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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

  • Supervised and Unsupervised Learning
  • Linear Regression Theory
  • Linear Regression Programming with Working on Case Study
  • Theory behind multiple linear regression
  • Multiple Linear Regression with R
  • Working on Case Study
  • Theory Behind Decision Tree
  • Decision Tree with R
  • Working on Case Study
  • Theory behind Naïve Bayes classifiers
  • Naive Bayes Classifiers with R
  • Working on Case Study
  • Theory behind Support Vector Machines
  • Support vector machines with R
  • Improving the performance with Kernals
  • Working on Case Study
  • Theory behind Association Rule
  • Working on Case Studies
  • Artificial Neural Network
  • Connection Weights in Neural Network
  • Generating Neural Network with R
  • Improving Neural Network Accuracy with Hidden Layers
  • Working on Case
  • Theory behind Random Forest
  • Random Forest with R
  • Improving performance of Random Forest
  • Working on Case Study
  • Theory behind Recommendation Engines
  • Working on Case Study with R

Dimension Reduction

  • Theory behind Recommendation Engine
  • Working on Case Studies
  • Theory behind Recommendation Engine
  • Working on Case Studies
  • Simple and Multiple Linear Regression
  • KNN etc…
  • Theory of Linear Regression
  • Hands on with use Cases
  • Simple and Multiple Linear Regression
  • KNN etc…
  • Theory of Linear Regression
  • Hands on with use Cases
  • Naive Bayes for text classification
  • New Articles Tagging
  • K-means Clustering
  • Tuning with Hyper Parameters
  • Popular ML Algorithms
  • Clustering, Classification and Regression
  • Supervised vs Unsupervised
  • Choice of ML Algorithm
  • Ensemble Theory
  • Random Forest Tuning
  • Simple and Multiple Linear regression
  • KNN
  • Text Processing with Vectorization
  • Sentiment analysis with TextBlob
  • Twitter sentiment analysis.
  • Basic ANN network for regression and classification

Let Your Certificates Speak

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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.

The algorithm is trained on a labeled dataset including input-output pairs in supervised learning. Unsupervised learning involves providing unlabeled data to an algorithm, which it must use to find patterns or links without human intervention.
Neural networks, support vector machines, random forests, decision trees, and linear regression are examples of supervised learning algorithms.
Principal Component Analysis (PCA), Hierarchical Clustering, K-Means Clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE) are examples of unsupervised learning algorithms.

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