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Course Outline
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
Requirements
- Experience with Python programming
- Basic familiarity with statistics and mathematical concepts
Audience
- Developers
- Data scientists
28 Hours
Testimonials (1)
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