IBM Machine Learning Professional Certificate

In this section

• Data Analysis and Machine Learning
  This section covers retrieving data from diverse sources such as SQL and NoSQL databases, APIs, and cloud platforms. It explores common feature selection and engineering techniques, including handling categorical and ordinal features, addressing missing values, and managing outliers using various methods. Additionally, it emphasizes the importance of feature scaling and demonstrates multiple scaling techniques to prepare data effectively for machine learning.
  • Module-1
    • AI and Machine Learning Introduction
      An overview of Artificial Intelligence and Machine Learning, their history applications, and impact.
    • Machine Learning and Deep learning
      Overview of Machine Learning and Deep Learning, including their definitions differences, applications, and the importance of features and targets in ML projects
    • History of AI
      Comprehensive overview of artificial intelligence history from its origins in the 1940s through modern developments and future possibilities
    • Modern AI Applications and ML Workflow
      Current advancements in artificial intelligence highlighting transformative impacts across industries and practical applications shaping everyday life
    • Machine Learning Workflow
      Foundational concepts, workflow, and vocabulary of machine learning providing clear understanding of tools and processes for building and deploying ML models
  • Module-2
    • Retrieving Data from CSV and JSON Files
      Methods for retrieving data from various sources including CSV and JSON files with practical considerations using Python and Pandas
    • Retrieving Data from SQL and NoSQL Databases, APIs, and Cloud Data Sources
      Methods for retrieving data from SQL and NoSQL databases, APIs, and Cloud data sources with practical considerations and Python code examples
    • Data Cleaning
      Importance of data cleaning in machine learning, common issues with messy data, and methods for handling duplicate data to ensure reliable model outcomes
    • Outliers and Missing Values
      Comprehensive guide on handling missing values and outliers in datasets including detection, imputation, and removal techniques with practical Python examples
• Regression Supervised Machine Learning
• Classification Supervised Machine Learning
• Unsupervised Machine Learning
• Deep and Reinforcement Learning
• Capstone Project