Data Science & Machine Learning (AI)

Module 1: Foundations of Data Science & AI (15 Credit Hours)
1.1 Introduction to AI & ML (5 Hours):

• Overview of AI, types of AI, ethical considerations, introduction to ML (supervised, unsupervised, reinforcement learning). Brief overview of generative AI.

1.2 Data Science Workflow (5 Hours):

• Data acquisition, cleaning, preprocessing, feature engineering, model selection, training, evaluation, deployment,
  monitoring. Agile development principles.

1.3 Python Fundamentals for Data Science (5 Hours):

• Basic syntax, data structures (lists, tuples, dictionaries), control flow, functions, object-oriented programming (OOP) concepts.

Module 2: Math & Statistics Essentials (10 Credit Hours)
2.1 Descriptive & Inferential Statistics (5 Hours):

• Descriptive statistics (mean, median, mode, variance, standard deviation), probability distributions, hypothesis testing, confidence intervals.

2.2 Linear Algebra Fundamentals (5 Hours):

• Vectors, matrices, matrix operations, eigenvalues, eigenvectors (focused on applications in ML).

Module 3: Data Manipulation & Visualization (10 Credit Hours)
3.1 Data Wrangling with Pandas (5 Hours):

• Data cleaning, transformation, manipulation using Pandas. Handling missing data, outliers. Data merging and concatenation.

3.2 Data Visualization with Matplotlib & Seaborn (5 Hours):

• Creating various types of plots (bar, line, scatter, histograms) for exploratory data analysis and communicating insights.

Module 4: SQL for Data Science (10 Credit Hours)
4.1 Relational Databases & SQL Fundamentals (5 Hours):

• Introduction to relational databases, SQL basics (SELECT, FROM, WHERE, JOINs, aggregate functions).

4.2 Advanced SQL & Database Design (5 Hours):

• Subqueries, CTEs, window functions, database normalization, indexing for query optimization.

Module 5: Supervised Learning (15 Credit Hours)
5.1 Regression & Classification Algorithms (10 Hours):

• Linear regression, logisticregression, decision trees, random forests. Model evaluation metrics (confusionmatrix, precision, recall, F1-score, AUC). Hyperparameter tuning (basic techniques).

5.2 Ensemble Methods (5 Hours):

• Bagging, boosting (brief overview).

Module 6: Unsupervised Learning & Dimensionality Reduction (10 Credit Hours)
6.1 Clustering Techniques (5 Hours):

• K-means clustering, hierarchical clustering (brief overview).

6.2 Dimensionality Reduction (5 Hours):

• Principal Component Analysis (PCA).

Module 7: Deep Learning Introduction (10 Credit Hours)
7.1 Neural Networks Fundamentals (5 Hours):

• Introduction to neural networks, perceptrons, activation functions, backpropagation.

7.2 Deep Learning Frameworks (5 Hours):

• Introduction to TensorFlow/Keras or PyTorch (basic usage).

Module 8: Natural Language Processing (NLP) & Generative AI (10 Credit Hours)
8.1 NLP Fundamentals (5 Hours):

• Text preprocessing, tokenization, stemming, word embeddings. Basic text classification.

8.2 Generative AI Introduction (5 Hours):

• Overview of generative models (LLMs, GANs – high-level concepts), prompt engineering basics.

Module 9: Model Deployment & MLOps (10 Credit Hours)
9.1 Model Deployment (5 hours):

• Introduction to model deployment strategies (cloud platforms, APIs – high-level overview).

9.2 MLOps Fundamentals (5 hours):

• Introduction to MLOps principles, version control for models (basic concepts).

• You will learn how to design intelligent models and advanced artificial neural networks and leverage predictive analytics to solve real-time problems in this course,
• Learn about the major applications of Artificial Intelligence across various use cases across various fields like customer service, financial services, healthcare, etc.
• Master the skills and tools used by the most innovative Artificial.
• Intelligence teams across the globe as you delve into specializations, and gain experience solving real-world challenges.
• Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, and tracking.
• Design and build your own intelligent agents and apply them to create practical Artificial Intelligence projects including games, Machine Learning models, logic constraint satisfaction problems, knowledge- based systems, probabilistic models, agent decision-making functions and more.
• Gain the ability to apply Artificial Intelligence techniques for problem- solving and explain the limitations of current Artificial Intelligence techniques.

Artificial Intelligence course is well suited for a variety of profiles like:

• Developers aspiring to be an ‘Artificial Intelligence Engineer’ or Machine Learning engineers
•  Analytics managers who are leading a team of analysts
•  Information architects who want to gain expertise in Artificial Intelligence algorithms
•  Graduates looking to build a career in Artificial Intelligence and Machine Learning