Data Science Using Python

 AI/ML.

• Introduction to Python Programming
• Key Python Libraries (NumPy, SciPy, Pandas, Matplotlib)
• Refresher for Math Concepts (Online)
• Overview of AI and ML Concepts

Basic AI/ML  

• Formulating Real World Problems for AI/ML
• Intuitive and Simple Algorithms
• Data Processing and Visualization
• Representation of Real World Data
• Supervised and Unsupervised Learning
• Classification and Regression Problems
• End-to-end Problem Solving

Advanced AI/ML algorithms to create AI/ML applications.

• Principles and Practice of ML
• Linear Algorithms, Training and Optimization
• Non-linear Solutions and MLP
• Gradient Descent and Backpropagation
• Decision Trees, Random Forests and Ensembles
• Support Vector Machines and Kernels

Deep Learning Techniques.

• Introduction to Deep Learning
• Convolutional Neural Networks
• Auto-Encoders
• Recurrent Neural Networks
• Overview of Advanced Topics
• Human In the Loop Solutions, Deployment
• Generative Adversarial Networks

Machine Learning
Students will learn how to explore new data sets, implement a HOURS
comprehensive set of machine learning algorithms from scratch, and
master all the components of a predictive model, such as data
preprocessing, feature engineering, model selection, performance metrics
and hyperparameter optimization.
Predictive Modeling
Regression, Classification, Data Preprocessing, Model
Evaluation and Ensembles
Data Mining
Dimensionality Reduction, Clustering, Association Rules, Anomaly Detection,
Network Analysis and Recommender Systems
Specialty Topics
Data Engineering, Natural Language Processing, and Web Applications.