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Understanding the problem statement

Importing the problem statement

Installing Keras and LSTM

Importing the necessary libraries for applying Neural Networks

Performing basic EDA and checking for the null values

Imputing the null values using appropriate method

Plotting a Time Series plot

Creating a Dataset matrix for applying LSTM

Sequentially initializing a Neural Networks

Defining the error function

Understanding solver used "Adam"

Applying LSTM as training model

Visualizing the loss and accuracy with each epoch

Tuning the final model and using it to make predictions

Saving the predictions made in CSV format

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.

Hive Project -Learn to write a Hive program to find the first unique URL, given 'n' number of URL's.

Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.

In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.

In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.

In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.

Deep Learning Architectures

06m

DNN - Deep Neural Network

00m

CNN - Convolutional Neural Network

01m

RNN - Recurrent Neural Network

02m

Deep Belief & Boltzman Network

01m

Deep Neural Network - Graphical Representation

20m

Activation Functions

02m

Perceptron and Bias

02m

Convolutional Neural Network - Graphical Representation

08m

Recurrent Neural Network - Graphical Representation

07m

Deep Belief & Boltzman Network - Graphical Representation

00m

Problem Statement

01m

Data Set

05m

Setting up Libraries

15m

Setting Theano as backend

01m

Import Libraries

02m

Create Seed function

01m

Normalize the dataset

04m

Split dataset into training and testing

06m

Create Dataset Matrix

07m

Reshape Dataset

05m

Create RNN or LSTM Model

04m

Make Predictions

08m

Calculate Mean Squared Error

03m

Conclusion

05m