The Nuts and Bolts of ML
Learn to distinguish between different types of machine learning, prepare data for model development, build and evaluate Python-based models for both supervised and unsupervised learning, and choose the right model and metric for a given algorithm.
Description for The Nuts and Bolts of ML
Level: Advanced
Certification Degree: Yes
Languages the Course is Available: 2
Offered by: On Coursera provided by Google
Duration: 36 hours (approximately)
Schedule: Flexible
Pricing for The Nuts and Bolts of ML
Use Cases for The Nuts and Bolts of ML
FAQs for The Nuts and Bolts of ML
Reviews for The Nuts and Bolts of ML
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for The Nuts and Bolts of ML
Learn to apply advanced machine learning and deep learning models to real-world challenges by immersing yourself in the cutting-edge world of AI-powered finance and insurance.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
A program that emphasizes the practical implementation of data science and machine learning to overcome obstacles through the use of Python.
In-depth study of the applications of machine learning and computer vision, as well as practical experience in data analysis and Python programming.
Featured Tools
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Preparing students for a future in artificial intelligence security, this course offers AI hacking, vulnerability discovery, and attack mitigating techniques.
Acquire an extensive understanding of reinforcement learning, deep neural networks, clustering, and dimensionality reduction to effectively address real-world machine learning challenges.
To address OpenAI Gym challenges and real-world problems, this course offers pragmatic artificial intelligence methods like Genetic Algorithms, Q-Learning, and neural network implementation.
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.