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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom, Campus Based/Physical Classroom	Video and Text Based

Course Overview

By studying and processing data to get insights that can be used to generate more precise forecasts and provide new and robust business values, Machine Learning And Data Analytics Using Python certification course may help organisations gain a competitive edge. An essential need for success in machine learning and data analytics is the understanding and use of programming languages. One of the most in-demand programming languages in the workplace is Python, which is utilised by professionals and software developers in key sectors including banking, healthcare, consulting, and academia.

The National University of Singapore's School of Computing's Machine Learning and Data Analytics using Python certification syllabus is designed with a heavy focus on real-world applicability to address the quickly changing demands and trends of business. Machine Learning and Data Analytics using Python training is available on Emeritus where students can attend live and interactive sessions and simulate real-life projects.

The Highlights

Flexible mode of delivery
No prior coding experience
Industry relevant curriculum
8 months course
6-8 hours/week

Programme Offerings

Certificate of completion
distinguished faculty
Live and interactive sessions
Campus immersion
Capstone Project

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	NUS

Eligibility Criteria

Certification qualifying details

A digital Machine Learning and Data Analytics using Python Certification by Emeritus will be given to learners who successfully complete the course work.

What you will learn

SQL knowledge

After completing the Machine Learning and Data Analytics using Python online course students will be able to:

Describe how to use data to fit machine learning models, generate insights, and visualise data using Python.
Recognise Python's data structures.
Utilise SQL to extract data from a database.
Utilise Python's useful libraries, such as Pandas and Numpy, to create custom functions and programmes and to handle data.
Apply data analysis expressions (DAX) to ETL (Extract, Transform, and Load) operations to do computations.
Create dashboards using Power BI.
Align and apply the supervised and unsupervised learning models in Python.
Optimise neural networks by identifying the ideal parameter settings using normalisation.
Apply exploratory data analysis (EDA) and data wrangling to a variety of data sources.
Describe difficulties and recommended practices for implementing machine learning models.

Who it is for

Data AnalystMachine Learning EngineerBig Data Analytics Engineer

The curriculum is intended for people from the technical field like:

Admission Details

The following steps are to be followed to enroll in Machine Learning and Data Analytics using Python certification online course:

Step 1: Browse the official URL

https://nus.comp.emeritus.org/machine-learning-and-data-analytics-using-python

Step 2: Click on the apply now option, log in from the registered email id and pay for the course.

Step 3: Learners will receive a confirmation email after which they can start their course.

Application Details

Students first need to visit the official website, apply for the Machine Learning and Data Analytics using Python classes by logging in with their email id, and make the payment.

The Syllabus

Week 1: Introduction to Analytics

The role of analytics in business intelligence and artificial intelligence
Data Processing Chain
A simple analytics example

Week 2: Database: Data Source and Data Queries

Understanding databases structure
Exploring a database in SQLite
Getting started with queries using SQLite
Turning data into information
Working with multiple tables
Using functions

Week 3: Data Warehouse: Load and Transform Data and Create a Data Model

Data preparation and data wrangling
Introduction to Exploratory Data Analysis (EDA)
Exploring and importing data into Power Pivot
Data munging with Power Query
Star schema data model
Creating a data model in Power Pivot

Week 4: Data Warehouse: DAX and Time-based Analysis

DAX calculation types and functions
Getting data from related tables
Data context
Creating calculated columns and measures in Power Pivot
Time-based analysis and period-based evaluations
Utilising EDA to present time-based data

Week 5: Data Visualisation: Pivot Tables and Charts and Power BI Basics

Working with Pivot tables and charts to create visualisations
Creating the BI interface in Excel
Case study using Power Pivot: Reseller Sales Analysis
Creating data Model, visualisations and dashboards in Power BI
Case study using Power BI: Sales Quota Analysis
Common visualisations used in EDA

Week 6: Data Mining Basics

The fundamentals of data mining and CRISP-DM
Demonstrating unsupervised learning technique, specifically, K-Means clustering in Orange using practical examples
Demonstrating supervised learning techniques such as decision tree and linear regression models in Orange to solve real-life problems

Week 8: Getting Started with Python

Introduction to Python as a programming language
Identifying types of errors in a Python program
Examining the basic building blocks in a Python program
Creating a simple Python program

Week 9: Collections, Strings and Comments

Using appropriate containers or collection data types to store data
Applying properties of lists, tuples, sets and dictionaries to organise data
Manipulating data and containers using functions and built-in methods on collections
Identifying the characteristics of strings
Implementing functions, operators and built-in methods on strings
Applying comments to describe written Python programs

Week 10: Operators and Program Flow Controls

Explaining concept of various operators
Using operators under appropriate conditions
Applying operator precedence and associativity for expression resolution
Examining the different types of program flow control
Utilising appropriate program flow control mechanisms
Executing sequence, selection and iteration program flow controls

