DeepLearning.AI |
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Machine Learning Modeling Pipelines in Production (Coursera) https://www.coursera.org/learn/machine-learning-modeling-pipelines-in-production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build |
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Mathematics for Machine Learning and Data Science (CourseraSpecs) https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science Offered by DeepLearning.AI. Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a ... |
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Natural Language Processing (CourseraSpecs) https://www.coursera.org/specializations/natural-language-processing Offered by DeepLearning.AI. Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest ... |
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Natural Language Processing in TensorFlow (Coursera) https://www.coursera.org/learn/natural-language-processing-tensorflow If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that |
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Natural Language Processing with Attention Models (Coursera) https://www.coursera.org/learn/attention-models-in-nlp In Course 4 of the Natural Language Processing Specialization, you will: a) Translate complete English sentences into German using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering, and d) Build a chatbot using a Reformer model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summ |
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Natural Language Processing with Classification and Vector Spaces (Coursera) https://www.coursera.org/learn/classification-vector-spaces-in-nlp In Course 1 of the Natural Language Processing Specialization, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest n |
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Natural Language Processing with Probabilistic Models (Coursera) https://www.coursera.org/learn/probabilistic-models-in-nlp In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of |
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Natural Language Processing with Sequence Models (Coursera) https://www.coursera.org/learn/sequence-models-in-nlp In Course 3 of the Natural Language Processing Specialization, you will: a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have |
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Neural Networks and Deep Learning (Coursera) https://www.coursera.org/learn/neural-networks-deep-learning In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specializati |
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Probability & Statistics for Machine Learning & Data Science (Coursera) https://www.coursera.org/learn/machine-learning-probability-and-statistics Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. After completing this course, learners will be able to: • Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. • Visua |
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Réseaux neuronaux et Deep Learning (Coursera) https://www.coursera.org/learn/neural-networks-deep-learning-fr Vous souhaitez vous lancer dans l’IA de pointe ? Ce cours est là pour vous y aider. Les ingénieurs en Deep Learning sont très convoités et la maîtrise de ce domaine vous ouvrira de nombreuses opportunités professionnelles. Le Deep Learning est également un nouveau « superpouvoir » qui vous permettra de développer des systèmes d’IA qui n’étaient même pas envisageables il y a encore quelques années. Vous découvrirez dans ce cours les bases du Deep Learning. Une fois que vous l’aurez terminé, vous |
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https://www.coursera.org/learn/nlp-sequence-models In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Emb |
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Sequences, Time Series and Prediction (Coursera) https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finall |
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Source Systems, Data Ingestion, and Pipelines (Coursera) https://www.coursera.org/learn/source-systems-data-ingestion-and-pipelines In this course, you will explore various types of source systems, learn how they generate and update data, and troubleshoot common issues you might encounter when trying to connect to these systems in the real world. You’ll dive into the details of common ingestion patterns and implement batch and streaming pipelines. You’ll automate and orchestrate your data pipelines using infrastructure as code and pipelines as code tools. You’ll also explore AWS and open source tools for monitoring your data |
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Structuring Machine Learning Projects (Coursera) https://www.coursera.org/learn/machine-learning-projects In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, |
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Supervised Machine Learning: Regression and Classification (Coursera) https://www.coursera.org/learn/machine-learning In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly prog |
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Team Software Engineering with AI (Coursera) https://www.coursera.org/learn/team-software-engineering-with-ai In this course, you'll elevate your software development skills by learning how to leverage AI in collaborative team environments. You'll discover how to use large language models (LLMs) to streamline testing processes, create comprehensive documentation, and manage complex dependencies. By the end of this course, you will be able to: - Utilize LLMs to generate and implement various types of software tests, from exploratory to security testing - Create clear, useful documentation that follows |
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TensorFlow: Advanced Techniques (CourseraSpecs) https://www.coursera.org/specializations/tensorflow-advanced-techniques Offered by DeepLearning.AI. Expand your skill set and master TensorFlow. Customize your machine learning models through four hands-on courses! |
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TensorFlow: Data and Deployment (CourseraSpecs) https://www.coursera.org/specializations/tensorflow-data-and-deployment Offered by DeepLearning.AI. |
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Unsupervised Learning, Recommenders, Reinforcement Learning (Coursera) https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly |
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Искусственный Интеллект (ИИ) для всехin (Coursera) https://www.coursera.org/learn/ai-for-everyone-ru ИИ предназначен не только для инженеров. Если вы хотите, чтобы ваша организация стала лучше в использовании ИИ, то этот курс поможет пройти курс всем, особенно вашим коллегам не из технической среды. В этом курсе вы узнаете: - Значение общей ИИ-терминологии, включая нейронные сети, машинное обучение, глубокое обучение и обработку данных - Что ИИ реально «может» и «не может» сделать - Как определить возможности применения ИИ дл решения проблем в вашей организации - Что такое создание машинного |
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Нейронные сети и глубокое обучение (Coursera) https://www.coursera.org/learn/neural-networks-deep-learning-ru Этот курс поможет вам ознакомиться с новейшими технологиями искусственного интеллекта. Инженеры по глубокому обучению сейчас широко востребованы, освойте методы глубокого обучения и перед вами откроются многочисленные карьерные возможности. Глубокое обучение также можно считать новой «сверхспособностью», с помощью которой вы будете строить такие ИИ-системы, которые невозможно было создать еще пару лет назад. В этом курсе вы познакомитесь с основами глубокого обучения. После завершения курса вы: |
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الذكاء الاصطناعي للجميع (Coursera) https://www.coursera.org/learn/ai-for-everyone-ar إن الذكاء الاصطناعي لا يقتصر على المهندسين فقط. إذا أردت أن تصبح مؤسستك أفضل في مجال استخدام الذكاء الاصطناعي، فإن هذه هي الدورة التدريبية المناسبة التي يمكنك دعوة الجميع، وبخاصة زملاؤك غير العاملين بالتكنولوجيا، للانضمام إليها. \n\nستتعلم في هذه الدورة:\n\n- المعنى الكامن وراء مصطلح الذكاء الاصطناعي، بما في ذلك الشبكات العصبية والتعلُّم الآلي والتعلُّم العميق وعلم البيانات\n- ما يمكن أن يفعله الذكاء الاصطناعي من الناحية الواقعية وما لا يمكنه فعله\n- كيفية اكتشاف فرص تطبيق الذكاء الاصطناعي على ا |
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الشبكات العصبية والتعلم العميق (Coursera) https://www.coursera.org/learn/neural-networks-deep-learning-ar إذا كنت ترغب في اختراق عالم الذكاء الاصطناعي شديد التطور، فسوف تساعدك هذه الدورة التدريبية على تحقيق ذلك. إن مهندسي التعلم العميق مطلوبون بشدة، كما أن إتقان التعلم العميق يمنحك العديد من فرص المستقبل المهني الجديدة. إن التعلم العميق يعد بمثابة "قوة عظمى" جديدة كذلك تساعدك على بناء أنظمة الذكاء الاصطناعي التي لم يكن بالإمكان الوصول إليها منذ عدة سنوات قليلة مضت. في هذه الدورة التدريبية، سوف تتعرف على أسس التعلم العميق. عندما تنتهي من هذا الفصل الدراسي، سيكون بإمكانك ما يلي: - فهم اتجاهات التقني |