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Weekend: 11AM - 3PM
Online Short-Term Training Program on Machine Learning with Python
The online short-term training program on Machine Learning with Python is designed for students, researchers, and working professionals who want to build strong skills in data science and artificial intelligence. This program offers clear explanations, hands-on practice, and real-world project experience. As a result, participants develop both theoretical knowledge and practical confidence in machine learning techniques.
Moreover, the program follows a structured progression, starting from the basics and gradually moving toward advanced concepts. Therefore, even beginners with basic Python knowledge can follow the learning path smoothly.
Program Overview
Throughout the program, participants explore essential topics in data science, machine learning, and AI. They move step-by-step from understanding simple concepts to applying advanced algorithms. In addition, practical demonstrations help bridge the gap between theory and real-world use.
Program Duration: 24 November – 4 December 2025
Mode: Online
Maximum Seats: 150
Registration Fee: ₹1000
Since seats are limited, early registration is strongly recommended.
Core Learning Modules
Introduction to Data Science and Machine Learning
First, this module explains what data science is and why it matters in today’s digital world. It also compares machine learning, data science, and artificial intelligence. Furthermore, participants explore real-life applications and understand the main roles played by data professionals.
Python Essentials for Data Analysis
Next, participants learn the essential Python libraries used in machine learning. These include:
- NumPy for numerical computing
- Pandas for managing data
- Visualization tools for exploring trends
- Scikit-Learn for building models
As a result, learners gain the ability to clean, analyze, and prepare data for machine learning tasks.
Probability Concepts for Machine Learning
After that, this module introduces basic probability concepts such as random variables, probability distributions, and Bayes’ Theorem. These ideas are important because they help in understanding how models make predictions.
Regression Techniques
Then, participants study different regression models, including:
- Univariate linear regression
- Multivariate linear regression
- Polynomial regression
Through examples, they learn how to predict values such as house prices and future trends.
Classification Algorithms
In this section, the focus moves to classification. Participants understand how models separate data into different categories using:
- Logistic regression
- Support Vector Machines (SVM)
- Decision trees
- Naïve Bayes
Consequently, they learn how machine learning helps in areas like medical diagnosis and spam detection.
Ensemble Learning Approaches
To improve accuracy, the program introduces ensemble methods. These include:
- Bagging
- Random Forest
- AdaBoost
- Gradient Boosting
By combining multiple models, participants see how prediction performance improves significantly.
Optimization Methods
Participants also learn optimization techniques such as:
- Gradient descent
- Stochastic gradient descent
- Batch gradient descent
These methods help models learn faster and perform better.
Clustering Techniques
In addition, the program covers clustering methods used in unsupervised learning. These techniques group similar data points, which helps in customer segmentation, image processing, and pattern recognition.
Feature Engineering
After clustering, participants study feature engineering. They learn techniques such as:
- Feature scaling
- Feature selection using filter methods
- Wrapper and embedded techniques
These steps improve model accuracy and efficiency.
Dimensionality Reduction
Furthermore, advanced techniques like PCA, LDA, and MDA are introduced. These methods reduce data complexity while keeping important information.
Neural Networks and Deep Learning
In this module, participants learn how neural networks work. They understand the concept of neurons, layers, and backpropagation. As a result, they gain a foundation in deep learning.
Reinforcement Learning
Then, the program explains reinforcement learning, including:
- Markov Decision Processes
- Planning and control methods
- Real-world applications
This knowledge is useful for robotics, gaming, and automated decision-making systems.
Recommendation Systems
Finally, participants explore recommendation systems, including:
- Content-based filtering
- Collaborative filtering
- Hybrid approaches
- Matrix factorization
- Time series forecasting
These models are commonly used by platforms like streaming services and online stores.
Hands-On Learning and Project Development
One of the strongest features of the online short-term training program on Machine Learning with Python is its hands-on approach. Participants work on industry-focused projects such as:
- House price prediction using regression
- Diabetes prediction using logistic regression
- Customer churn prediction using ensemble models
- Image color compression using K-means
- Handwriting recognition using neural networks
- Self-driving simulation using Q-learning
Through these projects, learners strengthen their problem-solving skills and build an impressive portfolio.
Important Dates
- Last date for registration: 22 November 2025
- Selection announcement: 23 November 2025
- Program start date: 24 November 2025
Therefore, interested candidates should complete the process without delay.
Institutional Support
The program is supported by a highly respected technical institution known for academic excellence and research leadership. Since its establishment in 1959, the institute has contributed significantly to innovation and professional development. In addition, experienced faculty members guide participants throughout the training program.
Registration and Contact Details
Participants must complete the online registration form and submit the required fee.
Contact Person: Dr. Venkateswara Rao Kagita
Phone Number: 6281746931
If you have any questions, you can reach out for guidance.