Machine Learning
Join India’s #1 leading Machine Learning Course Training and unlock the potential of AI-driven innovation. Master Machine Learning algorithms, data preprocessing, model evaluation, and cutting-edge AI techniques with industry experts. Gain hands-on experience through real-world projects and job-readiness training to kickstart your career as a Machine Learning Engineer. Stay ahead in the AI revolution with a curriculum designed for the future!
Why Choose Our Machine Learning Training?
Techtroma, a leading Edu-Tech company in Bengaluru, India, offers a comprehensive Machine Learning Certification Training designed for beginners and professionals. This course provides in-depth knowledge, practical experience, and real-world project exposure, equipping you with industry-relevant skills.
Note: This is a course completion certification program by Techtroma. We do not provide third-party or vendor-specific certifications.
What You Will Learn
Fundamentals of Machine Learning – Supervised & Unsupervised Learning
Python for Machine Learning – Data Processing & Libraries (NumPy, Pandas, Matplotlib)
Data Preprocessing & Feature Engineering – Handling Missing Data & Feature Selection
Model Building & Evaluation – Regression, Classification, Clustering
Deep Learning & Neural Networks – Introduction to TensorFlow & Keras
Real-World Projects – Hands-on experience with industry case studies
Who Should Enroll?
This training is perfect for:
- Students & Graduates aspiring for a career in AI & Machine Learning
- Software Developers looking to upskill in Data Science
- Data Analysts & Business Professionals aiming to leverage ML for insights
Enthusiasts with a passion for Artificial Intelligence & Predictive Analytics
Course Highlights
Live Instructor-Led Sessions – Learn from industry experts
Real-Time Projects – Hands-on training with case studies
Doubt-Solving Sessions – Interactive learning experience
Course Completion Certificate – Recognized by Techtroma
Job-Oriented Curriculum – Industry-focused training
Machine Learning Engineer Project Life Cycle
Phase 1: Problem Definition & Data Preparation
Define the business problem, objectives, and success metrics. Collect, clean, and preprocess data, handling missing values and outliers. Perform Exploratory Data Analysis (EDA) to identify patterns and relationships.
Phase 2: Model Development & Training
Select an appropriate machine learning algorithm (e.g., regression, classification, clustering). Split data into training and test sets, apply feature engineering, and optimize hyperparameters. Evaluate performance using metrics like accuracy, RMSE, or AUC-ROC.
Phase 3: Deployment & Monitoring
Deploy the trained model using APIs, cloud services, or embedded systems. Continuously monitor performance, track data drift, and retrain the model as needed. Maintain logs and dashboards for real-time insights and decision-making.