• Classroom
  • Online, Instructor-Led
Course Description

Dive into the fascinating world of AI and Machine Learning with our three-day, comprehensive course, “Machine Learning Essentials with Python”. This course, perfect for basic Python developers, equips you with the skills to leverage Python for intelligent applications like data analysis, predictive modeling, automation, and chatbots, transforming your project capabilities. Participants will get hands-on experience with popular machine learning algorithms, exploring their potential applications and limitations.

Learning Objectives

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll learn how to: Master the Python Programming for Data Science: Gain an in-depth understanding of Python’s role in data science and AI, including proficiency in using key Python data science libraries like Pandas, NumPy, and Matplotlib. Understand the Fundamentals of AI and Machine Learning: Develop a strong grasp of AI and Machine Learning concepts, their applications, and how to differentiate between AI, Machine Learning, and Deep Learning. Dive into Supervised and Unsupervised Learning Techniques: Acquire hands-on skills to conduct Regression Analysis, Binary Classification, and k-means Clustering – key methods in Supervised and Unsupervised Learning. Apply Data Wrangling and Preprocessing Techniques: Learn to handle missing data, outliers, and categorical data effectively and perform feature scaling and normalization – crucial steps in Machine Learning projects. Create and Evaluate Machine Learning Models: Get a grip on the lifecycle of AI projects, including model creation, evaluation, validation, and the application of Ensemble Learning techniques. Understand and implement crucial data preprocessing techniques in Python: Attendees will acquire the ability to handle missing data, outliers, and categorical data, essential for creating reliable machine learning models. Develop competency in creating and interpreting data visualizations: Students will learn how to leverage Python’s powerful libraries such as Matplotlib and Seaborn to create compelling visualizations and extract meaningful insights from data. Construct a machine learning pipeline for real-world applications: Participants will gain the practical know-how to carry a machine learning project from initial data collection through to final model deployment, using Python. (Optional / Bonus Topics): Implement AI into Real-World Applications: By the end of the course, you’ll be able to build applications that integrate AI functionalities, using popular Python frameworks and modern AI technologies, like GPT-4.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Cyber Defense Analysis
  • Cyber Defense Infrastructure Support
  • Cyber Investigation
  • Digital Forensics
  • Exploitation Analysis
  • Network Services
  • Risk Management
  • Software Development
  • Test and Evaluation
  • Threat Analysis
  • Training, Education, and Awareness
  • Vulnerability Assessment and Management