Machine Learning Practical Workout | 8 Real-World Projects
Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks
4.51 (2016 reviews)
20 972
students
14.5 hours
content
Jan 2025
last update
$74.99
regular price
What you will learn
Deep Learning Practical Applications
Machine Learning Practical Applications
How to use ARTIFICIAL NEURAL NETWORKS to predict car sales
How to use DEEP NEURAL NETWORKS for image classification
How to use LE-NET DEEP NETWORK to classify Traffic Signs
How to apply TRANSFER LEARNING for CNN image classification
How to use PROPHET TIME SERIES to predict crime
How to use PROPHET TIME SERIES to predict market conditions
How to develop NATURAL LANGUAGE PROCESSING MODEL to analyze Reviews
How to apply NATURAL LANGUAGE PROCESSING to develop spam filder
How to use USER-BASED COLLABORATIVE FILTERING to develop recommender system
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Our Verdict
The Machine Learning Practical Workout offers an opportunity to explore various practical applications while diving into 8 distinct projects in a hands-on manner. Despite some room for improvement regarding workflow and variety of algorithms used, the course excels at presenting realistic, engaging examples of machine learning tools in action. With clear explanations on mathematical concepts, you will expand your understanding of this complex field, just be aware that datasets could better reflect real-life situations.
What We Liked
- Comprehensive coverage of various machine learning and deep learning applications
- Hands-on approach with 8 practical projects helps to reinforce theoretical concepts
- The course provides real-world examples, making it easy to follow and apply the knowledge gained
- Clear explanations of mathematical concepts behind models are given
Potential Drawbacks
- Datasets used in projects are too clean, not reflecting real-life scenarios where data needs preprocessing
- Limited variety of algorithms applied to solve problems; a single algorithm being used repeatedly across several projects
- Lack of depth in addressing machine learning workflow, including data preparation and exploratory data analysis
- Concerns about the quality of instruction on certain libraries such as pandas, seaborn, matplotlib leading to repetition in coding
2367072
udemy ID
14/05/2019
course created date
09/09/2019
course indexed date
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