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In this course, you will :
- Learn the fundamentals of Time Series Analysis and Forecasting.
- Recognize the business scenarios in which Time Series Analysis can be used.
- In Python, I'm creating 5 different Time Series Forecasting Models.
- Discover the concepts of auto regression and moving average models.
- Learn about the ARIMA and SARIMA forecasting models.
- To manipulate Time Series data and perform statistical computations, use Pandas DataFrames.
Syllabus :
- Time Series - Basics
- Setting up Python and Python Crash Course
- Time Series - Data Loading
- Time Series - Feature Engineering
- Time Series - Resampling
- Time Series - Visualization
- Time Series - Transformation
- Time Series - Important Concepts
- Time Series - Test Train Split
- Time Series - Naive (Persistence) model
- Time Series - Auto Regression Model
- Time Series - Moving Average model
- Time Series - ARIMA model
- Time Series - SARIMA model
- Stationary time Series
- Linear Regression - Data Preprocessing
- Linear Regression - Model Creation
- Introduction to ANN
- Single Cells - Perceptron and Sigmoid Neuron
- Neural Networks - Stacking cells to create network
- Important concepts: Common Interview questions
- Standard Model Parameters
- Tensorflow and Keras
- Python - Dataset for classification problem
- Python - Building and training the Model
- Python - Solving a Regression problem using ANN
