Audience
This course suits those that have a background in Python or any other high level programming language such as Java, C, or C++. This course is also potentially useful to those considering entry into the field of machine learning, or would like to add practical aspects to their current knowledge of the subject. You will particularly find this 3 day course useful if you are a hands-on type of learner.
Prerequisites
You should have experience with Python or another high level programming language, such as Java, C, or C++.
Duration
3 days. Hands on.
Course Objectives
Machine Learning is essentially about building software systems that learn from data. This course is intended to provide a grounding in the theory surrounding machine learning, including the larger discipline of Artificial Intelligence. This course is intended to be as complete as possible, and hence considers foundational aspects of data analytics that are vital to the machine learning process.
Course Content
1. Foundations of Machine Learning
Machine Learning Perspective of Data
Scales of Measurement
• Nominal Scale of Measurement
• Ordinal Scale of Measurement
• Interval Scale of Measurement
• Ratio Scale of Measurement
Feature Engineering
• Dealing with Missing Data
• Handling Categorical Data
• Normalizing Data
• Feature Construction or Generation
Exploratory Data Analysis (EDA)
• Univariate Analysis
• Multivariate Analysis
2. Supervised Learning – Regression
Correlation and Causation
Fitting a Slope
Assessing your model
Polynomial Regression
Multivariate Regression
Multicollinearity and Variation Inflation Factor (VIF)
Interpreting the Ordinary Least Squares (OLS) Regression Results
Regression Diagnosis
Regularization
Nonlinear Regression
3. Supervised Learning – Classification
Logistic Regression
Evaluating a Classification Model Performance
ROC Curve
Fitting Line
Stochastic Gradient Descent
Regularization
Multiclass Logistic Regression
Generalized Linear Models
Supervised Learning – Process Flow
Decision Trees
Support Vector Machine (SVM)
k Nearest Neighbors (kNN)
Time-Series Forecasting
4. Unsupervised Learning Process Flow
Clustering
K-means Algorithm
Finding Value of k in K-means
Hierarchical Clustering
Principal Component Analysis (PCA)
5. Text Mining and Recommender Systems
Text Mining Process Overview
Text Data Assemble
Social Media
Text Preprocessing
Data Exploration (Text)
Model Building
Text Similarity
Text Clustering
Topic Modeling
Text Classification
Sentiment Analysis
Deep Natural Language Processing (DNLP)
Recommender Systems
6. Deep and Reinforcement Learning
Artificial Neural Network (ANN)
What Goes Behind, When Computers Look at an Image?
Why Not a Simple Classification Model for Images?
Perceptron – Single Artificial Neuron
Multilayer Perceptrons (Feedforward Neural Network)
MLP Using Keras
Autoencoders
Dimension Reduction Using Autoencoder
Convolution Neural Network (CNN)
Recurrent Neural Network (RNN)
Long Short-Term Memory (LSTM)
Reinforcement Learning