Official CertNexus Program

Mathematical Foundation for AI and Machine Learning has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with innovations like self-driving cars, medical diagnosis and even beating humans at strategy games like Go and Chess. The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge. Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML.

Level: Beginner

Content

• Linear Algebra
• Multivariate Calculus
• Probability

Acquired skills and competences

The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts.

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 Python and 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. By the end of this course, you will be confident when it comes to building your own AI applications with your newly acquired skills!

Artificial Intelligence and Machine Learning Fundamentals 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. However, it's recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

Content

• Principles of Artificial Intelligence
• AI with Search Techniques and Games
• Regression Polynomial and Support Vector Regression
• Classification
• Using Trees for Predictive Analysis
• Clustering
• Deep Learning with Neural Networks

Acquired skills and competences

• Understand the importance, principles, and fields of A
• Implement basic artificial intelligence concepts with Python
• Apply regression and classification concepts to real-world problems
• Perform predictive analysis using decision trees and random forests
• Carry out clustering using the k-means and mean shift algorithms
• Understand the fundamentals of deep learning via practical examples

Machine learning is rapidly becoming the most preferred way of solving data problems, thanks to the huge variety of mathematical algorithms that find patterns which are otherwise invisible to us. Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The course begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you’ll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN).

Applied Deep Learning with PyTorch is designed for data scientists, data analysts, and developers who want to work with data using deep learning techniques. Anyone looking to explore and implement advanced algorithms with PyTorch will also find this course useful. Some working knowledge of Python and familiarity with the basics of machine learning are a must. However, knowledge of NumPy and pandas will be beneficial, but not essential.

Level: Intermediate (prerequisites Python and ML)

Content

• Introduction to Deep Learning and PyTorch
• Building Blocks of Neural Networks
• Classification Problem Using DNN
• Convolutional Neural Networks
• Style Transfer
• Analyzing the Sequence of Data with RNNs

Acquired skills and competences

• Detect a variety of data problems to which you can apply deep learning solutions
• Learn the PyTorch syntax and build a single-layer neural network with it
• Build a deep neural network to solve a classification problem
• Develop a style transfer model
• Implement data augmentation and retrain your model
• Build a system for text processing using a recurrent neural network

Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The course goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning course, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.

Level: Intermediate

Content

• Introduction to Natural Language Processing
• Applications of Natural Language Processing
• Introduction to Neural Networks
• Foundations of Convolutional Neural Networks
• Recurrent Neural Networks
• Gated Recurrent Units
• Long Short Term Memory
• State of the art in Natural Language Processing
• A practical NLP project workflow in an organization

Acquired skills and competences

• Understand various pre-processing techniques for deep learning problems
• Build a vector representation of text using word2vec and GloVe
• Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
• Build a machine translation model in Keras
• Develop a text generation application using LSTM
• Build a trigger word detection application using an attention model

Remote Course for Certified Artificial Intelligence Practitioner | Certificated Course