Description
Machine Learning and Deep Learning Projects in Python course. Machine learning and deep learning projects course in Python. Machine learning and deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been deployed in a wide variety of real-world projects, transforming the way businesses operate and improving outcomes across multiple domains. In this training, an attempt has been made to teach the audience their application in some real problems and projects (which are mostly popular and widely used projects) after the initial introduction to machine learning and deep learning. Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students’ skill in Python language also increases and they become more proficient in it. In this course, students are introduced to some machine learning and deep learning algorithms such as logistic regression, polynomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, etc. and different models. They will also use artificial neural networks for modeling to carry out projects. The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation criteria, different prediction methods, image processing, data analysis and statistical analysis from other departments. This is the course. Machine learning and deep learning have had a transformative impact on many industries, creating intelligent systems with the ability to make informed decisions and make accurate predictions. These innovative technologies have been applied in a series of real-world projects, which change the operational landscape of businesses and bring better results in various domains. In this training course, the main goal is to transfer knowledge to the audience assuming a basic understanding of machine learning and deep learning concepts. The focus then shifts to their practical applications in addressing real-world challenges and conducting projects, many of which are widely recognized and used in the field. In addition, the entire coding and implementation of the models is done using the Python programming language. This dual approach not only deepens students’ understanding of machine learning, but also helps them master the Python language itself. The curriculum of this course includes an introduction to several basic machine learning and deep learning algorithms, including logistic regression, simple Bayes polynomial, simple Bayes Gaussian, SGDClassifier, and some other algorithms along with various model architectures. As a central component of this course, students will use artificial neural networks for modeling, which will serve as a cornerstone for the implementation of various projects. Comprehensive use of relevant datasets in diverse domains, along with comprehensive data preparation and preprocessing techniques, is a priority. Students are equipped with the skills to effectively visualize and interpret results, apply validation criteria, explore diverse prediction methods, engage in image processing, and perform data analysis and statistical analysis. These aspects together form the multifaceted landscape covered in this course. And at the end, more than 40 complete and practical cheat sheets in the field of data science, machine learning, deep learning and Python have been provided to you.
What you will learn in the Machine Learning and Deep Learning Projects in Python course
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Introducing the structure of machine learning and deep learning and their application in real problems
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Introducing machine learning and deep learning algorithms and launching them in projects
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Implementation of machine learning and deep learning algorithms in Python
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Familiarity with Python syntax for using machine learning and deep learning
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Familiarity with forecasting models
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Data preparation and visualization for use in machine learning and deep learning algorithms
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Using case studies in projects
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Learn how to use APIs to collect up-to-date data and learn about different data sets
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Introducing and using different machine learning and deep learning libraries in Python
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Getting to know different neural networks and using them in real projects
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Image processing using artificial neural network (ANN) in Python
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Classification with neural networks using Python
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Familiarity with natural language processing (NLP) and its use in projects
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Forecasting sales volume, product price, sales price, etc
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Introduction and use of algorithm validation criteria such as: confusion matrix, accuracy score, precision score, recall score, F1 score, etc.
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+40 Data Science, Machine Learning, Deep Learning and Python Cheat Sheets
This course is suitable for people who
- Developers
- Data scientists
- Data analysts
- researchers
- teachers
- Managers
- students
- job seekers
Details of the Machine Learning and Deep Learning Projects in Python course
- Publisher: Udemy
- teacher: S. Emadedin Hashemi
- Training level: beginner to advanced
- Training duration: 5 hours and 33 minutes
- Number of courses: 59
Headlines of the Machine Learning and Deep Learning Projects in Python course on 11/2023
Prerequisites of the Machine Learning and Deep Learning Projects in Python course
Basic Python
Course images
Sample video of the course
Installation guide
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