Scalecast: Machine Learning & Deep Learning

Scalecast: Machine Learning & Deep Learning, available at $49.99, has an average rating of 4.38, with 43 lectures, based on 8 reviews, and has 1030 subscribers.

You will learn about Data Analysis and Exploration Plotting Graphs Scalecast Interfaces for Machine Learning and Deep Learning Exporting Results This course is ideal for individuals who are Beginners of Machine Learning & Deep Learning It is particularly useful for Beginners of Machine Learning & Deep Learning.

Enroll now: Scalecast: Machine Learning & Deep Learning

Summary

Title: Scalecast: Machine Learning & Deep Learning

Price: $49.99

Average Rating: 4.38

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will Learn

Data Analysis and Exploration
Plotting Graphs
Scalecast Interfaces for Machine Learning and Deep Learning
Exporting Results

Who Should Attend

Beginners of Machine Learning & Deep Learning

Target Audiences

Beginners of Machine Learning & Deep Learning

Uniform modeling (i.e. models from a diverse set of libraries, including scikit-learn, statsmodels, and tensorflow), reporting, and data visualizations are offered through the Scalecast interfaces. Data storage and processing then becomes easy as all applicable data, predictions, and many derived metrics are contained in a few objects with much customization available through different modules.

The ability to make predictions based upon historical observations creates a competitive advantage. For example, if an organization has the capacity to better forecast the sales quantities of a product, it will be in a more favorable position to optimize inventory levels. This can result in an increased liquidity of the organizations cash reserves, decrease of working capital and improved customer satisfaction by decreasing the backlog of orders. In the domain of machine learning, there’s a specific collection of methods and techniques particularly well suited for predicting the value of a dependent variable according to time, ARIMA is one of the important technique.

LSTM is the Recurrent Neural Network (RNN) used in deep learning for its optimized architecture to easily capture the pattern in sequential data. The benefit of this type of network is that it can learn and remember over long sequences and does not rely on pre-specified window lagged observation as input. The scalecast library hosts a TensorFlow LSTM that can easily be employed for time series forecasting tasks. The package was designed to take a lot of the headache out of implementing time series forecasts. It employs TensorFlow under-the-hood.

Some of the features are:

Lag, trend, and seasonality selection

Hyperparameter tuning using grid search and time series

Transformations

Scikit models

ARIMA

LSTM

Multivariate

– Assignment

Course Curriculum

Chapter 1: Scalecast – ML and DL

Lecture 1: Course Overview

Lecture 2: Introduction

Lecture 3: Forecaster

Lecture 4: Data Plotting

Lecture 5: Estimator- Multiple Linear Regression

Lecture 6: Multiple Machine Learning Models

Lecture 7: Analyzing Time Series – 1

Lecture 8: Analyzing Time Series – 2

Lecture 9: Transformations – 1

Lecture 10: Transformations – 2

Lecture 11: Transformations Example-1

Lecture 12: Transformations Example-2

Lecture 13: Transformations with Prediction

Lecture 14: ARIMA overview

Lecture 15: ARIMA – Simple Approach

Lecture 16: ARIMA – Iterative Approach

Lecture 17: ARIMA Auto – Overview

Lecture 18: ARIMA Auto – Implementation

Lecture 19: ARIMA – grid search

Lecture 20: Exporting results

Lecture 21: LSTM default usage

Lecture 22: LSTM and Linear Regression

Lecture 23: LSTM Prediction – 1

Lecture 24: LSTM Prediction -2

Lecture 25: LSTM Prediction – 3

Lecture 26: Multivariate Overview

Lecture 27: Multivariate Example 1 – 1

Lecture 28: Multivariate Example 1 – 2

Lecture 29: Multivariate Example 1 – 3

Lecture 30: Multivariate Example 1 – 4

Lecture 31: Multivariate Example 2 – 1

Lecture 32: Multivariate Example 2 – 2

Lecture 33: Multivariate Example 2 – 3

Lecture 34: Multivariate Example 2 – 4

Lecture 35: Multivariate Example 2 – 5

Lecture 36: Multivariate Assignment

Lecture 37: Sourcecode Information

Chapter 2: Supporting Videos

Lecture 1: Machine Learning Introduction

Lecture 2: Deep Learning Introduction

Lecture 3: RNN

Lecture 4: RNN with Keras

Lecture 5: LSTM

Lecture 6: LSTM with Keras

Instructors

Shrirang Korde
Technologist

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 2 votes
5 stars: 4 votes

Frequently Asked Questions

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