IBM AI Engineering Professional Certificate

Throughout the program, you will build a portfolio of projects demonstrating your mastery of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your ability to communicate project outcomes.

Advanced 0(0 Ratings) 0 Students enrolled English
Created by Thi Nguyen Van
Last updated Thu, 15-Aug-2024
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Course overview

What you'll learn

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    Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction 

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    Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn 

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    Deploy machine learning algorithms and pipelines on Apache Spark 

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    Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow 

Curriculum for this course
6 Lessons 00:00:00 Hours
Machine Learning with Python
3 Lessons 00:00:00 Hours
  • SciPy and scikit-learn
    .
  • Machine Learning
    .
  • Hierarchical Clustering Hierarchical Clustering
    .
Introduction to Deep Learning & Neural Networks with Keras
3 Lessons 00:00:00 Hours
  • Artificial Neural Network Artificial Neural Network
    .
  • Artificial Intelligence (AI)
    .
  • Deep Learning
    .
Introduction to Computer Vision and Image Processing
0 Lessons 00:00:00 Hours
Deep Neural Networks with PyTorch
0 Lessons 00:00:00 Hours
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About instructor

Thi Nguyen Van

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