20 practical projects of Machine Learning and Deep Learning and their implementation in Python along with all the codes
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 applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains.
In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects).
Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students' skills in Python language will also increase and they will become more proficient in it.
In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, ... and different models. Also, they will use artificial neural networks for modeling to do the projects.
The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course.
Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains.
Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then shifts to their practical applications in addressing real-world challenges and undertaking projects, many of which are widely recognized and utilized within the field.
Moreover, the entirety of coding and models implementation is conducted using the Python programming language. This dual approach not only deepens the students' grasp of machine learning but also contributes to their proficiency in the Python language itself.
The curriculum of this course encompasses the introduction of several fundamental machine learning and deep learning algorithms, including Logistic Regression, Multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, and some other algorithms among others, alongside diverse model architectures. As a pivotal component of the course, students delve into the utilization of artificial neural networks for modeling, which serves as the cornerstone for executing the various projects.
Comprehensive utilization of pertinent datasets spanning diverse domains, coupled with comprehensive data preparation and preprocessing techniques, takes precedence. The students are further equipped with the skills to visualize and interpret outcomes effectively, employ validation metrics judiciously, explore varied prediction methodologies, engage in image processing, and undertake data analysis and statistical analysis. These facets collectively constitute the multifaceted landscape covered by 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 given to you.
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