Machine Learning Training by Experts
Our Training Process
Machine Learning - Syllabus, Fees & Duration
Module 1 : CORE PYTHON
- Short history
- Introduction
- Features of Python
- Python2 Vs Python 3
- Python Installation
- Python Interpreter
- How to Run Python
- Basic Syntax
- Python Identifiers, Keywords and Indentation Rules
- Type Checking
- Input, Output, Variables, Data Type and Datatype Casting
Module 2 : MACHINE LEARNING
- Data Analysis
- Data Visualization
- Data Classification
- Supervised Learning Unsupervised Learning
Module 3 : SUPERVISED LEARNING
- Classification
- K-Nearest Neighbours
- Decision Tree
- Naive Bayes
- Logistic Regression
- Support Vector Machine
- Random Forest
- Logistic Regression
- Regression
- Single Linear Regression
- Multiple Linear Regression
Module 4 : UNSUPERVISED LEARNING
- Clustering
- Hierarchical Clustering
- KMeans Algorithm Association
Module 5 : DATA PREPROCESSING
- PCA
- Dimensionality reduction
- Correlation
- Features Extraction Algorithm
This syllabus is not final and can be customized as per needs/updates
- Since 2001, Making IT Experts by Experts
- Duration: 40 Hours (Vary as per your skill)
- Course Fees
- Individual Live Classes (1:1)
- Batch Classes @ Low Fees
- Training on Your Time, Any Where
- Access to Recorded Videos
- Practical Internship on Projects
- 100% Placement Support by our jobsNEAR.in
- Training/Internship Certificate
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Course Highlights
Featured Courses
Check out our NESTSOFT courses in Charlottetown if you're interested in learning more about Machine Learning.
You'll also have the opportunity to work as a data scientist, Machine Learning engineer, or data engineer for several years and learn from industry specialists. By enrolling in NESTSOFT machine learning classes, you will gain exposure to industrial projects or machine learning certification from a specific area.
An overview of artificial intelligence and machine learning, fundamental principles for machine learning, data pre-processing, feature extraction, regression, logistic regression, nave Bayes, decision trees, kernel methods, and support vector machine and k-means and hierarchical clustering are among the topics covered in this course. You'll need data training capabilities, algorithm basics, advanced, automation, and iterative processes, ensemble modeling, and scalability to build a strong ML (machine learning) system. Candidates will acquire the fundamental concepts and intuition underpinning modern machine learning algorithms, as well as a more formal knowledge of how, when, and why they work, in this course. The student will be able to create and apply pattern classification algorithms to categorize multivariate data, create and apply regression algorithms to uncover correlations between data variables, and use reinforcement learning methods to operate complicated systems after finishing the course. Image recognition, speech recognition, traffic prediction, product recommendations, self-driving cars, and other applications of machine learning are just a few examples.
Machine Learning Engineer, Data Architect, Data Manager, Machine Learning Specialist, and more job profiles are available to machine learning certification holders. Anyone who completes this course can expect a typical salary increase of 48% and be hired by one of our 100+ hiring employers.
