Description for Python and Pandas for Data Engineering
Techniques for Word Feature Conversion: Gain knowledge of methodologies such as one-hot encoding, bag-of-words, embeddings, and embedding bags for the transformation of words into features.
Contextual Embedding Utilizing Word2Vec: Acquire practical expertise in the application of Word2Vec models for the generation of contextual embeddings.
Constructing a Fundamental Neural Network Language Model: Comprehend the methodology for constructing a fundamental language model utilizing a neural network.
Applying N-Gram and Sequence-to-Sequence Models: Investigate N-gram models and sequence-to-sequence architectures for applications including document classification, text analysis, and sequence transformation.
Level: Beginner
Certification Degree: yes
Languages the Course is Available: 1
Offered by: On edX provided by AI
Duration: 3�6 hours per week 4 weeks (approximately)
Schedule: Flexible
Pricing for Python and Pandas for Data Engineering
Use Cases for Python and Pandas for Data Engineering
FAQs for Python and Pandas for Data Engineering
Reviews for Python and Pandas for Data Engineering
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Python and Pandas for Data Engineering
A fundamental introduction to the development of AI-powered applications using IBM Watson APIs and Python programming.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Acquire the fundamental skills of data management, extraction, querying, and visualization to power your AI initiatives.
Develop an expertise in fundamental mathematical concepts, such as vectors, matrices, statistics, differentiation, and equations, to facilitate your quantitative pursuits.
Provides learners with the necessary skills to implement advanced AI concepts and practical applications through an examination of reinforcement learning.
Using Python, participants will analyze supply chain datasets, resolve optimization issues, and cultivate transferable data analysis abilities.
This course offers a structured Python introduction for individuals who are not majoring in computer science. The course concentrates on data analysis and visualization, with practical, cross-disciplinary applications.
Understand Python methodologies like lambdas, csv file manipulation, and prevalent data science features, including cleansing and processing DataFrame structures.
Learn Python, analyze and visualize data, and apply your skills to data science and machine learning with a practical assignment to acquire hands-on skills for a career in data science.
Featured Tools
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.
This course covers Python programming, TensorFlow for linear regression, and app development for stock market prediction.
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.
Compare & Review AI Tool Directory AI Course Marketplace
