Machine Learning Course Training in Bangalore
Interview Guarantee Program (IGP)*
Master Machine Learning with hands-on training in Python, Data Analysis, Supervised & Unsupervised Learning, Neural Networks, and AI Applications. Build industry-ready projects and gain practical skills for high-growth AI and Data Science careers.
1500+
Learners Trained
2,000+
Hiring Partners
Duration
40 Hours
Machine Learning Course Training in Bangalore
Interview Guarantee Program (IGP)*
Master Machine Learning with hands-on training in Python, Data Analysis, Supervised & Unsupervised Learning, Neural Networks, and AI Applications. Build industry-ready projects and gain practical skills for high-growth AI and Data Science careers.
1500+
Learners Trained
2,000+
Hiring Partners
Course Description
Machine Learning is one of the fastest-growing fields in Artificial Intelligence, enabling systems to learn from data, identify patterns, and make intelligent decisions with minimal human intervention. The NexEdge Machine Learning Program provides a strong foundation in data-driven problem solving, predictive analytics, and AI-powered applications through a practical, hands-on learning approach.
This program covers the complete Machine Learning lifecycle, including data preprocessing, exploratory data analysis, supervised and unsupervised learning, model evaluation, feature engineering, and deployment concepts. Learners will gain experience working with industry-standard tools and real-world datasets to build solutions that address modern business challenges.
Through live projects, case studies, and expert mentorship, participants will develop the skills required to pursue careers in Machine Learning, Data Science, Artificial Intelligence, and Advanced Analytics.

Industry Expert Trainers

Comprehensive ML Curriculum

Career-Focused Learning

Hands-On Projects & Certification
Machine Learning Course Objectives
The primary objective of this program is to help learners understand how machine learning can be used to solve complex business and technical problems. Participants will gain knowledge of supervised and unsupervised learning techniques, data preprocessing methods, feature engineering, model optimization, and performance evaluation. The course also focuses on developing analytical thinking and problem-solving skills while providing hands-on experience with machine learning tools and frameworks. Upon completion, learners will be equipped to build intelligent solutions, analyze large datasets, and contribute to AI-driven projects across various industries.
Training Requirements
This course is suitable for individuals with basic computer knowledge and an interest in data, analytics, or artificial intelligence. While prior programming experience can be beneficial, the curriculum is structured to support both beginners and professionals looking to advance their skills. A basic understanding of mathematics and logical reasoning will help learners grasp machine learning concepts more effectively
Who Should Attend This Course?
- Students and fresh graduates looking to build a career in Machine Learning and Artificial Intelligence.
- Software developers interested in integrating AI and Machine Learning into applications.
- Data analysts seeking to enhance their skills in predictive analytics and data-driven decision-making.
- Business analysts who want to leverage Machine Learning for business insights and optimization.
- IT professionals planning to transition into Data Science, AI, or Machine Learning roles.
- Engineers and technology enthusiasts eager to explore intelligent systems and automation.
- Working professionals looking to upskill in one of the fastest-growing technology domains.
- Entrepreneurs and business leaders interested in applying Machine Learning to solve real-world business challenges.
- Anyone with a passion for data, analytics, and emerging AI technologies.
Contact Our Team of Experts
Course Curriculum
- Data Types
- Random Variable
- Probability
- Probability Distribution
- Sampling Funnel
- Measure Of central tendency
- Measures of Dispersion
- Expected Value
- Graphical Techniques
- Introduction to R
- R Studio
- Introduction to Python (Installation basic commands)
- Python & R Contd...Skewness & Kurtosis
- Box Plot
- Normal Distribution
- Sampling Variation
- CLT
- Confidence interval
- Intro to HT, 2 sample t test, 1 sample tests
- Other parametric and non parametric tests
- By R Code
- Scatter Diagram
- Corr Analysis
- Principles of Regression
- Intro to Simple Linear Regression
- Multiple Linear Regression
- Principles of Logistic regression
- Multiple Logistic Regression
- ROC curve
- Gain chart
- Binomial
- Neg Binomial
- Possion
- Poission
- Neg Binomial
- Models with Excessive '0's
- Multinomial Regression
- Naïve Bayes
- Decision Tree & Random Forest
- Bagging and boosting
- ANN & SVM
- Contact Form
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Connect with our team to learn more about courses, certifications, corporate training, and career-focused learning opportunities.
Email Support
info@avvnexedge.com
Business Hours
Monday – Saturday, 09:00 – 17:00