Data Analysis and Visualization using Python for Machine Learning

LEVEL: INTERMEDIATE                    HRDF: CLAIMABLE 

Eight (8) CPD Hours approved by MBOT

TRAINERS: 

Dr. Kishore Bingi, Dr. Madiah Binti Omar & Associate Professor Ir. Dr. Rosdiazli Ibrahim 

 

3 - 4 APRIL 2023

 

MS TEAMS

 

9.00AM - 5.00PM

 

RM 1,650 FOR PROFESSIONALS

10% Discount for Early Bird (until 3 March 2023) / Group / Students

CONTENT SUMMARY

INTRODUCTION

Data analysis and visualization are crucial components of the machine learning process. Python is a popular programming language for data science and machine learning, and it offers a wide range of libraries and frameworks that can help with both of these tasks. For data visualization, some popular Python libraries include Matplotlib and Seaborn. These libraries can help you create a wide range of charts, plots, and visualizations to help you understand and communicate your data more effectively. For data analysis, some popular Python libraries include Pandas, designed for working with tabular data, and NumPy, designed for working with numerical data. These libraries can help you load, manipulate, and analyze your data to prepare it for use in a machine-learning model. In addition to these libraries, many other tools and frameworks are available in the Python ecosystem that can help with data analysis and visualization for machine learning. For example, scikit-learn is a popular machine-learning library for Python that includes many useful tools for data preprocessing, model fitting, and evaluation. 

In this course, the participants will learn how to use Python to perform data analysis and visualization for machine learning. At the same time, the participants will learn how to use these tools to load, manipulate, and analyze data and visualize data using libraries like Matplotlib and Seaborn. It also explains data analysis like regression, clustering and classification using scikit-learn. By the end of this course, the participants will have a solid understanding of Python for data analysis and visualization in machine learning and be able to apply these skills to real-world data science problems and be well-prepared to continue their learning and development in this exciting field.

COURSE CONTENT

Introduction to Numpy

  • Creating arrays
  • Using arrays and scalars 
  • Indexing Arrays
  • Array Transposition
  • Universal Array Function
  • Array Processing
  • Array Input and Output

Introduction to Pandas

  • Data Frames
  • Index objects
  • Selecting Entries
  • Data Alignment
  • Rank and Sort
  • Summary Statistics
  • Missing Data
  • Index Hierarchy

Data Visualization Using Matplotlib

  • Matplotlib Overview
  • Bar Plots
  • Line Plots
  • Scatter Plots
  • Histograms
  • Box Plots and Violin Plots
  • Style and Presentation

Data Visualization Using Seaborn

  • Seaborn Overview
  • Categorical Plots
  • Relational Plots
  • Distribution Plots
  • Regression Plots
  • Matrix Plots
  • Multi Plot Grids
  • Style and Presentation

Data Analysis Using Machine Learning with SciKit Learn

  • Linear Regression
  • Logistic Regression
  • k Nearest Neighbor
  • Support Vector Machines
  • Decision Trees and Random Forests

Hands-on with examples from different fields

  • Case Study 1
  • Case Study 2



OBJECTIVES

Upon completion of this course, participants will be able to:

  • Perform data visualization for machine learning using Python Matplotlib and Seaborn libraries
  • Perform data analysis for machine learning using scikit-learn in Python
  • Applying data analysis and visualization for real-world data science problems


WHO SHOULD ATTEND?

  • Anyone interested in Big Data and Artificial Intelligence
  • Managers and Executives 
  • Engineers, Researchers & Consultants 
  • Start-ups Company
  • Junior/Senior Lecturers





OUR TRAINERS

1. Dr. Kishore Bingi  (UTP)

Kishore Bingi is a lecturer at the Electrical and Electronic Engineering Department and member of the Center for Systems Engineering at Universiti Teknologi PETRONAS (UTP). He obtained his Master’s degree from the National Institute of Technology Calicut, India and his PhD from UTP, Malaysia. He worked as an Assistant Systems Engineer at TATA Consultancy Services Limited, India. He also worked as Research Scientist and Post-Doctoral Researcher at the Universiti Teknologi PETRONAS, Malaysia. He served as an Assistant Professor at the Process Control Laboratory, School of Electrical Engineering, Vellore Institute of Technology, India. His research area is developing fractional-order neural networks, including fractional-order systems and controllers, chaos prediction and forecasting, advanced hybrid optimization techniques, Artificial Intelligence and Systems Engineering. He is an IEEE and IET Member and a registered Chartered Engineer (CEng) from Engineering Council UK. He currently serves as an Editorial Board Member/Academic Editor/Guest Editor for the International Journal of Applied Mathematics and Computer Science, Mathematical Problems in Engineering, Journal of Control Science and Engineering, and Fractal and Fractional. He has published over 50 articles in international journals and conference proceedings and has delivered over 15 lectures at international conferences, as well as four books and 6 book chapters.

2. Dr. Madiah Omar  (UTP)

MADIAH OMAR is a lecturer under Chemical Engineering Department at Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak, Malaysia. She is a classy IR4.0 academician with over 6 years of experience in Artificial Intelligence (AI), Modelling, Predictive Maintenance, Digital Twin, Hardware Integration and System Engineering for Rotating Equipment and Chemical Processes Applications. Driven to bridge academia and industry together, her work involves multiple industrial collaborators and experience in machine learning deployment to the field. Her current interest in IR4.0 focuses on transforming brilliant AI ideas into products.

3. Associate Professor Ir. Dr. Rosdiazli Ibrahim  (UTP)

ROSDIAZLI B. IBRAHIM is an Associate Professor under Electrical and Electronics Engineering Department at Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak, Malaysia. He is an experienced academician with over 25 years of involvement in Advanced Process Control, Automation, Intelligent Systems, Wireless Technology and Robotics. He secured multiple fundamental research, prototype, and industrial consultancy grants for his work. He owns 5 patents to date, and his current interest includes Predictive Maintenance, Artificial Intelligence and System Engineering for various applications.

COUNTDOWN

DaysHoursMinutesSeconds

REGISTRATION FEES

PROFESSIONALS

MYR1,650*

*fee quoted does not include SST, HRDF service fee, GST/VAT or withholding tax (if applicable).

EARLY BIRD/ GROUP/ STUDENT

MYR1,485*

*fee quoted does not include SST, HRDF service fee, GST/VAT or withholding tax (if applicable).

OUR LOCATION

Centre for Advanced & Professional Education (CAPE)

 Level 8, Permata Sapura, Kuala Lumpur City Centre, 50088 Kuala Lumpur​

CALL US

+605 - 368 7558 /

+605 - 368 8485

DROP US AN EMAIL

cape@utp.edu.my