Week 11: Functions, Import Statements, Inputs, Outputs, Exception Handling and File Handling

Exploring different types of functions
Differentiating functions from methods
Applying import keywords to Python modules
Applying the correct input syntax
Using exception statements to handle code errors
Displaying outputs with the correct syntax and format
Exploring methods in handling text and binary files

Week 12: The Numpy Library

Applying concepts in indexing, slicing and iterating arrays
Understanding the process of reading and writing arrays into files
Exploring the methods to manipulate arrays

Week 13: The Pandas Library

Creating and manipulating series and dataframes in the pandas library
Applying operations and functions to manipulate and manage values

Week 14: Scikit Learn and Keras

Overview on Scikit learn
Classification, regression, clustering, model selection, pre-processing and dimensionality reduction in Scikit Learn
Importing the library along with various functions required for model evaluation- confusion matrix, test_train_split etc.,
Overview on deep learning
Types of models supported by Keras
Importing the library and building models in Keras and its applications

Week 15: Data Visualisation

Creating data visualisations in matplotlib, pandas plot and Seaborn library
Applying concepts in enhancing and manipulating data visualisations

Week 16: Python Application Development

Applying concepts in Python to create an Inventory Management System
Building a Python application using external data

Week 18: Introduction to Machine Learning and Learning

Defining artificial intelligence, machine learning and data science and their correlation
The history and applications of AI and machine learning
Future trends and approaches in the AI field
Learning patterns of machines
The machine learning process and approaches
Concepts of learning difficulties and lack of performance
Underfitting and overfitting

Week 19: Supervised Learning: Classification

Applications of classification in business scenario
Explanation of dependent variable in classification
Continuous dependent variable versus categorical variable
ML techniques to predict the classes in classification
Demonstration using logistic regression in Python
Discussion of model statistics to evaluate the model

Week 20: Supervised Learning: Regression

Regressive algorithms for implementing supervised learning with examples
Case study discussion: The use of supervised learning, specifically linear regression in NBA teams to improve their ranking and win a cup
Regressive models such as function estimation as linear regression and classification using logistic regression
The role of regressive models in function estimation and classification

Week 21: Supervised Learning: Decision Trees

The theory, terminologies and applications of decision trees
The random forest ensemble system for high-performance decision-making and its applications
Machine learning concepts such as the entropy, GINI index and imbalanced classes issues

Week 22: Support Vector Machine Algorithms

Defining support vector machine (SVM) and explaining the underlying algorithm
Discussing parameter tuning in SVM
Demonstrating SVM for classification and regression
Advantages and disadvantages of using SVM

Week 23: Unsupervised Learning: Clustering techniques in K-Means

Describing the K-means clustering algorithm
Developing and using the K-means algorithm using Scikit Learn
Optimising a clustering system by changing the clustering parameters
VIsualising the results of clustering

Week 24: Unsupervised Learning: Hierarchical Clustering Method

Different applications of hierarchical clustering
The importance of metrics and linkage criteria in hierarchical clustering
Top-down and bottom-up approaches of hierarchical clustering
The implementation of hierarchical clustering and interpretation of results

Week 25: Unsupervised Learning: Probabilistic and Association Rule

Overview of probabilistic clustering and comparison with K-means
Exploring the Gaussian Mixture Model and Expectation Maximization algorithm
Overview of association rule and its application
Applying pre-processing and Apriori algorithm
Demonstration of association rule for market basket analysis

Week 26: Neural Network

The theory, terminology and definition of artificial neural networks
Training algorithms of neural networks
Neural network parameters and ways to prevent underfitting and overfitting
Identifying a multi-layer perceptron as a solution to the problems of nonlinearity
The implementation of some machine learning applications in the system
Developing a model of biological brain as an artificial neural network
Examples of how to use neural networks as a function estimator
Setting optimal parameters to improve performance

Week 27: Reinforcement Learning

Definitions and applications of reinforcement learning
Types of reinforcement learning
Reinforcement learning examples, such as traffic light control and personalised recommendation systems
Robot navigation using reinforcement learning

Week 28: Deep Learning Using Keras and Tensorflow

Defining deep learning and its applications
Examining how deep neural networks deal with semi-structured and unstructured data
Explain types of networks and its applications
Exploring backward and reverse propagation
Describing layers in sequential neural net-hidden layers, input and output layers
Describing hyperparameters in neural net
Demonstration and application of deep learning techniques

Week 29: Recommendation Systems

Defining recommendation systems and its uses
Exploring types of recommendation systems and their advantages and disadvantages
Exploring types of recommendation systems and their advantages and disadvantages