neural networks for sentiment and stock price prediction
neural networks for sentiment and stock price prediction, available at $49.99, has an average rating of 4.5, with 23 lectures, based on 290 reviews, and has 1758 subscribers.
You will learn about You can create an LSTM neural network and do a basic stock price prediction You know how to do sentiment analysis with LSTM neural networks You increase your knowledge and understanding of the deep learning library keras and pyhton You might open up new career opportunities for you which are not only highly rewarding but also offer more job satisfaction This course is ideal for individuals who are It's a hands on course so Your committment to code along with me or beginners to intermediate students in neural networks and machine learning who already know the basics or students who are eager to learn and dive into one of the hottest topics currently out there or students who ask how to do stock market predictions with neural networks or students who ask how to do sentiment analysis with neural networks It is particularly useful for It's a hands on course so Your committment to code along with me or beginners to intermediate students in neural networks and machine learning who already know the basics or students who are eager to learn and dive into one of the hottest topics currently out there or students who ask how to do stock market predictions with neural networks or students who ask how to do sentiment analysis with neural networks.
Enroll now: neural networks for sentiment and stock price prediction
Summary
Title: neural networks for sentiment and stock price prediction
Price: $49.99
Average Rating: 4.5
Number of Lectures: 23
Number of Published Lectures: 21
Number of Curriculum Items: 23
Number of Published Curriculum Objects: 21
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will Learn
- You can create an LSTM neural network and do a basic stock price prediction
- You know how to do sentiment analysis with LSTM neural networks
- You increase your knowledge and understanding of the deep learning library keras and pyhton
- You might open up new career opportunities for you which are not only highly rewarding but also offer more job satisfaction
Who Should Attend
- It's a hands on course so Your committment to code along with me
- beginners to intermediate students in neural networks and machine learning who already know the basics
- students who are eager to learn and dive into one of the hottest topics currently out there
- students who ask how to do stock market predictions with neural networks
- students who ask how to do sentiment analysis with neural networks
Target Audiences
- It's a hands on course so Your committment to code along with me
- beginners to intermediate students in neural networks and machine learning who already know the basics
- students who are eager to learn and dive into one of the hottest topics currently out there
- students who ask how to do stock market predictions with neural networks
- students who ask how to do sentiment analysis with neural networks
Let’s dive into data science with python and predict stock prices and customer sentiment.
machine learning / ai ? How to learn machine learning in python? And what is transfer learning ? How to use it ? How to create a sentiment classification algorithm in python? How to train a neural network for stock price prediction?
Good questions here is a point to start searching for answers
In the world of today and especially tomorrow machine learning and artificial intelligence will be the driving force of the economy. Data science No matter who you are, an entrepreneur or an employee, and in which industry you are working in, machine learning (especially deep learning neural networks) will be on your agenda.
“From my personal experienceI can tell you that companies will actively searching for you if you aquire some skills in the data science field. Diving into this topic can not only immensly improve your career opportunities but also your job satisfaction!”
It’s time to get your hands dirty and dive into one of the hottest topics on this planet.
To me the best way to get exposure is to do it “Hands on”. And that’s exactly what we do. Together we will go through the whole process of data import, preprocess the data , creating an long short term neural network in keras (LSTM), training the neural network and test it (= make predictions)
The course consists of 2 parts. In the first part we will create a neural network for stock price prediction. In the second part we create a neural network for sentiment analysis on twitter tweets.
Let’s get into it. See you in the first lecture
Course Curriculum
Chapter 1: LSTM neural networks for stock price prediction
Lecture 1: Stock market prediction with LSTM neural networks intro
Lecture 2: Download the resources
Lecture 3: One important thing before you start
Lecture 4: 1 Install Dependencies and load the data
Lecture 5: 2 Creating the dataset
Lecture 6: 3 Preprocessing the Dataset
Lecture 7: 4 Training the network
Lecture 8: 5 Visualizing the Network results
Chapter 2: Sentiment prediction with LSTM neural networks
Lecture 1: Sentiment Analysis Intro and load the dataset
Lecture 2: Visualizing the data
Lecture 3: Data preprocessing for sentiment analysis with neural networks
Lecture 4: Creating the model and train the neural network
Lecture 5: Testing and evaluating the LSTM model on sentiment
Lecture 6: Final words – Congratulation
Chapter 3: NEW BONUS – Predicting the next X days in the future with LSTM Models (multiple)
Lecture 1: The dataset we use (you can also use your own later on)
Lecture 2: 1 Predicting future prices for the next X days Introduction
Lecture 3: 2 Predicting future prices – dataprepreparation and visualization
Lecture 4: 3 Predicting future prices – defining and training the model
Lecture 5: 4 Predicting future prices – visualizing training and prediction results
Lecture 6: 5 Predicting future prices – next 10 day prediction and visualization
Lecture 7: More learning
Instructors
-
Dan We
Coach
Rating Distribution
- 1 stars: 11 votes
- 2 stars: 12 votes
- 3 stars: 30 votes
- 4 stars: 82 votes
- 5 stars: 155 votes
Frequently Asked Questions
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