What you’ll learn
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An understanding of the fundamental principles of machine learning.
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The differences between various types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
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Real-world applications of machine learning across different industries.
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Basics of Python programming, including data types, variables, and operators.
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How to work with Jupyter Notebooks for Python coding and data analysis.
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The usage of key Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.
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Different types of data: structured and unstructured data.
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Techniques for data preprocessing: cleaning, transformation, and normalization.
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How to conduct feature extraction and selection.
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Understanding and applying descriptive statistics in data analysis.
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Data visualization techniques using Matplotlib and Seaborn.
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The concepts of correlation and covariance in data.
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Implementing basic machine learning algorithms like Linear Regression and Logistic Regression
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Introduction to classification techniques: Decision Trees, Random Forests, and K-Nearest Neighbors (KNN).
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Unsupervised learning techniques like K-Means and Hierarchical Clustering.
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The concepts of overfitting, underfitting and understanding the bias-variance trade-off.
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Evaluation metrics for regression and classification tasks.
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Techniques for model validation, including cross-validation.
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An introduction to deep learning and neural networks.
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The architecture and applications of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
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How to use Scikit-Learn for building and training models.
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Techniques for hyperparameter tuning and model optimization.
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An introduction to Natural Language Processing (NLP).
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Text cleaning and preprocessing techniques for NLP.
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An overview of basic NLP algorithms.
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Understanding the concept of bias in machine learning models.
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Learning about the ethical implications of machine learning.
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Strategies for reducing bias and promoting fairness in machine learning models.
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Hands-on experience applying machine learning techniques to real-world datasets.
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Steps for continuing learning and advancing in the field of Machine Learning Engineering.
Can I download Machine Learning Engineering Tools for Beginners course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Machine Learning Engineering Tools for Beginners course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
You can check out for more Udemy coupons @ www.coursecouponclub.com
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Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
