Machine Learning

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

Course Curriculum

Module 1: Basic Stat

Data Types
Random Variable
Probability
Probability Distribution
Sampling Funnel
Measure Of central tendency

Module 2: Basic Stat Contd..

Measures of Dispersion
Expected Value
Graphical Techniques
Introduction to R
R Studio

Module 3: Python

Introduction to Python (Installation basic commands)

Module 4: Basic Stat Contd..

Python & R Contd…Skewness & Kurtosis
Box Plot

Module 5: Basic Stat Contd..

Normal Distribution

Module 6: Basic Stat Contd..

Sampling Variation
CLT
Confidence interval

Module 7: Hypothesis Testing

Intro to HT, 2-sample t test, 1-sample tests
Other parametric and non-parametric tests
By R Code

Module 8: Linear Regression

Scatter Diagram
Corr Analysis
Principles of Regression
Intro to Simple Linear Regression
Multiple Linear Regression

Module 9: Logistic Regression

Principles of Logistic regression
Multiple Logistic Regression
ROC curve
Gain chart

Module 10: Disc Prob Distribution

Binomial
Neg Binomial
Possion

Module 11: Adv Regression

Poission
Neg Binomial
Models with Excessive ‘0’s

Module 12: Multinomial Regression

Multinomial Regression

Module 13: Supervised - Classifiers

KNN
Naïve Bayes
Decision Tree & Random Forest
Bagging and boosting

Module 14: Supervised - Black Box

ANN & SVM

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.