Deep Learning & Machine Learning Course Singapore

Machine Learning Course Objectives

Course Objectives

This Machine Learning course will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.

Upon course completion, the candidate will be able to:

• Describe Machine Learning (ML) and its concepts
• Gain insights into the roles played by a Machine Learning Engineer
• Discuss various ML algorithms and their implementation
• Validate ML algorithms

Why should you learn Machine Learning to grow your career?

• Harvard Business Review endorses Machine Learning as the hottest job for today’s digital world due to its remarkable achievements in the R&D (Research and Development) sector.
• Most top-rated organizations are seeking Machine Learning professionals and offering high payouts.
• According to Indeed.com, on average, a Machine Learning professional earns $110,304 per annum.

Who should learn Machine Learning?

Machine Learning is being used by most among the world leading organizations. This training is the perfect choice for:

• Analytics managers
• Developers
• Business Analysts
• Information architects
• Graduates aiming to build a machine learning career

What are the prerequisites for the Machine Learning course?

The participants of our training should have:

• Familiarity with Python Programming fundamentals
• Fair understanding of Statistics and Mathematics basics

What will you learn in this Machine Learning training?

The following are the essential skills that users will gain upon completion of our training course:

• Representation of an artificial neural network
• Fundamentals of using data to train machines
• Logistic regression for classifying data using R
• Designing of a machine learning system
• Principle Component Analysis of data modeling
• Support Vector Machines algorithms
• Linear regression with multiple variables using R

SkillsFuture Claimable Course

Machine Learning Training Curriculum

• Introduction to Data Science
• What is data science and why is it so important?
• Applications of data science
• Various data science tools
• Data Science project methodology
• Tool of choice-Python: what & why?

Case study

• Introduction to Python
• Installation of Python framework and packages: Anaconda & pip
• Writing/Running python programs using Spyder Command Prompt
• Working with Jupyter notebooks
• Creating Python variables
• Numeric , string and logical operations
• Data containers : Lists , Dictionaries, Tuples & sets
• Practice assignment

Iterative Operations & Functions in Python

Writing for loops in Python

• While loops and conditional blocks
• List/Dictionary comprehensions with loops
• Writing your own functions in Python
• Writing your own classes and functions
• Practice assignment

Data summary & visualization in Python

• Need for data summary & visualization
• Summarising numeric data in pandas
• Summarising categorical data
• Group wise summary of mixed data
• Basics of visualisation with ggplot & Seaborn
• Inferential visualisation with Seaborn
• Visual summary of different data combinations
• Practice assignment

Data Handling in Python using NumPy & Pandas

• Introduction to NumPy arrays, functions & properties
• Introduction to Pandas & data frames
• Importing and exporting external data in Python
• Feature engineering using Python

Generalised Linear Models in Python

• Linear Regression
• Regularisation of Generalised Linear Models
• Ridge and Lasso Regression
• Logistic Regression
• Methods of threshold determination and performance measures for classification score models

Tree Models using Python

• Introduction to decision trees
• Tuning tree size with cross validation
• Introduction to bagging algorithm
• Random Forests
• Grid search and randomized grid search
• ExtraTrees (Extremely Randomised Trees)
• Partial dependence plots
• Case Study & Assignment
• Boosting Algorithms using Python
• Concept of weak learners
• Introduction to boosting algorithms
• Adaptive Boosting
• Extreme Gradient Boosting (XGBoost)
• Case Study & assignment

Machine Learning Basics

• Converting business problems to data problems
• Understanding supervised and unsupervised learning with examples
• Understanding biases associated with any machine learning algorithm
• Ways of reducing bias and increasing generalisation capabilites
• Drivers of machine learning algorithms
• Cost functions
• Brief introduction to gradient descent
• Importance of model validation
• Methods of model validation
• Cross validation & average error

Support Vector Machines (SVM) & kNN in Python

Introduction to idea of observation based learning

• Distances and similarities
• k Nearest Neighbours (kNN) for classification
• Brief mathematical background on SVM/li>
• Regression with kNN & SVM

Text Mining in Python

• Gathering text data using web scraping with urllib
• Processing raw web data with BeautifulSoup
• Interacting with Google search using urllib with custom user agent
• Collecting twitter data with Twitter API
• Naive Bayes Algorithm
• Feature Engineering with text data
• Sentiment analysis

Version Control using Git and Interactive Data Products

• Need and Importance of Version Control
• Setting up git and github accounts on local machine
• Creating and uploading GitHub Repos
• Push and pull requests with GitHub App
• Merging and forking projects
• Introduction to Bokeh charts and plotting
• Examples of static and interactive data products