DeepLearning.AI TensorFlow Developer Professional Certificate
Learn to use TensorFlow for computer vision and natural language processing, manage image data, prevent overfitting, and train RNNs, GRUs, and LSTMs on text repositories.
Description for DeepLearning.AI TensorFlow Developer Professional Certificate
Features of Course
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by DeepLearning.AI
Duration: 2 months at 10 hours a week
Schedule: Flexible
Pricing for DeepLearning.AI TensorFlow Developer Professional Certificate
Use Cases for DeepLearning.AI TensorFlow Developer Professional Certificate
FAQs for DeepLearning.AI TensorFlow Developer Professional Certificate
Reviews for DeepLearning.AI TensorFlow Developer Professional Certificate
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for DeepLearning.AI TensorFlow Developer Professional Certificate
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.
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.
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
Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.
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.
Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.
Use AI skills to advance your engineering career. Acquire practical knowledge regarding deep learning methodologies for computer vision.
The "Introduction to Vertex AI" course provides a four-hour, practical, and fundamental overview of Vertex AI, ideal for professionals and enthusiasts aiming to leverage AI effectively.
Featured Tools
Master the implementation of deep learning algorithms using PyTorch, covering Deep Neural Networks and machine learning techniques, along with Python library utilization, to construct and deploy deep neural networks effectively.
Develop and evaluate a neural network that can identify handwritten numerals, implement One Hot Encoding for classification, and evaluate the efficacy of the model through practical exercises.
Learn to develop interactive web applications with Python and Streamlit, train machine learning models using scikit-learn, and visualize evaluation metrics for binary classification algorithms.
Apply linear algebra concepts like linear independence, rank, singularity, eigenvalues, and eigenvectors to analyze data and solve machine learning problems using standard vector and matrix operations.
Master the AI and machine learning toolkit. Mathematics for Machine Learning and Data Science is a Specialization that is accessible to beginners. In this program, you will acquire the basic mathematics tools of machine learning, including calculus, linear algebra, statistics, and probability.