Vendor: Python

After you complete this course, you will be excited to revamp your current projects or build new intelligent networks. Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Pythonand discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.

 

As you make your way through the course, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore’s law. By the end of this course, you will be confident when it comes to building your own AI applications with your newly acquired skills.

Osnovne informacije

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  1. Understand the importance, principles, and fields of AI.
  2. Implement basic Artificial Intelligence concepts with Python.
  3. Apply regression and classification concepts to real-world problems.
  4. Perform predictive analysis using decision trees and random forests.
  5. Carry out clustering using the k-means and mean shift algorithms.
  6. Understand the fundamentals of deep learning via practical examples.
This course is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI.
It’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

Moduli koji će se izvoditi

  • Principles of Artificial Intelligence
    • Fields and Applications of Artifcial Intelligence
    • AI Tools and Learning Models
    • The Role of Python in Artifcial Intelligence
    • Python for Game AI
  • AI with Search Techniques and Games
    • Heuristics
    • Pathfnding with the A* Algorithm
    • Game AI with the Minmax Algorithm and Alpha-Beta Pruning
  • Regression
    • Linear Regression with One Variable
    • Linear Regression with Multiple Variables
    • Polynomial and Support Vector Regression
  • Classification
    • The Fundamentals of Classifcation
    • Classifcation with Support Vector Machines
  • Using Trees for Predictive Analysis
    • Introduction to Decision Trees
    • Random Forest Classifer
  • Clustering
    • Introduction to Clustering
    • The k-means Algorithm
    • Mean Shift Algorithm
  • Deep Learning with Neural Networks
    • TensorFlow for Python
    • Introduction to Neural Networks
    • Deep Learning

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Trajanje: 24 sati

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