Lakshmi Biradar Engineering
LINKEDIN LEARNING Path
Cautions and eTHICS IN ai
deep learning
Neural networks
rEINFORCEMENT lEARNING
Machinelearning
Timeline:May 18, 2026
Goal
Deep Learning: Getting Started
Deep Learning: Getting Started
Deep learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use deep learning as their foundational technology. Studying this technology, however, has several challenges. Most learning resources are math-heavy and are difficult to navigate without good math skills. IT professionals need a simplified resource to learn the concepts and build models quickly. This course aims to provide a simplified path to studying the basics of deep learning and becoming productive quickly. Instructor Kumaran Ponnambalam starts off with an intro to deep learning, including artificial neural networks and architectures. He navigates through various building blocks of neural networks with simple and easy to understand explanations. Kumaran also builds code in Keras to implement these building blocks. He then pulls it all together with an end-to-end exercise. Finally, test what you learned with a deep learning problem and compare your solution with Kumaran’s.
1 hour 13 min
nEURAL NETWORKS
Artificial Intelligence Foundations: Neural Networks
An artificial neural network uses the human brain as inspiration for creating a complex machine learning system. They can classify millions of sounds, videos, and images, answer our questions, understand our behaviors, and even drive our cars. Neural networks are also the foundation of generative AI.This course introduces the fundamental techniques and principles of neural networks, common models, and their applications. Instructor Gwendolyn Stripling takes you through the different neural network architectures, their components, appropriate use cases, and best practices for improving neural network model performance. Plus, gain hands-on experience building and training a neural network using the Keras Sequential API, an open-source library that demystifies the design and training of neural networks.
1 Hour 56 min.
MACHINE LEARNING
Artificial Intelligence Foundations: Machine Learning
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If you’re looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
1 Hour 56 min.
Cautions and eTHICS IN ai
Caution when working with Gen AI
Whether you work in film, marketing, healthcare, automobile, or real-estate, generative AI is changing the way your job is executed, and those who adapt early will reap its benefits sooner. All professions will be affected by generative AI. Its invention can be compared to the invention of photography, a true creative revolution. If you want to be part of the leaders that are advancing this revolution, this course can get you started on your learning journey. In this course, generative AI expert Pinar Seyhan Demirdag covers the basics of generative AI, with topics including what it is, how it works, how to create your own content, different types of models, future predictions, and ethical implications.
7 min.
reinforcement learning
Reinforcement Learning Foundations
Innovations in finance, health, robotics, and a variety of other sectors have been made possible with reinforcement learning (RL), which involves the training of machines to learn from their environment. Many top tech companies are investing heavily in this field. In this course, instructor Khaulat Abdulhakeem helps you learn the basics of this relatively new, but valuable skill. Get to know the key terminology used in RL, how RL plays a major role in the advancement of AI, and the kinds of problems you can use RL to solve. Khaulat shows you how to define and represent reinforcement learning problems. She also delves into RL algorithms, including the Monte Carlo and temporal difference methods. Plus, she explores deep and multi-agent RL, as well as how inverse learning works and how it can help agents learn by imitation.
44 min.
Goal
Develop a foundational understanding of artificial intelligence concepts and tools relevant to software engineering, including machine learning, neural networks, and generative AI, and apply this knowledge to better understand, collaborate on, and contribute to AI-enabled software solutions.
LINKEDIN LEARNING Path
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Transcript
Lakshmi Biradar Engineering
LINKEDIN LEARNING Path
Cautions and eTHICS IN ai
deep learning
Neural networks
rEINFORCEMENT lEARNING
Machinelearning
Timeline:May 18, 2026
Goal
Deep Learning: Getting Started
Deep Learning: Getting Started
Deep learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use deep learning as their foundational technology. Studying this technology, however, has several challenges. Most learning resources are math-heavy and are difficult to navigate without good math skills. IT professionals need a simplified resource to learn the concepts and build models quickly. This course aims to provide a simplified path to studying the basics of deep learning and becoming productive quickly. Instructor Kumaran Ponnambalam starts off with an intro to deep learning, including artificial neural networks and architectures. He navigates through various building blocks of neural networks with simple and easy to understand explanations. Kumaran also builds code in Keras to implement these building blocks. He then pulls it all together with an end-to-end exercise. Finally, test what you learned with a deep learning problem and compare your solution with Kumaran’s.
1 hour 13 min
nEURAL NETWORKS
Artificial Intelligence Foundations: Neural Networks
An artificial neural network uses the human brain as inspiration for creating a complex machine learning system. They can classify millions of sounds, videos, and images, answer our questions, understand our behaviors, and even drive our cars. Neural networks are also the foundation of generative AI.This course introduces the fundamental techniques and principles of neural networks, common models, and their applications. Instructor Gwendolyn Stripling takes you through the different neural network architectures, their components, appropriate use cases, and best practices for improving neural network model performance. Plus, gain hands-on experience building and training a neural network using the Keras Sequential API, an open-source library that demystifies the design and training of neural networks.
1 Hour 56 min.
MACHINE LEARNING
Artificial Intelligence Foundations: Machine Learning
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If you’re looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
1 Hour 56 min.
Cautions and eTHICS IN ai
Caution when working with Gen AI
Whether you work in film, marketing, healthcare, automobile, or real-estate, generative AI is changing the way your job is executed, and those who adapt early will reap its benefits sooner. All professions will be affected by generative AI. Its invention can be compared to the invention of photography, a true creative revolution. If you want to be part of the leaders that are advancing this revolution, this course can get you started on your learning journey. In this course, generative AI expert Pinar Seyhan Demirdag covers the basics of generative AI, with topics including what it is, how it works, how to create your own content, different types of models, future predictions, and ethical implications.
7 min.
reinforcement learning
Reinforcement Learning Foundations
Innovations in finance, health, robotics, and a variety of other sectors have been made possible with reinforcement learning (RL), which involves the training of machines to learn from their environment. Many top tech companies are investing heavily in this field. In this course, instructor Khaulat Abdulhakeem helps you learn the basics of this relatively new, but valuable skill. Get to know the key terminology used in RL, how RL plays a major role in the advancement of AI, and the kinds of problems you can use RL to solve. Khaulat shows you how to define and represent reinforcement learning problems. She also delves into RL algorithms, including the Monte Carlo and temporal difference methods. Plus, she explores deep and multi-agent RL, as well as how inverse learning works and how it can help agents learn by imitation.
44 min.
Goal
Develop a foundational understanding of artificial intelligence concepts and tools relevant to software engineering, including machine learning, neural networks, and generative AI, and apply this knowledge to better understand, collaborate on, and contribute to AI-enabled software solutions.