Udemy – Machine Learning and Deep Learning Projects in Python 2023-8 – Downloadly

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, changing the way businesses operate and improving outcomes across multiple domains. In this training, an attempt has been made to teach the audience about their application in some real problems and projects (which are mostly popular and widely used projects) after an initial introduction to machine learning and deep learning. Also, all the coding and implementation of the models is done in Python, so that apart from machine learning, students also increase their skills in Python language 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 various models. They will also use artificial neural networks for modeling to complete the projects. Using effective data sets in various fields, data preparation and pre-processing, visualization of results, use of validation criteria, various prediction methods, image processing, data analysis and statistical analysis from other departments. This course is. Machine learning and deep learning have had a transformational impact on many industries, leading to the creation of intelligent systems with the ability to make informed decisions and accurate predictions. These innovative techniques have been applied in a range of real-world projects, transforming the operational landscape of businesses and leading to better results in various fields. In this training course, the main goal is to transfer knowledge to the audience by having 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 the students’ understanding of machine learning but also helps them master the Python language. 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 with different model architectures. As a central component of this course, students will use artificial neural networks for modeling, which will serve as the cornerstone for the implementation of various projects. Comprehensive data preparation and preprocessing techniques as well as extensive use of relevant datasets across diverse domains are a priority. Students are equipped with the skills to effectively visualize and interpret results, apply validation criteria, explore various predictive methods, engage in image processing, and perform data analysis and statistical analysis. These aspects together form the multifaceted landscape covered in this course.

What you will learn in the Machine Learning and Deep Learning Projects in Python course

Introduction to the structure of machine learning and deep learning and their application in real problems
Introducing machine learning and deep learning algorithms and launching them into projects
Implementation of Machine Learning and Deep Learning Algorithms in Python
Familiar with Python syntax for using machine learning and deep learning
Getting familiar with forecasting models
Data preparation and visualization for use in machine learning and deep learning algorithms
Using Case Studies in Projects
Learn how to use APIs to collect the latest data and learn about different data sets
Introduction and use of various machine learning and deep learning libraries in Python
Getting to know the different neural networks and using them in real projects
Image Processing using Artificial Neural Networks(ANN) in Python
Classification with Neural Networks using Python
Getting familiar with Natural Language Processing (NLP) and its usage in projects
Forecasting sales volume, product price, selling price, etc.
Introduction and use of algorithm validation criteria such as: confusion matrix, accuracy score, precision score, recall score, F1 score, etc.
+40 Data Science, Machine Learning, Deep Learning, and Python Cheat Sheets