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Machine Learining(ML) Training in Hyderabad
Machine learning training has a huge demand in the market. Machine learning is a branch of artificial intelligence (AI) where the machine learns from the data provided and experience. Machine learning courses in the market have evolved over the past decade, where people from different job roles try to get skilled in machine learning to enhance their future in this growing technology.
21 Modules
with Certifications
Certificate
After Completion
English
Language
Would you like to embark on the evolutionary journey of artificial intelligence? For that look here comes the version it’s machine learning training in Hyderabad. With today’s speedy technological environment, being good at machine learning is no longer optional but compulsory. The course is very carefully tailored to equip people with what they need to survive in the changing AI universe.
A subset called machine learning allows systems to acquire knowledge through experience automatically without programming instructions. Machine Learning Training for IT is designed for both novices and experts while offering an easy-going experience. Industry specialists draft it concentrating on practical practice and application of knowledge.
Course Features:
There is a Hyderabad-based course that involves live sessions, project collaboration work, and onsite practical tests to strengthen theoretical ideas. Supervised learning, deep learning, and neural networks are just a few of them. With this program, the participants will master today’s most popular machine learning tools. This course is led by experienced professionals and industry experts who serve as advisers, guides, and consultants. Practical projects mimic industry challenges which help students in competing with other graduating students for placement opportunities.
Outcomes:
Participants taking the Machine Learning course will attain a high level of competence in several machine learning algorithms including tools and frameworks. It will also advance careers in data science, artificial intelligence, and machine learning domain while getting recognized by an established institution. Furthermore, version IT graduates will get networking
Join Version It’s Machine Learning Training in Hyderabad and discover the new world of machine learning. Upgrade your capabilities, advance your career, and stay ahead of the game in today’s era of artificial intelligence. Come today and be part of a unique learning journey towards success.
Topics You will Learn
Machine Learning Introduction
- Supervised and Unsupervised Learning
- Linear Regression Theory
- Linear Regression Programming with Working on Case Study
Multiple Linear Regression
- Theory behind multiple linear regression
- Multiple Linear Regression with R
- Working on Case Study
Decision Tree
- Theory Behind Decision Tree
- Decision Tree with R
- Working on Case Study
Naive Bayes
- Theory behind Naïve Bayes classifiers
- Naive Bayes Classifiers with R
- Working on Case Study
Support Vector Machines
- Theory behind Support Vector Machines
- Support vector machines with R
- Improving the performance with Kernals
- Working on Case Study
Associate Rule
- Theory behind Association Rule
- Working on Case Studies
Expert Neural Net
- Artificial Neural Network
- Connection Weights in Neural Network
- Generating Neural Network with R
- Improving Neural Network Accuracy with Hidden Layers
- Working on Case
Random Forest
- Theory behind Random Forest
- Random Forest with R
- Improving performance of Random Forest
- Working on Case Study
Recommendation Engine
- Theory behind Recommendation Engines
- Working on Case Study with R
Dimension Reduction
- Theory behind Recommendation Engine
- Working on Case Studies
Dimension Reduction
- Theory behind Recommendation Engine
- Working on Case Studies
Machine Learning Algorithms
- Simple and Multiple Linear Regression
- KNN etc…
- Theory of Linear Regression
- Hands on with use Cases
Supervised Learning
- Simple and Multiple Linear Regression
- KNN etc…
Linear Regression and Logistic Regression
- Theory of Linear Regression
- Hands on with use Cases
Naive Bayes Classifier
- Naive Bayes for text classification
- New Articles Tagging
Unsupervised Learning
- K-means Clustering
Advanced Machine Learning Concepts
- Tuning with Hyper Parameters
- Popular ML Algorithms
- Clustering, Classification and Regression
- Supervised vs Unsupervised
- Choice of ML Algorithm
Random Forest – Ensemble
- Ensemble Theory
- Random Forest Tuning
Support Vector Machine (SVM)
- Simple and Multiple Linear regression
- KNN
Natural Language Processing (NLP)
- Text Processing with Vectorization
- Sentiment analysis with TextBlob
- Twitter sentiment analysis.
Artificial Neural Network (ANN)
- Basic ANN network for regression and classification
Tensorflow overview and Deep Learning Intro
Let Your Certificates Speak
- Certified in Machine Learning: Using Intelligence to Create Creative Solutions.
- Certificates are globally recognized & they upgrade your programming profile.
- Certificates are generated after the completion of course.
All You Need to Start this Course
- Take Introduction to Machine Learning if you're new to the field. Basic machine learning ideas are introduced in this brief self-study course.
- Learning Both Supervised and Unsupervised
Testimonials
Still Having Doubts?
Within the field of artificial intelligence (AI), machine learning is the process of creating models and algorithms that allow computers to learn and make decisions without explicit programming.
Three primary categories exist:
Supervised Learning: The input and output data for a labeled dataset are supplied to train the algorithm.
Unsupervised Learning: When an algorithm is given unlabeled data, it has to figure out any patterns or connections on its own without direct supervision.
Reinforcement Learning: The system picks up new skills through interacting with its surroundings and getting feedback in the form of incentives or punishments.
Principal Component Analysis (PCA), Hierarchical Clustering, K-Means Clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE) are examples of unsupervised learning algorithms.