Machine Learning and Data Analytics using Python
It is a comprehensive program that teaches individuals the principles, techniques, and tools necessary to analyse and derive insights from large datasets
Part time, Online
8 Months
4,299 USD 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.
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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
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
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
Week 30: Challenges
- The effects of super intelligence on society and its future
- Examples of AI used in the real world
- The role of AI in developing effective surveillance systems
- Technical advantages and future trends
- The role of quantum computing in the field of ML and big data
NUS Frequently Asked Questions (FAQ's)
This course is taught in online mode.
No, learners from technical as well as non-technical fields with no prior coding expertise can also opt for this course.
It will take the participant 8 months to complete the course where they will have to give 6-8 hours every week to attend classes.
Yes, students can pay the fees in installments and they can receive a discount by using a referral code.
Yes, after successfully completing all the coursework students will be awarded the completion certificate.