Certificate Program in Machine Learning
Course Overview
Course Contents
This Certificate Program in Machine Learning is designed to introduce individuals with minimal technical experience to the exciting field of machine learning (ML). Covering fundamental concepts and practical skills, this program enables students to understand and apply ML basics. Each module is carefully designed to progressively build knowledge and introduce tools used by data scientists and machine learning practitioners.
| Module | Details |
|---|---|
| Module 1 Introduction to Machine Learning |
Objective: Overview of ML applications and impact. Differences between supervised and unsupervised learning. Understanding data, features, and labels. |
| Module 2 Basics of Python Programming |
Objective: Introduction to Python for ML. Variables, data types, loops, and basic functions. Simple data manipulations using Python. |
| Module 3 Data Handling and Libraries in Python |
Objective: Introduction to ML libraries: NumPy, pandas, and Matplotlib. Loading, cleaning and exploring data. Basic data visualization techniques. |
| Module 4 Introduction to Statistics for Machine Learning |
Objective: Descriptive statistics, mean, median and mode. Variance, standard deviation and distributions. Probability basics and importance in ML. |
| Module 5 Exploratory Data Analysis (EDA) |
Objective: Identifying data trends and outliers. Analyzing patterns through visualizations. Data transformation and feature scaling. |
| Module 6 Supervised Learning: Linear Regression |
Objective: Fundamentals of linear regression. Building and evaluating a linear regression model. Practical exercises using simple datasets. |
| Module 7 Supervised Learning: Classification Algorithms |
Objective: Introduction to classification tasks and algorithms. Understanding K-Nearest Neighbors (KNN) and decision trees. Creating and evaluating a classification model. |
| Module 8 Unsupervised Learning: Clustering |
Objective: Introduction to clustering and K-means algorithm. Use cases for clustering in real-world scenarios. Practical exercises on clustering with small datasets. |
| Module 9 Model Evaluation and Metrics |
Objective: Importance of model evaluation in ML. Metrics for classification (accuracy, precision, recall). Understanding confusion matrix and cross-validation. |
| Module 10 Introduction to Neural Networks |
Objective: Basics of artificial neural networks and deep learning. Simple neural network structure and working principles. Overview of common applications of neural networks. |
| Module 11 Practical Project: Building a Simple ML Model |
Objective: Applying knowledge from previous modules. Choosing a dataset, defining the ML problem and implementing a model. Model evaluation and interpreting results. |
| Module 12 Ethics and Future of Machine Learning |
Objective: Understanding ethical concerns in ML (bias, fairness, privacy). Real-world examples and impacts of ML applications. Future trends in machine learning and AI. |
Course Fee
Your Investment
Rs 60,000/=
Admission Requirements
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Computer Literacy
Applicants should possess basic computer skills and familiarity with standard digital tools.
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Zero Technical Prerequisites
Designed for beginners—no prior coding, IT, or mathematical background is required to enroll.
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Target Applicants
Suitable for high school graduates, career changers, and non-technical entry-level professionals seeking an introduction to machine learning.
Target Audience
This program is ideal for beginners, including high school graduates, career changers and entry-level professionals in non-technical fields who have basic computer skills and want to explore machine learning.
Please review the entry requirements before starting your application.
