These are the exercise files used for # Data Analytics and Deep Learning for Financial Services (IBF Funded) course.
The course outline can be found in
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Topic 1.1 Get Started on Python
- Overview of Python
- Set Python
- Code Your First Python Script
Topic 1.2: Data Types
- Number
- String
- List
- Tuple
- Dictionary
- Set
Topic 1.3 Operators
- Arithmetic Operators
- Compound Operators
- Comparison Operators
- Membership Operators
- Logical Operators
Topic 1.4 Control Structure, Loop and Comprehension
- Conditional
- Loop
- Iterating Over Multiple Sequences
- Comprehension
Topic 1.5 Function
- Function Syntax
- Return Values
- Default Arguments
- Variable Arguments
- Lambda, Map, Filter
Topic 1.6 Modules & Packages
- Import Modules and Packages
- Python Standard Packages
- Third Party Packages
Topic 2.1 Data Preparation
- Data Analytics with Pandas
- Pandas DataFrame and Series
- Import and Export Data
- Filter and Slice Data
- Clean Data
Topic 2.2 Data Transformation
- Join Data
- Transform Data
- Aggregate Data
Topic 2.3 Data Visualization
- Data Visualization with Matplotlib and Seaborn
- Visualize Statistical Relationships with Scatter Plot
- Visualize Categorical Data with Bar Plot
- Visualize Correlation with Pair Plot and Heatmap
- Visualize Linear Relationships with Regression
Topic 2.4 Data Analysis
- Statistical Data Analysis
- Time Series Analysis
Topic 2.5 Advanced Data Analytics
- Data Piping
- Groupby and Apply Custom Functions
- Linear Regression
Topic 3.1 Introduction to Deep Learning
- Overview of Artificial Intelligence (AI)
- Applications of AI to Finance Services
- Deep Learning Methodology
- Setup Tensorflow Keras
Topic 3.2 Introduction to Neural Network
- What is Neural Network (NN)?
- Loss Function and Optimizer
- Build a Neural Network Model for Regression
Topic 3 Classification Model with Neural Network
- One Hot Encoding and SoftMax
- Cross Entropy Loss Function
- Build a Neural Network Model for Classification
Topic 4.1 Convolutional Neural Network (CNN)
- Introduction to Convolutional Neural Network (CNN)
- Convolution & Pooling
- Build a CNN Model for Image Recognition
- Overfitting and Underfitting Issues
- Methods to Solve Overfitting
- Small Dataset Overfitting Issue
- Data Augmentation & Dropout
Topic 4.2 Transfer Learning
- Introduction to Transfer Learning
- Pre-trained Models
- Transfer Learning for Feature Extraction & Fine Tuning
Topic 4.3 Recurrent Neural Network (RNN)
- Introduction to Recurrent Neural Network (RNN)
- Types of RNN Architectures
- RNN Model for Sentiment Analysis
- RNN Model for Stock Price Prediction
Final Assessment
- Written Assessment (Q&A)
- Written Assessment (Case Study)