Machine Learning: Natural Language Processing in Python (V2) .... Highest rated Data Science Course
Highest rated Udemy Data Science Course
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| Machine Learning: Natural Language Processing in Python (V2) .... Highest rated Data Science Course |
Description
This course focuses on the foundations of AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion. It covers vector models and text preprocessing methods, probability models and Markov models, machine learning methods, and deep learning and neural network methods.
In part 1, you will learn about vectors and their importance in data science and artificial intelligence, such as CountVectorizer and TF-IDF, as well as neural embedding methods like word2vec and GloVe. You will apply these knowledge for tasks such as text classification, document retrieval, and text summarization.
Part 2 covers probability models and Markov models, which are essential prerequisites for understanding the latest Transformer (attention) models like BERT and GPT-3. These models can be applied to tasks such as building a text classifier, article spinning, and text generation.
Part 3 covers machine learning methods, covering classic NLP tasks such as spam detection, sentiment analysis, latent semantic analysis, and topic modeling. The course is application-focused rather than theory-focused, focusing on how they can be applied to these tasks.
In part 4, you will learn about modern neural network architectures that can be applied to solve NLP tasks. Neural networks can be used for tasks such as language translation, speech recognition, and text-to-speech. The study of RNNs involves modern architectures such as LSTM and GRU, which have been widely used by Google, Amazon, Apple, and Facebook for difficult tasks.
This course is suitable for anyone interested in natural language processing (NLP), artificial intelligence, machine learning, deep learning, or data science, and anyone who wants to go beyond typical beginner-only courses on Udemy. The unique features of this course include detailed explanations of every line of code, no wasted time typing on the keyboard, and not fearing university-level math.
In part 1, you will learn about vectors and their importance in data science and artificial intelligence, such as CountVectorizer and TF-IDF, as well as neural embedding methods like word2vec and GloVe. You will apply these knowledge for tasks such as text classification, document retrieval, and text summarization.
Part 2 covers probability models and Markov models, which are essential prerequisites for understanding the latest Transformer (attention) models like BERT and GPT-3. These models can be applied to tasks such as building a text classifier, article spinning, and text generation.
Part 3 covers machine learning methods, covering classic NLP tasks such as spam detection, sentiment analysis, latent semantic analysis, and topic modeling. The course is application-focused rather than theory-focused, focusing on how they can be applied to these tasks.
In part 4, you will learn about modern neural network architectures that can be applied to solve NLP tasks. Neural networks can be used for tasks such as language translation, speech recognition, and text-to-speech. The study of RNNs involves modern architectures such as LSTM and GRU, which have been widely used by Google, Amazon, Apple, and Facebook for difficult tasks.
This course is suitable for anyone interested in natural language processing (NLP), artificial intelligence, machine learning, deep learning, or data science, and anyone who wants to go beyond typical beginner-only courses on Udemy. The unique features of this course include detailed explanations of every line of code, no wasted time typing on the keyboard, and not fearing university-level math.
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