Learn Machine Learning with Future Finders
Machine Learning is one of the fastest-growing areas in technology. It helps computers learn from data and make predictions or decisions without being directly programmed for every task.
At Future Finders, we provide practical Machine Learning training for students, freshers, and working professionals. Our training focuses on learning concepts through examples, practice, and projects.
Best Machine Learning Training in Mohali
If you are looking for the Best Machine Learning Training in Mohali, Future Finders provides beginner-friendly and practical training.
You will learn Python, data handling, Machine Learning algorithms, model building, and project development. Our trainers explain each topic in simple language so students can learn step by step.
Best Machine Learning Training in Chandigarh
Future Finders offers the Best Machine Learning Training in Chandigarh for students who want to build a career in AI and Machine Learning.
The course includes practical exercises and real project work. Students get an opportunity to understand how Machine Learning is used in different industries.
Best Machine Learning Training in Gurdaspur
For students searching for the Best Machine Learning Training in Gurdaspur, Future Finders provides practical and career-focused training.
Students can learn Machine Learning from the basics and gradually move toward advanced topics and projects.
What You Will Learn
Python Basics
- Python programming
- Variables and data types
- Conditions and loops
- Functions
- Lists, tuples, and dictionaries
- Object-oriented programming
Data Handling
- NumPy
- Pandas
- Data cleaning
- Data analysis
- Data visualization
- Working with datasets
Machine Learning
You will learn important Machine Learning concepts such as:
- Supervised Learning
- Unsupervised Learning
- Regression
- Classification
- Clustering
- Model training
- Model testing
- Model evaluation
Advanced Machine Learning
The course also introduces advanced algorithms such as:
- Decision Trees
- Random Forest
- Support Vector Machine
- K-Means
- XGBoost
- Feature selection
- Model improvement
Deep Learning Basics
Students can also get an introduction to:
- Neural Networks
- TensorFlow
- Keras
- CNN
- Basic AI concepts
Practical Projects
Projects help students understand how Machine Learning works in real situations.
During training, students can work on projects related to:
- Data prediction
- Customer analysis
- Classification
- Recommendation systems
- Business data analysis
- AI-based applications
Tools and Technologies
The training covers commonly used tools and technologies such as:
Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, Jupyter Notebook, Git and GitHub.
Why Choose Future Finders?
- Simple and practical teaching
- Experienced trainers
- Hands-on practice
- Project-based learning
- Industry-focused curriculum
- Doubt-solving sessions
- Career guidance
- Training for beginners and freshers
Who Can Join?
This course is suitable for:
- College students
- Graduates
- Freshers
- IT professionals
- Python learners
- Anyone interested in AI and Machine Learning
You do not need advanced programming knowledge to start. Basic computer knowledge and an interest in learning technology are enough to begin.
Career Opportunities
After learning Machine Learning, students can explore roles such as:
- Machine Learning Developer
- AI Developer
- Data Analyst
- Junior Data Scientist
- Python Developer
- AI/ML Intern
Start Your Machine Learning Journey
Want to build your career in AI and Machine Learning?
Join Future Finders and learn Machine Learning through simple concepts, practical training, and projects.
Future Finders – Finding Future for You.
Introduction to AI and Machine Learning
Machine Learning
- Day 1: Setup Python, Jupyter, Colab, Git
- Day 2: Python practice for ML
- Day 3: NumPy practice
- Day 4: Pandas practice
- Day 5: Data cleaning lab
- Day 6: Visualization with Matplotlib/Seaborn
- Day 7: EDA project
- Day 8: Scikit-learn workflow, train/test split
- Day 9: Linear regression from scratch and sklearn
- Day 10: Multiple and polynomial regression
- Day 11: Regularization: Ridge, Lasso
- Day 12: Project: house price prediction
- Day 13: Logistic regression
- Day 14: KNN
- Day 15: Naive Bayes
- Day 16: Decision trees
- Day 17: Random forest
- Day 18: SVM
- Day 19: Gradient boosting, XGBoost, LightGBM
- Day 20: Project: classification (churn/spam)
- Day 21: Confusion matrix, precision, recall, F1
- Day 22: ROC-AUC and threshold tuning
- Day 23: Cross-validation
- Day 24: GridSearch and RandomSearch
- Day 25: Feature scaling, encoding, selection
- Day 26: Imbalanced data handling
- Day 27: Sklearn pipelines
- Day 28: K-means clustering
- Day 29: Hierarchical clustering and DBSCAN
- Day 30: PCA
- Day 31: Project: customer segmentation
- Day 32: Recommendation system (content/collaborative)
- Day 33: Anomaly detection
- Day 34: Time-series forecasting basics
- Day 35: Neural networks with Keras
- Day 36: CNN image classification
- Day 37: NLP text classification
- Day 38: Save and load models
- Day 39: Flask/FastAPI model API
- Day 40: Streamlit ML web app
- Day 41: Capstone: problem and data collection
- Day 42: Capstone: EDA and preprocessing
- Day 43: Capstone: modeling and tuning
- Day 44: Capstone: deployment
Day 45: Final demo, GitHub, resume
- Week 1: Python, NumPy, Pandas lab
- Week 2: Visualization and EDA project
- Week 3: Regression models and regularization
- Week 4: Classification: logistic, KNN, Naive Bayes
- Week 5: Trees, random forest, SVM
- Week 6: Boosting and model evaluation
- Week 7: Tuning, pipelines, imbalanced data
- Week 8: Clustering and PCA projects
- Week 9: Recommendation systems and anomaly detection
- Week 10: Time series and neural networks
- Week 11: Deployment with FastAPI/Streamlit
Week 12: Capstone project and demo
- Week 1: Python for ML
- Week 2: NumPy and Pandas
- Week 3: Visualization and EDA
- Week 4: Data preprocessing and feature engineering
- Week 5: Linear and polynomial regression
- Week 6: Regularization and regression project
- Week 7: Logistic regression, KNN
- Week 8: Naive Bayes, SVM
- Week 9: Decision trees and random forest
- Week 10: Boosting algorithms
- Week 11: Evaluation, cross-validation, tuning
- Week 12: Classification project
- Week 13: Clustering
- Week 14: PCA and dimensionality reduction
- Week 15: Recommendation systems
- Week 16: Anomaly detection and time series
- Week 17: Neural networks (Keras/PyTorch)
- Week 18: CNN projects
- Week 19: NLP projects
- Week 20: Intro to reinforcement learning (Gym lab)
- Week 21: Deployment: FastAPI, Streamlit, Docker
- Week 22: MLOps basics: MLflow, Git, CI
- Week 23: Capstone project
Week 24: Demo, portfolio, interview prep
Apply here
| Machine Learning Course Fee and Duration | |||
|---|---|---|---|
| Track | Regular Track | Weekend Track | Fast Track |
| Course Duration | 150 - 180 days | 28 Weekends | 90- 120 days |
| Hours | 2 hours a day | 3 hours a day | 6+ hours a day |
| Training Mode | Live Classroom | Live Classroom | Live Classroom |