Data & AI Next-Gen Skill

Artificial Intelligence & Machine Learning Foundations

Demystify machine learning algorithms, NLP foundations, and ethical AI implementation.

Artificial Intelligence is reshaping every industrial vertical. This course strips away the hype to provide an authentic, mathematically sound foundation in modern machine learning. Using Python, scikit-learn, and real-world datasets, you will build predictive classification and regression models, understand NLP concepts, and master prompt engineering best practices.

Total Duration 10 Weeks (40 Live Hours)

Experience Level Intermediate

Instructional Mode Live Online + Capstone Model

Authorized Credential Course Certificate

Target Career Roles

Associate ML Engineer AI Specialist Predictive Modeling Analyst Data Science Associate

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Learning Deliverables

What You Will Master

Navigate complete machine learning pipelines from exploratory data prep to model evaluation

Build predictive models using standard algorithms (Linear/Logistic Regression, Decision Trees, Random Forests)

Evaluate models rigorously with metrics: Accuracy, Precision, Recall, F1-Score, and ROC-AUC curves

Understand transformer architectures, tokenization, embeddings, and prompt engineering

Identify algorithmic bias and implement fair, transparent, and ethical AI practices

Syllabus Breakdown

Curriculum Modules

Sequenced deliberately to transition you from core statutory or architectural logic to independent practical execution.

Module 1: Machine Learning Pipelines & Data Preprocessing

Distinguishing AI vs. ML vs. Deep Learning, Python data science libraries (NumPy, Pandas, Matplotlib), handling missing values, feature scaling (StandardScaler, MinMaxScaler), and train-test splits.

Module 2: Supervised Learning (Regression & Classification)

Linear regression, cost functions, gradient descent, logistic regression for binary classification, decision trees, ensemble learning with random forests, cross-validation, and hyperparameter tuning.

Module 3: Unsupervised Learning & NLP Fundamentals

K-Means clustering, PCA dimensionality reduction, text preprocessing, tokenization, stop-word removal, TF-IDF vectorization, and building a basic sentiment classification model.

Module 4: Modern Generative AI, LLMs & Capstone Project

Neural network intuition, understanding transformer models, token embeddings, prompt engineering frameworks, AI ethics, fairness guidelines, and end-to-end deployment of an ML project demo.

Prerequisites

âœ“ Basic familiarity with Python programming (loops, functions)
âœ“ High school mathematics (basic algebra and statistics)

Who This Program Is For

âœ“ Software engineers seeking to transition into machine learning and AI development
âœ“ Data analysts expanding from descriptive reporting to predictive modeling
âœ“ Product managers and technology enthusiasts wanting genuine AI literacy

Authorized Certificate of Completion

AI & Machine Learning Foundations Certificate granted upon passing practical lab evaluations and capstone defense.

Backed by practical assignment verification and capstone code/return review.

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