IBM AI Engineering Specialization Highlights:
- IBM via Coursera
- Learn for FREE, Up-gradable [Enroll to Specialization for FREE Now]
- 8 Months (3 hours weekly) of effort required
- 27,754+ already enrolled!
- ★★★★★ 4.4 (13,983 Ratings)
- Language: English
Artificial intelligence (AI) is reforming whole ventures, changing the path organizations across areas influence information to decide. To remain serious, associations need qualified AI engineers who utilize forefront strategies like AI calculations and profound learning neural organizations to give information driven significant insight to their organizations. This 6-course Professional Certificate is intended to furnish you with the tools you need to prevail in your job as an AI or ML engineer.
What will you learn in this Specialization?
You’ll learn about the essential ideas of machine learning and deep learning, including administered and unaided picking up, utilizing programming dialects like Python. moreover, you will apply well known machine learning and deep learning libraries, for example, SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry issues including object acknowledgment, PC vision, picture and video preparing, text investigation, characteristic language handling (NLP), recommender frameworks, and different sorts of classifiers.
With the help of activities involved in this course, you’ll acquire fundamental information science abilities machine learning algorithms on huge information utilizing Apache Spark. You’ll construct, train, and send various sorts of profound designs, including convolutional neural organizations, intermittent organizations, and autoencoders.
IBM AI Engineering Professional Certificate (6 Courses)
Following are the 6 courses that are a part of this specialization
- Course 1: Machine Learning with Python (★★★★★ 4.7 | 10,447 ratings | 1,746 reviews)
This course jumps into the rudiments of machine learning utilizing a congenial, and notable programming language, Python. In this course, you will survey two fundamental segments: In the first one, you will find out about the reason for Machine Learning and where it applies to this present reality. In the subsequent one, you will get an overall diagram of Machine Learning subjects, for example, administered versus unaided learning, model assessment, and Machine Learning calculations. You might also be interested in difference between Grafana vs Tableau.
Besides, in this course, you practice with genuine instances of Machine learning and perceive how it influences society in manners you might not have speculated. After the completion of this course, you can add new experiences to your resume, for example, regression, classification, clustering, sci-kit learn, and SciPy.
- Course 2: Scalable Machine Learning on Big Data using Apache Spark (★★★★ 3.8 | 1,105 ratings | 287 reviews)
This course will enable you with the skills to scale information science and machine learning (ML) errands on Big Data sets utilizing Apache Spark. Most genuine machine learning work includes extremely huge informational collections that go past the CPU, memory, and capacity limits of a solitary PC.
Apache Spark is an open source system that uses bunch figuring and disseminated stockpiling to handle incredibly huge informational collections in a proficient and financially savvy way. Consequently, an applied information on working with Apache Spark is an incredible resource and likely differentiator for a Machine Learning engineer.
With the assistance of this course, you will gain a pragmatic comprehension of Apache Spark, and apply it to take care of machine learning issues including both little and huge information, and see how equal code is composed, equipped for running on a large number of CPUs.
- Course 3: Introduction to Deep Learning & Neural Networks with Keras (★★★★★ 4.7 | 804 ratings | 161 reviews)
This course will acquaint you with the field of profound learning and help you answer numerous inquiries that individuals are posing these days, similar to what is profound realizing, and how do profound learning models contrast with counterfeit neural organizations? You will find out about the diverse profound learning models and assemble your first profound learning model utilizing the Keras library.
On the successful completion of this course, you will have the option to depict what a neural organization is, the thing that a profound learning model is, and the contrast between them. You will likewise have the option to exhibit a comprehension of solo profound learning models, for example, autoencoders and confined Boltzmann machines.
- Course 4: Deep Neural Networks with PyTorch (★★★★★ 4.4 | 803 ratings | 178 reviews)
The course will show you how to grow a deep learning model utilizing Pytorch. The course will begin with Pytorch’s tensors and Automatic separation bundle. At that point, each segment will cover various models getting going with basics, for example, Linear Regression, and calculated/softmax relapse. Followed by Feedforward profound neural organizations, the part of various enactment capacities, standardization, and dropout layers. After this, the Convolutional Neural Networks and Transfer learning will be covered. At long last, a few other Deep learning strategies will be covered.
By the end of this course, you will have the knowledge to explain and apply their knowledge of Deep Neural Networks and related machine learning methods. Furthermore, you will know how to use Python libraries such as PyTorch for Deep Learning applications.
- Course 5: Building Deep Learning Models with TensorFlow (★★★★★ 4.3 | 524 ratings | 107 reviews)
Most of the information on the planet is unlabeled and unstructured. Shallow neural organizations can only with significant effort catch applicable construction in, for example, pictures, sound, and text based information. Profound organizations are fit for finding shrouded structures inside this sort of information. In this course, you’ll utilize the TensorFlow library to apply profound figuring out how-to various information types to take care of true issues.
After you complete this course, you will be able to explain foundational TensorFlow concepts such as the main functions, operations, and execution pipelines. In addition, your knowledge will be enough to describe how TensorFlow can be used in curve fitting, regression, classification, and minimization of error functions. Moreover, you will have a complete and deep understanding of different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
- Course 6: AI Capstone Project with Deep Learning (★★★★★ 4.5 | 300 ratings | 53 reviews)
In this course, students will apply their deep learning information and mastery to a true test. They will utilize a library of their decision to create and test a profound learning model. They will stack and pre-measure information for a genuine issue, construct the model and approve it. Students will at that point present an undertaking report to exhibit the legitimacy of their model and their capability in the field of Deep Learning. Visit here to learn How AI is Playing a Vital Role for Industrial Companies?
With the help of this course, you can determine what kind of deep learning method to use in which situation, and you can know how to build a deep learning model to solve a real problem. Last but not least you will have the chance to apply knowledge of deep learning to improve models using real data.
So, this was an overview of the IBM AI Engineering Professional Certificate Review. Our experts gave a detailed description of all of the courses of this specialization. We hope that you find this article helpful. Stay safe and keep learning.