Each project comes with 2-5 hours of micro-videos explaining the solution.

Get access to 50+ solved projects with iPython notebooks and datasets.

Add project experience to your Linkedin/Github profiles.

I have had a very positive experience. The platform is very rich in resources, and the expert was thoroughly knowledgeable on the subject matter - real world hands-on experience. I wish I had this... Read More

This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More

What are Artificial Neural Networks

Backpropagation and Forwardpropagation

Structure of a Neural Network (Neuron)

Input feature Weight Vector, Sum Function, Activation Function, and Bias in the network

Defining an activation function and understanding different types of activation function

Back Propagation NN is the multilayered feedforward NN

Defining a function for Initializing the network

Calculating the neuron activation for an input

Defining the Transfer function for neuron activation

Defining function for forwarding propagate input to a network output

Testing the forward propagation

Calculating the derivative of a neuron output

Backpropagating the error and storing it in neurons

Updating the network weights with calculated error

Training the network with some iterations

Importing the final dataset for testing created algorithm

Preprocessing the dataset and scaling it for better results

Feeding the dataset into Artificial Neural Networks and calculating the results

Applying cross-validation to prevent overfitting

Making the final predictions and calculating the accuracy score

From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, so in this machine learning project we present the novel Sorting Smoothing Method to estimate the real probability of default.

With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.

In this machine learning project, we will build a predictive model to find out the sales of each product at a particular store.

The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.

This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

4-Dec-2016

04h 44m