IBM
IBM AI Engineering Professional Certificate
IBM

IBM AI Engineering Professional Certificate

Launch your career as an AI engineer. Learn how to provide business insights from big data using machine learning and deep learning techniques.

Wojciech 'Victor' Fulmyk
Ricky Shi
Aman Aggarwal

Instructors: Wojciech 'Victor' Fulmyk

154,905 already enrolled

Included with Coursera Plus

Earn a career credential that demonstrates your expertise
4.5

(7,754 reviews)

Intermediate level
Some related experience required
Flexible schedule
2 months at 10 hours a week
Learn at your own pace
Build toward a degree
Earn a career credential that demonstrates your expertise
4.5

(7,754 reviews)

Intermediate level
Some related experience required
Flexible schedule
2 months at 10 hours a week
Learn at your own pace
Build toward a degree

What you'll learn

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

  • Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn 

  • Deploy machine learning algorithms and pipelines on Apache Spark 

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

Details to know

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Taught in English

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Professional Certificate - 6 course series

Machine Learning with Python

Machine Learning with Python

Course 120 hours

What you'll learn

  • Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.

  • Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.

  • Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.

  • Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.

Skills you'll gain

Category: Regression Analysis
Category: Unsupervised Learning
Category: Machine Learning
Category: Supervised Learning
Category: Dimensionality Reduction
Category: Machine Learning Algorithms
Category: Decision Tree Learning
Category: Data Validation
Category: Scikit Learn (Machine Learning Library)
Category: Applied Machine Learning
Category: Python Programming
Category: Statistical Modeling
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Predictive Modeling
Category: Classification And Regression Tree (CART)
Category: Feature Engineering
Category: Performance Tuning
Category: Jupyter

What you'll learn

Skills you'll gain

Category: Apache Spark
Category: Data Processing
Category: Feature Engineering
Category: Statistical Methods
Category: Data Science
Category: Big Data
Category: Machine Learning
Category: Statistical Analysis
Category: Machine Learning Algorithms
Category: PySpark
Category: Distributed Computing
Category: Applied Machine Learning
Category: Data Manipulation
Category: Supervised Learning

What you'll learn

  • Describe the foundational concepts of deep learning, neurons, and artificial neural networks to solve real-world problems

  • Explain the core concepts and components of neural networks and the challenges of training deep networks

  • Build deep learning models for regression and classification using the Keras library, interpreting model performance metrics effectively.

  • Design advanced architectures, such as CNNs, RNNs, and transformers, for solving specific problems like image classification and language modeling

Skills you'll gain

Category: Deep Learning
Category: Artificial Neural Networks
Category: Keras (Neural Network Library)
Category: Network Architecture
Category: Computer Vision
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Natural Language Processing
Category: Unsupervised Learning
Category: Applied Machine Learning
Category: Machine Learning
Category: Regression Analysis
Category: Anomaly Detection
Category: Image Analysis

What you'll learn

  • Job-ready PyTorch skills employers need in just 6 weeks

  • How to implement and train linear regression models from scratch using PyTorch’s functionalities

  • Key concepts of logistic regression and how to apply them to classification problems

  • How to handle data and train models using gradient descent for optimization 

Skills you'll gain

Category: PyTorch (Machine Learning Library)
Category: Regression Analysis
Category: Data Manipulation
Category: Classification And Regression Tree (CART)
Category: Tensorflow
Category: Data Structures
Category: Statistical Methods
Category: Predictive Modeling
Category: Artificial Neural Networks
Category: Probability & Statistics
Category: Deep Learning

What you'll learn

  • Create custom layers and models in Keras and integrate Keras with TensorFlow 2.x

  • Develop advanced convolutional neural networks (CNNs) using Keras

  • Develop Transformer models for sequential data and time series prediction

  • Explain key concepts of Unsupervised learning in Keras, Deep Q-networks (DQNs), and reinforcement learning

Skills you'll gain

Category: Tensorflow
Category: Keras (Neural Network Library)
Category: Deep Learning
Category: Generative AI
Category: Reinforcement Learning
Category: Unsupervised Learning
Category: Time Series Analysis and Forecasting
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Image Analysis
Category: Performance Tuning
Category: Applied Machine Learning
Category: Artificial Neural Networks
Category: Natural Language Processing

What you'll learn

  • Demonstrate your hands-on skills in building deep learning models using Keras and PyTorch to solve real-world image classification problems

  • Showcase your expertise in designing and implementing a complete deep learning pipeline, including data loading, augmentation, and model validation

  • Highlight your practical skills in applying CNNs and vision transformers to domain-specific challenges like geospatial land classification

  • Communicate your project outcomes effectively through a model evaluation

Skills you'll gain

Category: Keras (Neural Network Library)
Category: PyTorch (Machine Learning Library)
Category: Deep Learning
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Computer Vision
Category: Python Programming
Category: Machine Learning

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

When you complete this Professional Certificate, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹

 

Instructors

Wojciech 'Victor' Fulmyk
IBM
7 Courses69,036 learners
Ricky Shi
IBM
1 Course44,614 learners
Aman Aggarwal
IBM
1 Course33,579 learners

Offered by

IBM

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Frequently asked questions

¹ Median salary and job opening data are sourced from Lightcast™ Job Postings Report. Content Creator, Machine Learning Engineer and Salesforce Development Representative (1/1/2024 - 12/31/2024) All other job roles (8/1/2024 - 8/1/2025)