ML with Python: A Practical Introduction
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.
Description for ML with Python: A Practical Introduction
Supervised Learning Algorithms: Acquire knowledge regarding supervised learning algorithms, which encompass classification and regression methodologies.
Unsupervised Learning Algorithms: Comprehend unsupervised learning algorithms, including dimensionality reduction and clustering techniques.
Statistical Modeling and Machine Learning: Investigate the correlation between statistical modeling and machine learning and the methods for comparing the two.
Real-World Applications: Evaluate the societal implications of machine learning and provide examples from the real world.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On edX provided by IBM
Duration: 4-6 hours per week approx 5 weeks
Schedule: Flexible
Pricing for ML with Python: A Practical Introduction
Use Cases for ML with Python: A Practical Introduction
FAQs for ML with Python: A Practical Introduction
Reviews for ML with Python: A Practical Introduction
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML with Python: A Practical Introduction
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Welcome to the 'Gen AI for Code Generation for Python' course, where you will begin a journey to hone and expand your abilities in the field of code generation using Generative AI.
Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.
Master coding basics and create a Hangman game using generative AI tools like Google Bard in a beginner-friendly, 1.5-hour guided project.
Learn the significance, use cases, history, and pros and cons of generative AI in a business context, with a focus on its relationship to machine learning and services at Amazon.
Learn about AI principles and platforms like IBM Watson and Hugging Face, integrate RAG technology for chatbot intelligence, create web apps using Python libraries, and develop interfaces with generative AI models and Python frameworks.
Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.
Featured Tools
Begin your vocation in generative AI data engineering. Obtain the necessary skills to secure a position as a data engineer by acquiring an understanding of generative AI. No prior experience is required.
Investigate the objectives and advantages of Google's Big Data and Machine Learning products, including the use of BigQuery for interactive analysis, Cloud SQL, and Dataproc for migrating MySQL and Hadoop applications, and the selection of a variety of data processing tools on Google Cloud.
Understand Generative AI, its potential and challenges, and the responsible use of the Gemini Enterprise add-on.
Learn how to use Gemini for Google Workspace to boost productivity and efficiency in Gmail through its generative AI features.
Develop an expertise in fundamental mathematical concepts, such as vectors, matrices, statistics, differentiation, and equations, to facilitate your quantitative pursuits.