModel evaluation and performance analysisHyperparameter tuning and model optimizationHandling imbalanced datasetsEnsemble learningBasic machine learning deploymentEnd-to-end ML project development

Learning Outcomes

  • Understand fundamental AI and ML concepts and workflows.
  • Prepare, clean, and preprocess datasets for machine learning.
  • Perform exploratory data analysis and select relevant features.
  • Build regression and classification models.
  • Apply different supervised and unsupervised learning algorithms.
  • Evaluate and compare machine learning models.
  • Apply appropriate performance metrics to ML models.
  • Improve models through feature engineering and hyperparameter tuning.
  • Handle imbalanced datasets and apply ensemble methods.
  • Develop and deploy a complete basic machine learning solution.