The “Machine
Learning with Python for Managers” course is created to introduce managers
and business professionals to the basic concepts and practical applications of machine
learning, with Python as the main programming language. Throughout the 12-week course,
participants will acquire theoretical knowledge and practical experience
required to seamlessly incorporate machine learning techniques into their
business strategies. The course starts by establishing a solid understanding of
machine learning concepts and covers important types of machine learning, like
supervised and unsupervised learning. Students will investigate how these
techniques can be applied in various fields. Additionally, they will develop an
acquaintance with Python, with a focus on its fundamental syntax, data types,
and essential libraries such as NumPy, Pandas, and Matplotlib, which are
crucial for data manipulation and visualization. As the course progresses,
students will explore various data preparation techniques, including data
preprocessing, exploratory data analysis, and feature engineering. These
techniques are crucial for developing effective machine learning models. The
course will cover supervised learning models like regression and
classification, providing insight into how these algorithms can be used to
address business challenges such as sales forecasting and customer segmentation.
Additionally, participants will be introduced to unsupervised learning techniques
like clustering, which will help them grasp the concepts of data grouping and segmentation.
The course covers model evaluation, optimization, and time series forecasting.
It includes ARIMA and SARIMA models with real-world applications such as stock
price prediction. By the end of the course, learners will explore practical
applications of machine learning in domains like marketing, finance, and human
resources. This will equip learners with the skills to apply machine learning
models in decision-making processes, improving business performance and
strategy development.
| Course Status : | Upcoming |
| Course Type : | Core |
| Language for course content : | English |
| Duration : | 12 weeks |
| Category : |
|
| Credit Points : | 4 |
| Level : | Postgraduate |
| Start Date : | 15 Jul 2025 |
| End Date : | 31 Oct 2025 |
| Enrollment Ends : | 31 Aug 2025 |
| Exam Date : | |
| Translation Languages : | English |
| NCrF Level : | 6.5 |
| Industry Details : | Technology (High-tech) |
Week 01: Introduction to Machine Learning
Week 02: Basics of Python 1
Week 03: Basics of Python 2
Week 04: Data Preparation
Week 05: Regressive Regression Models
Week 06: Supervised Learning: Classification
Models Part 1
Week 07: Supervised Learning: Classification
Models Part 2
Week 08: Model Evaluation and Optimization
Week 09: Clustering
Week 10: Time Series Forecasting Part 1
Week 11: Time Series Forecasting Part 2
Week 12: Examples of using ML
Dr. David Campbell is an academic and
industry professional with extensive experience in Marketing Management,
Machine Learning, and Business Analytics. He is currently an Assistant
Professor at Manipal University Jaipur and holds a PhD in Management from
Bundelkhand University, Jhansi, an MBA from Christ College, Bangalore, and a
Bachelor of Engineering in Computer Science from Rajasthan University. Dr.
Campbell has significant industry experience, having worked with leading
companies like AkzoNobel, Samsung, and Whirlpool, where he managed sales
channels and optimized business operations. His expertise spans practical
applications in areas such as Marketing Analytics, Sales and digital marketing,
giving him a unique perspective on integrating machine learning with business
strategy. Dr. Campbell is a dedicated researcher with numerous published papers
and conference presentations. He brings his wealth of academic and industry
knowledge to the classroom, equipping students with the skills to succeed in
today's data-driven business landscape.
Enrolling and learning from the course is free. However, if you wish to obtain a certificate, you must register and take the proctored exam in person at one of the designated exam centre’s. The registration URL will be announced when the registration form is open. To obtain the certification, you need to fill out the online registration form and pay the exam fee. More details will be provided when the exam registration form is published, including any potential changes. For further information on the exam locations and the conditions associated with filling out the form, please refer to the form.
Grading Policy:
Assessment Type | Weightage |
Weekend Assessment | 25% |
Final Exam | 75% |
Certificate Eligibility:
Score | Type of Certificate |
>=90 | Gold |
76 - 89 | Silver |
61 - 75 | Bronze |
40 - 60 | Successfully Completed |
<39 | No Certificate |
Sample Certificate:
Disclaimer: In order to be eligible for the certificate, you must register for enrolment and exams using the same email ID. If different email IDs are used, you will not be considered eligible for the certificate.
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