The Crash Course on Machine Learning by IIES has been designed for learners who want to quickly understand the real-world applications of Artificial Intelligence (AI) and Machine Learning (ML).
Machine learning is a vital branch of artificial intelligence that empowers computers to learn and make decisions from data without being explicitly programmed. With the rise of automation, AI-driven analytics, and embedded systems, learning machine learning is becoming an essential skill for every tech enthusiast in India.
This module is part of the PG Diploma in Embedded Systems Design & Development
Machine learning has transformed industries across India from healthcare and fintech to e-commerce and autonomous systems.
Its ability to analyse data, predict trends, and automate decisions makes it one of the most in-demand career paths today. Whether you are already pursuing an embedded systems course or exploring the best embedded course in Bangalore, mastering ML will help you stay ahead of the curve in both hardware and software domains.
Machine learning enables systems to automatically improve their performance with experience. Through this course, learners will explore how algorithms analyze data, detect hidden patterns, and make intelligent predictions.
In India, professionals trained in machine learning and embedded systems are highly valued by companies developing smart devices, IoT solutions, and AI-integrated applications. This makes it an ideal learning path for students and working professionals seeking job-ready skills in technology.
The IIES course covers both foundational concepts and advanced algorithms, empowering learners to build and deploy intelligent models effectively.
After completing this crash course, learners will:
Module 1: Introduction to machine learning and paradigms of ML
Module 2: PCA and Dimensionality Reduction, Nearest Neighbours and KNN
Module 3: Linear Regression and Logistic Regression
Module 4: Decision Trees and Random Forest
Module 5: Naïve Bayes Algorithm and Support Vector Machines
Module 6: Clustering algorithms: K-means, hierarchical clustering, DBSCAN
Module 7: Model Selection and Regularization
Module 8: Deep Learning: Introduction to ANN, CNN for image recognition, and RNN for sequence data.
Module 9: Reinforcement Learning: Markov Decision Processes (MDP) and Q-learning.
Each module has been structured with clarity, practical sessions, and real-time case studies to ensure in-depth understanding.
Machine learning provides numerous advantages to professionals and organizations alike:
When combined with skills from an embedded systems course, these benefits expand even further, allowing professionals to create intelligent hardware integrated with AI capabilities.
Machine learning impacts various industries across India and globally:
Upon completion, learners can explore exciting roles such as:
Professionals who already have experience or certification in an embedded course in Bangalore will find this program a perfect complement — enhancing both their AI and system design capabilities.
IIES (Institute of Industrial Embedded Systems) has established itself as a leading institute for AI, ML, and embedded technology training in India.
With a strong focus on hands-on learning, real projects, and placement guidance, IIES ensures that learners are industry-ready from day one. Its curriculum is constantly updated to align with the latest industry trends, ensuring that you receive one of the best embedded course and AI training experiences in Bangalore.
The Crash Course on Machine Learning by IIES bridges the gap between theory and application. With industry-relevant training, expert mentorship, and real-world projects, learners can confidently enter the growing fields of AI, data science, and embedded systems.
Whether you’re a student, an engineer, or a professional seeking upskilling, this course opens doors to the most future-ready career paths in India’s tech ecosystem.
Machine learning is a powerful tool that has the potential to transform various aspects of our lives.ML demonstrates its power in Handling Big Data, Complex pattern Recognition, Advanced Natural Language Processing, Automation and Efficiency, Personalization and Recommendations, Predictive Analytics and etc.
There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning entails training a model on labeled data in order to make predictions or categorize fresh data. Unsupervised learning entails detecting patterns or structures in unlabeled data. Reinforcement learning involves training an agent to make decisions based on rewards or punishments in a given environment.
Machine learning is a larger field that incorporates many algorithms and approaches for data-driven learning. Deep learning is a subfield of machine learning that focuses on learning data representations using artificial neural networks with several layers. Deep learning is especially useful for jobs like image and speech recognition.
Machine learning allows users to give massive amounts of data to a computer algorithm and have the machine analyze and make data-driven suggestions and decisions based solely on the input data.
Machine learning, in which a machine imitates human thinking by recognizing patterns and making predictions from data models, is being used in practically every industry. Indeed, machine learning examples abound, with applications ranging from healthcare and banking to marketing and sports.
No, machine learning is a subset of AI. AI encompasses a broader field focused on creating intelligent systems capable of performing tasks that would require human intelligence. Machine learning is a specific technique within AI that focuses on training models to learn from data and make predictions or decisions.
Yes, machine learning models can make mistakes. They learn from historical data, so if the data is incomplete, biased, or not representative, the models may make incorrect predictions or decisions. Regular monitoring, evaluation, and updating of models are necessary to identify and correct any mistakes or limitations.
In traditional programming, a programmer writes explicit instructions for the computer to follow. In machine learning, the computer learns from data and generates its own instructions based on that data. It allows the computer to learn and improve over time without being explicitly programmed for every possible scenario.
The future of machine learning is bright. As technology continues to develop, we can expect to see it being used in even more innovative and ground-breaking ways. machine learning is expected to be used to develop new drugs and treatments, diagnose diseases more accurately, and personalize medicine.
It is also expected to be used to predict financial markets, manage risk, and detect fraud.
