Machine Learning Training in Hyderabad The world’s most advanced training courses in Information Technology are based on future changing Technologies ruling the market. Machine learning is one of the best technologies, which is why people are getting their Courses done, on this advanced technology. What is the objective of Machine Learning Training The sole objective
Machine Learning Training in Hyderabad
The world’s most advanced training courses in Information Technology are based on future changing Technologies ruling the market. Machine learning is one of the best technologies, which is why people are getting their Courses done, on this advanced technology.
What is the objective of Machine Learning Training
The sole objective of getting trained in the Machine Learning Training program is to create opportunities for IT technology and professional career advancement.
There is an on-going trend worldwide being trained in Machine Learning as it is part of the advanced technology called Artificial Intelligence. It is adopted by most of the IT companies to create more chances of getting output with less or no error by the system itself.
Our Machine Learning course will create more opportunities by providing a successful boost to your career achievement.
Gratisol Labs’ Machine Learning Training in Hyderabad will enhance the knowledge of IT Professional, as hands-on training will be given. The individual will acquire knowledge in practical sessions with Machine Learning. The professionals of Gratisol Labs will handle each applicant as each of them gets to train on the right path of achieving a successful career with Machine Learning Training of Gratisol Labs.
So, if you are looking to upscale yourself with Artificial Intelligence Training, then Machine Learning training is the job oriented course you should take your admission ASAP. Give us a call to know more about our Machine Learning training courses.
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Linear Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Model Performance
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naïve Bayes Classification
- Decision Tree Classification
- Random Forest Classification
- Evaluating Classification Model Performance
- Basic of NLP
- Language preprocessing Techniques
- Auto summarizing the given text document
- K-Means Clustering
- K-mini Batch Clustering
- Hierarchical Clustering
8.Curve Smoothening Techniques
9.Association Rule Learning
11.Basics of Numpy and panda
- Basics/what is Deep Learning
13.Artificial Neural Networks
14.Dimension Reduction Techniques
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Standard Deviation
- Pearson Correlation Coefficient (PCC)/ Correlation Coefficient