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What is a Time Series?

How to create a time series?

Visualizing a time series plot

How to convert a multiplicative time series into an additive time series

Logarithmic transformation its significance and its use

Assumptions of a time series forecasting model

To extract the trend, seasonality and random terms from the model

How to decompose a time series

Using standard regression-based methods to do forecast

How to identify autocorrelation and partial autocorrelation

How to de-trend a time series

Augmented Dickey-Fuller Test (ADF test for stationarity)

When and how to use the differencing of a time series

Double exponential smoothing model

MAPE metric to compare the accuracy of any time series model

What is the ARIMA model and steps to initialize model

Applying ARIMA model for forcasting

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.

In this data science project, we will predict internal failures of Bosch using thousands of measurements and tests made for each component along the assembly line.

In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

What is Time Series

04m

How to create Time Series in R

05m

Components of Time Series

03m

Assumptions of Time Series Forecasting

05m

Investigate components of Time Series

02m

Example - Stock Market Dataset

01m

Decompose method - Additive/Multiplicative Component

03m

STL method

02m

Plot graph for Decompose and STL function

01m

How to do Time Series Forecasting

08m

Create Time Series Lag

10m

Add more Lag variables

02m

Identify AutoCorrelation

11m

Partial Autocorrelation

01m

DeTrend a Time Series using Johnson method

07m

Deseasonalize Time Series

05m

Seasonality Plot

03m

Augmented Dickey Fuller (ADF) Test

03m

KPSS Test

02m

Air Passenger Data - ADF Test

01m

Differencing Method

17m

Moving Average Method

04m

Data Visualization

01m

Forecast Methods

03m

Moving Average Method

06m

Exponential Smoothing Method

01m

Simple Exponential Smoothing Method

06m

Double Exponential Smoothing Method

01m

Triple Exponential Smoothing Method

01m

Compare accuracy of models

07m

Generate forecast using all 3 methods

03m

Arima - Auto Regressive Integrated Moving Average Model

17m

Get the Dataset ready

07m

Decompose Time Series Data

08m

Diffrentiation

05m

How to identify Lags

05m

Auto Arima

15m

Forecast

02m

Dummy Variables & Box Cox Transformation

02m

Neural Network based Forecasting

09m

Hyperparameter Tuning to increase accuracy

06m

Conclusion

01m