Machine Learning Practical: 6 Real-World Applications

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python

4.55 (3131 reviews)

Udemy

platform

English

language

Data Science

What you will learn

You will know how real data science project looks like

You will be able to include these Case Studies in your resume

You will be able better market yourself as a Machine Learning Practioneer

You will feel confident during Data Science interview

You will learn how to chain multiple ML algorithms together to achieve the goal

You will learn most advanced Data Visualization techniques with Seaborn and Matplotlib

You will learn Logistic Regression

You will learn L1 Regularization (Lasso)

You will learn Random Forest Classifier

Course Gallery

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Machine Learning Practical: 6 Real-World Applications – Screenshot 3 — Screenshot 3 – Machine Learning Practical: 6 Real-World Applications

Machine Learning Practical: 6 Real-World Applications – Screenshot 4 — Screenshot 4 – Machine Learning Practical: 6 Real-World Applications

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Our Verdict

Gain hands-on experience in real-world machine learning applications through this Udemy course, but be prepared to encounter inconsistencies in sound quality and outdated/erroneous codes that may hinder your progress. While the curriculum showcases insightful techniques and algorithms for data visualization and model chaining, some areas could benefit from improved accuracy and troubleshooting assistance. Explore the potential of this program understanding its limitations to strengthen your proficiency in machine learning practices.

What We Liked

Comprehensive coverage of 6 real-world applications with Python
In-depth exploration of data visualization techniques using Seaborn and Matplotlib
practical experience in chaining multiple ML algorithms
Gain insight into advanced topics like Logistic Regression, L1 Regularization (Lasso), and Random Forest Classifier

Potential Drawbacks

Inconsistent sound quality and instructor changes affecting continuity
Codes provided are outdated and contain errors, with insufficient troubleshooting guidance
Focus on classification examples; lack of diversification in regression algorithms
Occasional flaws in problem resolution leading to potentially faulty information acquisition