English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 41m | 353 MB
Build a strong foundation to enter the world of Machine Learning and data science with the help of this comprehensive guide
As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.
In this course, you’ll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you’ll learn about Deep learning and TensorFlow. Finally, you’ll see how to create an Ml architecture.
An easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of algorithms for Machine Learning.
What You Will Learn
- Explore the concept of Natural Processing Language and Recommendation Systems
- Tokenize a sentence
- Transform text tokens into numerical vectors by vectorizing
- Modeling topics to identify common topics among documents
- Find out about ANNs
- Compute the gradients of all output tensors
- Create a Machine Learning architecture from scratch
Table of Contents
Introduction to Natural Language Processing
1 The Corse Overview
2 NLTK and Built-In Corpora
3 The Bag-Of-Words Strategy
4 A Sample Text Classifier
Topic Modeling and Sentiment Analysis in NLP
5 Latent Semantic Analysis
6 Probabilistic Latent Semantic Analysis
7 Latent Dirichlet Allocation
Introduction to Deep Learning and TensorFlow
8 Deep Learning at a Glance
9 Introduction to TensorFlow
10 A Quick Glimpse Inside Keras
Creating a Machine Learning Architecture
11 Machine Learning Architecture
12 Scikit-learn Tools for Machine Learning Architectures
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