Machine Learning Course in Delhi
Artificial intelligence and data-driven technologies have rapidly gained momentum among students and professionals aiming for careers in AI and data science. A Machine Learning course equips learners with the skills required to build predictive models, automate decision-making, and solve complex business problems using data. Many institutes in Delhi now offer industry-aligned Machine Learning programs designed to meet current and future AI job market demands.
- English
- English, Hindi
Upcoming Batch Weekdays!!!
Starting from Upcoming Weekend!
09:00 am – 1:00 pm Weekends
Fully Interactive Classroom Training
- 90 Hours Online Classroom Sessions
- 11 Module 04 Projects 5 MCQ Test
- 6 Months Complete Access
- Access on Mobile and laptop
- Certificate of completion
65,000 Students Enrolled
What we will learn
- Intelligence & Machine Learning
- Python for Machine Learning
- Data Preprocessing
- Supervised Algorithm
- Unsupervised Learning Algorithms
- Model Evaluation and Optimization
- Ensemble Learning Techniques
- Deep Learning Technologies (DLTs)
- Model Deployment Fundamentals
Attend our Machine Learning course in Delhi and gain hands-on expertise in designing, evaluating, and deploying machine learning models. Starting from Python fundamentals and data handling before moving onto advanced machine learning algorithms such as model tuning for real world scenarios.
Who Can Go for a Machine Learning Course in Delhi
Our Machine Learning course in Delhi is suitable for students, engineering graduates, working professionals, data analysts, software developers and those aspiring to a career in AI. While prior knowledge in mathematics, statistics or programming is beneficial but all essential concepts will be covered from scratch.
Want to Discuss Your Roadmap to Become a Machine Learning Engineer in Delhi?
Do You Require Expert Guidance on Skills, Projects, Certifications & Interview Prep? – Wanting To Discuss The Road Map To Becoming One Now.
Mentors at Our Mentors assist you with creating a tailored roadmap–from foundational to advanced projects and career placement assistance–that ensures job-readiness for Machine Learning roles. For your convenience, unlimited batch access, expert trainers and shareable certification are just a few advantages you will receive with learning anywhere! When scheduling a Call Back from One of the above Mentors is available.
Advantages
Countless Batch Access
Industry Expret Trainers
Shareable Certificate
Learn from anywhere
Career Transition Guidance
Real-Time Projects
Industry Endorsed Curriculum
Interview Preparation Techniques
Class recordings
Course Mentor
Kushal Dwivedi
- 10 + Batches
- 4.8 Star Rating
- 859 Students Trained
- 450+ Successfully Placed
Hi, I’m Kushal Dwivedi, and I’m excited that you’re here.
Professionally, I am a Data Engineering mentor with strong industry exposure and hands-on experience in building scalable data solutions. I have successfully delivered 10+ batches and trained 859+ students, helping them understand data engineering concepts from fundamentals to advanced levels. With a 4.8-star rating and 450+ successful placements, I focus on practical learning, real-time tools, and industry use cases. In this course, you’ll learn how I combine real-world experience with structured, step-by-step teaching to help you build job-ready data engineering skills.
Machine Learning Course Content
- Definition of Machine Learning
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
- Applications of Machine Learning
- Overview of the Machine Learning Workflow
- Linear Algebra Concepts
- Calculus Basics
- Probability and Statistics for Machine Learning
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
- Cross-Validation
- Hyperparameter Tuning Techniques
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Association Rule Learning
- Basics of Neural Networks
- Perceptron and Multilayer Perceptron
- Backpropagation Algorithm
- Introduction to Deep Learning
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transfer Learning
- Text Preprocessing
- Bag of Words Model
- Word Embeddings (e.g., Word2Vec, GloVe)
- Sentiment Analysis
- Image Processing Basics
- Convolutional Neural Networks for Image Classification
- Object Detection
- What is machine learning
- Different stages of ML project
- Supervised vs Unsupervised ML
- Algorithms in Supervised and Unsupervised learning
- Introduction to Sklearn
- Data preprocessing
- Scaling techniques
- Training /testing / validation datasets
- Feature Engineering
- How to deal with Categorical Variables – Dummy variables
- Categorical embedding
- Basics of Reinforcement Learning
- Markov Decision Processes
- Q-Learning and Deep Q Networks (DQN)
- Model Deployment Strategies
- Integration with Web Applications
- Model Monitoring and Maintenance
- Bias and Fairness in Machine Learning
- Ethical Considerations in AI
- Responsible AI Practices
What Our Students Say About Us
About Palin Analytics
Palin Analytics in Delhi is a premier analytics and AI training institute committed to closing the gap between academic learning and industry needs. Through hands-on instruction, live projects, expert mentorship and hands-on mentorship services, Palin Analytics prepares learners for successful careers in Machine Learning or Artificial Intelligence.
FAQ's
Both AI and Machine Learning professionals enjoy competitive salaries; AI specialists may earn slightly more due to broader system design expertise while Machine Learning engineers with strong model development and deployment abilities can often earn similar high wages.
Machine Learning consists of four major categories, Supervised Learning, Unsupervised Learning, Semi-Supervised Learning and Reinforcement Learning. Each form can be applied for different problems statements from classification and prediction to clustering and decision-making tasks.
 A top institute offering Machine Learning courses in Delhi provides an industry-align curriculum, hands-on projects, experienced trainers and strong placement support. Institutes that focus on real world applications such as live projects or interview preparation will deliver greater career success outcomes.
Machine learning comprises seven distinct steps, such as data collection and preparation; model selection; training and evaluation of models; hyperparameter tuning to optimize model deployment. Together these stages ensure effective development as well as real world usability of ML solutions.
Supervised Learning provides the ideal entry point into Machine Learning for newcomers as its use of labeled data makes it more easily interpretable. Algorithms like linear regression and classification help create strong foundational knowledge before moving on to more advanced ML concepts.