Machine Learning Online Live Class by Experts
Our Training/Internship 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 jobsHybrid.com
- Training/Internship Certificate
- Abbotsford
- Alberta
- Barrie
- Brampton
- British Columbia
- Burlington
- Calgary
- Charlottetown
- Delta
- Edmonton
- Gatineau
- Guelph
- Halifax
- Hamilton
- Kamloops
- Kelowna
- Kingston
- Kitchener
- Manitoba
- Mississauga
- Moncton
- Montreal
- Nanaimo
- New Brunswick
- Niagara Falls
- Nova Scotia
- Ontario
- Oshawa
- Ottawa
- Peterborough
- Prince George
- Quebec
- Regina
- Saint John
- Saskatoon
- Sherbrooke
- Surrey
- Thunder Bay
- Toronto
- Vancouver
- Victoria
- Waterloo
- Whitehorse
- Windsor
- Winnipeg
Course Highlights
Our Locations
We live in a world surrounded by humans who can study everything using their abilities and learning abilities, as well as machines that follow our directions. Can a machine, like a human, learn from skills or previous data? So here's where Machine Learning comes in. 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. 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. As a result of the increased demand, experts have been able to land the highest-paying positions. Machine learning is the most in-demand position in the information technology industry right now.
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. Image recognition, speech recognition, traffic prediction, product recommendations, self-driving cars, and other applications of machine learning are just a few examples.
Machine learning is the study of computational algorithms that can automatically improve witpracticese and is implemented as part of artificial intelligence. 
