Practical Guide to AI & ML: Mastering Future Tech Skills
Artificial Intelligence & Machine Learning: Practical Training for Real-World Applications & Skills Development
4.47 (300 reviews)
1,881
students
33 hours
content
Jan 2025
last update
$59.99
regular price
What you will learn
Demonstrate a solid understanding of the difference between AI, Machine Learning and Deep Learning.
Clearly articulate why Large Language Models like ChatGPT and Bard are NOT intelligent.
Articulate the difference between Supervised, Unsupervised, and Reinforcement Machine Learning.
Explain the concept of machine learning and its relation to AI.
Define artificial intelligence (AI) and differentiate it from human intelligence.
Describe what Artificial Intelligence is, and what it is not.
Explain what types of sophisticated software systems are not AI systems.
Describe how Machine Learning is different to the classical software development approach.
Compare and contrast supervised, unsupervised, and reinforcement learning.
Explain Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features.
Explain Function Approximators and the role of Neural Networks as Universal Function Approximators.
Explain Encoding and Decoding when using machine learning models to work with non-numeric, categorical type data.
Demonstrate an intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals.
Identify examples of AI in everyday life and discuss their impact.
Evaluate the effectiveness of different AI applications in real-world scenarios.
Apply basic principles of neural networks to a hypothetical problem.
Discuss the role of data in training AI models
Construct a neural network model for a specified task
Assess the impact of AI on job markets and skill requirements
See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration.
Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process.
Learn how to use Model Builder to train models without having to code.
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udemy ID
1/21/2024
course created date
1/23/2024
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