About IBM AI Engineering Professional Certificate program
Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data-driven actionable intelligence for their businesses. Coursera has brought you the IBM AI Engineering Professional Certificate program, where you will be able to master fundamental concepts of machine learning and deep learning.
About Artificial Intelligence
Artificial Intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is constantly applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
And it has been demonstrated that computers can be programmed to carry out very complex tasks as, for example, discovering proofs for mathematical theorems or playing chess with great proficiency. so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.
Importance of Artificial Intelligence (AI)
Artificial Intelligence (AI) is rapidly transforming our world. Remarkable surges in AI capabilities have led to a number of innovations including autonomous vehicles and connected Internet of Things devices in our homes. AI is even contributing to the development of a brain-controlled robotic arm that can help a paralyzed person feel again through complex direct human-brain interfaces. These new AI-enabled systems are revolutionizing everything from commerce and healthcare to transportation and cybersecurity.
AI has the potential to impact nearly all aspects of our society, including our economy, but the development and use of the new technologies it brings are not without technical challenges and risks. AI must be developed in a trustworthy manner to ensure reliability, safety, and accuracy.
“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”
-Stephen Hawking, BBC
IBM AI Engineering Certificate Program is for
The IBM AI Engineering Professional Certificate is suitable for learners from a variety of backgrounds, including students looking to enter the workforce and existing professionals looking to future-proof themselves with in-demand AI skills.
What you will gain from this program
You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
Applied Learning Project
Throughout the program, you will build a portfolio of projects demonstrating your dominance of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning structures 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.
Syllabus of the IBM AI Engineering professional certificate program
This program consists of 6 courses, designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.
1. IBM AI Engineering: Machine Learning with Python. (Content ratings 93%)
This course dives into the basics of machine learning using an approachable and well-known programming language, Python. In this course, you will learn the following topics such as:
- Introduction to Machine Learning
- Regression- In this topic, you will get a brief introduction to Regression and learn about Linear, Non-linear, Simple, and Multiple regression and their applications.
- Classification- You will practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression, and SVM. Also, you learn about the pros and cons of each method and different classification accuracy metrics.
- Clustering- You will learn about different clustering approaches
- Recommender systems- In this module, you will learn about recommender systems and then you understand two main types of recommendation engines, namely, content-based and collaborative filtering.
- You will do a project based on what you have learned.
2. Scalable machine learning on Big Data using Apache Spark. (Content ratings 75%)
This course in the IBM AI Engineering certificate program will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real-world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer.
- Introduction to Apache Spark. You’ll learn how Apache Spark internally works and how to use it for data processing.
- Scaling Math for Statistics on Apache Spark- In this module, you will apply basic statistical calculations using the Apache Spark RDD API in order to experience how parallelization in Apache Spark works
3. Introduction to Deep Learning and Neural Networks with Keras. (Content ratings 94%)
Looking to start a career in Deep Learning? Look no further. This course of the IBM AI Engineering certification program will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks?
You will learn about the different deep learning models and build your first deep learning model using the Keras library.
After completing this course from the IBM AI Engineering program, you will be able to:
- Describe what a neural network is, what a deep learning model is, and the difference between them.
- Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
- Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
- Build deep learning models and networks using the Keras library.
4. IBM AI Engineering: Deep Neural Network with PyTorch. (Content ratings 88%)
This course from the IBM AI Engineering certification program will teach you how to develop deep learning models using Pytorch. This course will start with Pytorch’s tensors and Automatic differentiation package.
Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression followed by Feedforward deep neural networks, the role of different activation functions, normalization, and dropout layers.
Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
After completing this course, you will be able to:
- Explain and apply their knowledge of Deep Neural Networks and related machine learning methods.
- Know how to use Python libraries such as PyTorch for Deep Learning applications.
- Build Deep Neural Networks using PyTorch.
5. Building Deep Learning Models with Tensor Flow. (Content ratings 86%)
The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structures in, for instance, images, sound, and textual data.
Deep networks are capable of discovering hidden structures within this type of data. In this course, you will use the Tensor Flow library to apply deep learning to different data types in order to solve real-world problems.
After completing this course from the IBM AI Engineering certification, you will be able to:
- Explain foundational TensorFlow concepts such as the main functions, operations, and execution pipelines.
- Describe how TensorFlow can be used in curve fitting, regression, classification, and minimization of error functions.
- Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
- Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.
6. IBM AI Engineering: AI Capstone Project with Deep Learning. (Content ratings 90%)
In this capstone of the IBM AI Engineering Professional Certificate, you will apply their deep learning knowledge and expertise to a real-world challenge.
You will use a library of your choice to develop and test a deep learning model. You will load and pre-process data for a real problem, build the model, and validate it.
Then you will present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
After completing this course, you will be able to:
- Determine what kind of deep learning method to use in which situation.
- Know how to build a deep learning model to solve a real problem.
- Master the process of creating a deep learning pipeline.
- Apply knowledge of deep learning to improve models using real data.
- Demonstrate the ability to present and communicate outcomes of deep learning projects.
What you will learn from the IBM AI Engineering certificate program?
- Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction.
- You will be able to 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 Tensor Flow.
Related Job Roles
Machine Learning Engineer, Deep Learning Engineer, AI Engineer, Senior Data Scientist.
Requires foundational AI or Data Science skills including Python programming, as well as proficiency in High School level math. If you are new to AI and data science, check out the IBM AI Engineering Professional Certificate or IBM Data Science Professional Certificate.
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- Professional Certificate
- 3+ Months
- Paid Course (Paid certificate)
- Artificial intelligence Data Engineering Data Science Data Science with 'Python' Deep learning Keras Machine learning Pytorch Spark TensorFlow
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