Artificial Intelligence & Machine Learning (AI/ML) Course – Intermediate & Advanced Levels: [EC_AI/ML_CO] SEP 13 – NOV 2

Categories: AI/ML
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About Course

Course Overview 

Duration: 8 Weeks (Weekends)

Level: Intermediate to Advanced

Certifications Aligned: –

  • IBM Data Analyst / Data Science Professional Certificate
  • Associate Certified Analytics Professional (aCAP)
  • Certified Analytics Professional (CAP)

Course Objectives

By the end of the course, learners will: – Apply statistical and machine learning techniques to real-world datasets- Build and evaluate predictive models- Work with structured and unstructured data- Design compelling dashboards and communicate insights effectively- Develop and deploy AI/ML models- Prepare for IBM, aCAP, and CAP certification.

What Will You Learn?

  • By the end of the course, learners will:
  • - Apply statistical and machine learning techniques to real-world datasets
  • - Build and evaluate predictive models
  • - Work with structured and unstructured data
  • - Design compelling dashboards and communicate insights effectively
  • - Develop and deploy AI/ML models
  • - Prepare for IBM, aCAP, and CAP certification.

Course Content

Week 1: Foundations of Data Analytics & Science
- Role of data in decision-making - Analytics lifecycle overview (aligned with aCAP/CAP domains) - Python environment setup (Jupyter, Colab) - Data wrangling with Pandas - Exploratory data analysis (EDA) techniques - Hands-on: Exploratory insights on business data

  • Lesson 1: Role of data in decision-making
  • Lesson 2: Data wrangling with Pandas
  • Assignment 1

Week 2: SQL for Analytics + Visualization Tools
Data Analytics & Science Course (Intermediate to Advanced Level) - SQL essentials: SELECT, JOINs, GROUP BY, subqueries - Analytical queries: window functions, CTEs - Hands-on: Querying with real business datasets - Introduction to Data Visualization - Tools: Power BI, Tableau, Seaborn, Matplotlib - Hands-on: Creating dashboards and visual stories.

Week 3: Machine Learning Basics + Supervised Learning
- Introduction to AI & ML in the real world - Regression & classification basics - Algorithms: Linear/Logistic Regression, Decision Trees - Bias-variance trade-off, overfitting - Model performance metrics: Accuracy, AUC, Precision/Recall - Hands-on: Building a supervised model in Scikit-learn.

Week 4: Unsupervised Learning & NLP Introduction
- Clustering: K-Means, Hierarchical- Dimensionality Reduction: PCA, t-SNE - Introduction to NLP: tokenization, vectorization - Sentiment analysis basics with Python - Project: Customer segmentation + NLP task (product reviews or social media)

Week 5: Predictive Analytics & Time Series Forecasting
- Decomposition: Trend, seasonality, noise - Forecasting methods: ARIMA, Prophet - Evaluation for time-based models - Time-based feature engineering - Intro to Prescriptive Analytics: Optimization basics - Hands-on: Forecasting sales/demand

Week 6: Deep Learning & AI Applications
- Fundamentals of Neural Networks - CNNs, RNNs, transfer learning overview - AI use cases: image, text, recommendation engines - Building models with TensorFlow/Keras - Hands-on: Sentiment classifier or image recognizer.

Week 7: Model Deployment, Cloud Tools & Ethics
- Flask and Streamlit for ML apps - Cloud deployment: AWS/GCP walkthrough - Real-time APIs, model monitoring - Data ethics, privacy, responsible AI - aCAP/CAP exam prep: lifecycle, framing, deployment

Week 8: Capstone Project & Certification Readiness
- Group/individual capstone: end-to-end data project - Focus on modelling, dashboarding, storytelling - Final presentations + peer feedback - Certification tips: IBM, aCAP, CAP - Career guidance: Data Analyst -> Data Scientist -> AI Engineer

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