Machine Learning using Python

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++.

.

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

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

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

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

Unsupervised Learning Process Flow

• Clustering

• K-means Algorithm

• Finding Value of k in K-means

• Hierarchical Clustering

• Principal Component Analysis (PCA)

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

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