Vendor: Python

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.

Osnovne informacije

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By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.
  • The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.
  • So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.
  • A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming.
  • To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.
  • You should also have experience working with databases and a high-level programming language such as Python, Java or C/C++.

Moduli koji će se izvoditi

  • Solving Business Problems Using AI and ML
    • Identify AI and ML Solutions for Business Problems
    • Follow a Machine Learning Workflow
    • Formulate a Machine Learning Problem
    • Select Appropriate Tools
  • Collecting and Refining the Dataset
    • Collect the Dataset
    • Analyze the Dataset to Gain Insights
    • Use Visualizations to Analyze Data
    • Prepare Data
  • Setting Up and Training a Model
    • Set Up a Machine Learning Model
    • Train the Model
  • Finalizing a Model
    • Translate Results into Business Actions
    • Incorporate a Model into a Long-Term Business Solution
  • Building Linear Regression Models
    • Build a Regression Model Using Linear Algebra
    • Build a Regularized Regression Model Using Linear Algebra
    • Build an Iterative Linear Regression Model
  • Building Classification Models
    • Train Binary Classification Models
    • Train Multi-Class Classification Models
    • Evaluate Classification Models
    • Tune Classification Models
  • Building Clustering Models
    • Build k-Means Clustering Models
    • Build Hierarchical Clustering Models
  • Building Advanced Models
    • Build Decision Tree Models
    • Build Random Forest Models
  • Building Support-Vector Machines
    • Build SVM Models for Classification
    • Build SVM Models for Regression
  • Building Artificial Neural Networks
    • Build Multi-Layer Perceptrons (MLP)
    • Build Convolutional Neural Networks (CNN)
  • Promoting Data Privacy and Ethical Practices
    • Protect Data Privacy
    • Promote Ethical Practices
    • Establish Data Privacy and Ethics Policies
  • Certification exam: Certified Artificial Intelligence (AI) Practitioner (AIP-110)

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Trajanje: 40 sati

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