Fundamentals of Artificial Intelligence (AI) and Machine Learning

• Experience with Python programming
• Basic familiarity with statistics and mathematical concepts

Audience

• Developers
• Data scientists

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed.

Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning.
• Learn the applications and uses of deep learning in telecom.
• Use Python, Keras, and TensorFlow to create deep learning models for telecom.
• Build their own deep learning customer churn prediction model using Python.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Applied Machine Learning

• Statistical learning vs. Machine learning
• Iteration and evaluation
• Bias-Variance trade-off
• Supervised vs Unsupervised Learning
• Problems solved with Machine Learning
• Train Validation Test – ML workflow to avoid overfitting
• Workflow of Machine Learning
• Machine learning algorithms
• Choosing appropriate algorithm to the problem

Algorithm Evaluation

• Evaluating numerical predictions
  • Measures of accuracy: ME, MSE, RMSE, MAPE
  • Parameter and prediction stability
• Evaluating classification algorithms
  • Accuracy and its problems
  • The confusion matrix
  • Unbalanced classes problem
• Visualizing model performance
  • Profit curve
  • ROC curve
  • Lift curve
• Model selection
• Model tuning – grid search strategies

Data preparation for Modelling

• Data import and storage
• Understand the data – basic explorations
• Data manipulations with pandas library
• Data transformations – Data wrangling
• Exploratory analysis
• Missing observations – detection and solutions
• Outliers – detection and strategies
• Standarization, normalization, binarization
• Qualitative data recoding

Machine learning algorithms for Outlier detection

• Supervised algorithms
  • KNN
  • Ensemble Gradient Boosting
  • SVM
• Unsupervised algorithms
  • Distance-based
  • Density based methods
  • Probabilistic methods
  • Model based methods

Understanding Deep Learning

• Overview of the Basic Concepts of Deep Learning
• Differentiating Between Machine Learning and Deep Learning
• Overview of Applications for Deep Learning

Overview of Neural Networks

• What are Neural Networks
• Neural Networks vs Regression Models
• Understanding Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Connections
• Working with Neurons, Layers, and Input and Output Data
• Understanding Single Layer Perceptrons
• Differences Between Supervised and Unsupervised Learning
• Learning Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Back Propagation

Building Simple Deep Learning Models with Keras

• Creating a Keras Model
• Understanding Your Data
• Specifying Your Deep Learning Model
• Compiling Your Model
• Fitting Your Model
• Working with Your Classification Data
• Working with Classification Models
• Using Your Models 

Working with TensorFlow for Deep Learning

• Preparing the Data
  • Downloading the Data
  • Preparing Training Data
  • Preparing Test Data
  • Scaling Inputs
  • Using Placeholders and Variables
• Specifying the Network Architecture
• Using the Cost Function
• Using the Optimizer
• Using Initializers
• Fitting the Neural Network
• Building the Graph
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluating the Model
  • Building the Eval Graph
  • Evaluating with Eval Output
• Training Models at Scale
• Visualizing and Evaluating Models with TensorBoard 

Application of Deep Learning in Anomaly Detection

• Autoencoder
  • Encoder - Decoder Architecture
  • Reconstruction loss
• Variational Autencoder
  • Variational inference
• Generative Adversarial Network
  • Generator – Discriminator architecture
  • Approaches to AN using GAN

Ensemble Frameworks

• Combining results from different methods
• Bootstrap Aggregating
• Averaging outlier score