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All Levels
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26 Weeks
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MIT Certification
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Industry Immersion
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Capstone Projects
Overview
Our Machine Learning Course focuses on building strong fundamentals in AI, data modeling, and predictive analytics. Gain hands-on experience with real datasets, develop intelligent models, and understand how machine learning is applied across industries.
- Machine Learning Engineer
- Junior Machine Learning Engineer
- Machine Learning Developer
- Machine Learning Intern
- Data Scientist
- AI Engineer
- Machine Learning Consultant
- Research Assistant (Machine Learning)
Targeted Job
Roles
Training and Methodology
Secure your exclusive access by enrolling in this course -
Hands-On Learning - Work on real datasets and build practical ML models.- Real-World Projects - Develop machine learning solutions for real industry use cases.
- Expert Mentorship - Learn from industry professionals and improve your model-building skills.
Why Choose This
Course?
Become a Skilled Machine Learning Professional
Start your journey into AI with our Machine Learning Course. Designed for beginners and aspiring professionals, this course covers core concepts. With hands-on projects, a structured curriculum, and expert guidance, you’ll gain practical experience and the confidence to apply machine learning in real-world scenarios.
Register Now-
Placement Assistance Program
Get career support with resume building, interview preparation, and job guidance.
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Real-World ML Projects
Work on real datasets and build machine learning models for practical applications.
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Mentorship & Feedback
Receive expert guidance, model reviews, and continuous feedback to improve your skills.
Skills acquired from Machine Learning Course
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Strong understanding of Machine Learning concepts, workflows, and real-world applications.
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Perform data preprocessing, EDA, handling missing values, and outlier detection.
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Build and evaluate regression models including Linear, Multiple, and Polynomial Regression.
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Apply classification techniques like Logistic Regression, KNN, SVM, Naive Bayes, and Decision Trees.
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Understand model evaluation metrics like MAE, MSE, RMSE, R2 Score, Precision, Recall, and F1 Score.
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Handle overfitting using techniques like Cross Validation, Ridge, and Lasso Regularization.
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Work with Ensemble Learning methods like Random Forest, AdaBoost, and Gradient Boosting.
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Apply clustering techniques like K-Means and Hierarchical Clustering for unsupervised learning.
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Build and deploy machine learning models using Flask for real-world applications.
Tools & Technologies Covered In This Course
The Course Syllabus
The course covers important topics.
Introduction to Machine Learning
- What is Machine Learning
- Applications of Machine Learning
- Supervised Vs Unsupervised Machine Learning
- Regression vs classification
Exploratory Data Analysis (EDA)
- Introduction to MongoDB
- Detecting and removal of outliers
- Feature scaling – : Standardization and normalization
Introduction to Linear Regression
- What is regression?
- What is linear regression?
- Building First ML model for marks prediction
- Simple linear regression
- Multiple Regression
- Polynomial Regression
- Error functions in Regression (MAE, MSE, RMSE)
- Calculating accuracy using R2Score
Introduction to Overfitting and underfitting
- Overfitting Vs underfitting
- Bias-Variance Tradeoff
- Regularization Techniques -: Ridge and Lasso
- Understanding and demonstrating Ridge and lasso regression techniques
- Cross Validation Techniques
Introduction to Logistic Regression
- Sigmoid function
- Understanding parameters of logistic regression
- ROC AUC Curve
- Confusion Matrix -: Precision, Recall, accuracy, f1 Score
Introduction to KNN
- Understanding working of K – Nearest Neighbors
- Advantages and drawbacks of using KNN
- KNN for regression
Introduction to SVM
- Understanding Support Vector Machine
- Hard and soft margin
- Understanding Support Vectors , Hyperplane
- Kernel technique
- SVM for regression
Naive Bayes Classifier
- Understanding Naive Bayes Theorem
- Introduction to text classification
- NLP pipeline
- Vectorization of text data
- Case Study -: Spam mail classification using naive bayes
Decision Tree classifier
- Working of DT
- Gini Index and Entropy
- Pruning techniques
- Advantages and disadvantages of Decision Tree
- Decision Tree for regression
Introduction to Ensemble learning
- What is Bagging?
- Random Forest Classifier
- ADA Boost, XGboost, Gradient Boost
- Unsupervised Machine Learning Algorithm
Project deployment using Flask Framework
- Clustering
- K-means Clustering
- Hierarchical clustering
- Association rules
- PCA (principle component analysis)
Projects & Case Study
- CASE STUDY ON BREAST CANCER DETECTION USING CLASSIFICATION ALGORITHMS
- CASE STUDY ON FRAUD DETECTION USING CLASSIFICATION ALGORITHMS
Ready to build
real-world
ML skills?
Step into the world of Machine Learning with expert-led training, hands-on projects, and career-focused guidance in Thane. Book your free demo today!
Book Free Demo
Recruiters looking for Machine Learning Students
Certification For This
Course
Enhance your career with our Machine Learning Course. Gain practical experience through real-world projects and earn a recognized Machine Learning Certification.
Register Now
Get in touch today
What Our Students Say
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Personal Training from experienced faculty helped me to gain proper knowledge and skills.
Nikita Akhade
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Enrolling at MIT was a game-changer for me. I secured a job placement in just 8 months!
Ameya Rasam
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Joined MIT for Master's on my friend's suggestion, and it was the best decision i ever made.
Pratik Dubal
Frequently Asked Questions
Get answers to all your questions about our Machine Learning Course, including syllabus, hands-on projects, certification, and career support. Understand the complete learning path and confidently begin your journey into AI and data-driven technologies.
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Who can enroll in the Machine Learning Course?
Anyone who has completed 10th, 12th, or graduation can enroll. This course is ideal for students from BE, BTech, BCA, MCA, MSc, BSc IT, or anyone interested in AI and Machine Learning.
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What will I learn in this course?
You will learn core Machine Learning concepts including data preprocessing, regression, classification, clustering, model evaluation, and deployment using real-world datasets and tools like Python and Scikit-learn.
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Does the course include practical projects?
Yes, the course includes hands-on projects such as car price prediction, spam detection, and fraud detection to help you apply machine learning concepts in real-world scenarios.
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Will I receive a certificate after completion?
Yes, upon successful completion, you will receive a Machine Learning Certification from Milestone Institute of Technology, validating your skills and knowledge.
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Do you provide placement support?
Yes, we offer placement assistance including resume building, interview preparation, and job guidance to help you start your career in Machine Learning and AI.
