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Machine Learning Training in Bangalore
Machine learning program will offer the student to implement machine learning course programs in Bangalore with real-time problem solving, directing further on the techniques and exercises than on the statistics behind these systems. The classes will begin with a discussion of how machine learning training certification in Bangalore is more diverse than detailed statistics, and begin the 360EduKraft tool kit during a tutorial. The issue of the dimensionality of data will be presented, and the responsibility of clustering data, as well as deciding those clusters, will be tackled. Supervised methods for creating auspicious models will explained, and students will be capable to implement the predictive modeling techniques while learning method effects associated to data generalizability.
The classes will complete with an expression at more high-level techniques, such as structure groups, and practical conditions of predictive patterns. By the end of this course, trainees will be able to recognize the difference separating a supervised and unsupervised method, distinguishing which method they require to implement for a particular dataset and demand, engineer highlights to satisfy that demand, and draft python code to take out an examination.
As we are one of the best Machine learning certification training institute in Bangalore, trained many students and supporting to after completion also. We provide machine learning certification course in Bangalore with real-time problems to make student to understand practical methods. We provide classroom training in different locations in Bangalore including RT Nagar, Electronic City, Koramangala, BTM Layout and Marathahalli.
What you will Learn in Machine Learning Course?
360Edukraft’s Machine Learning training course will make you a become master in machine learning, strategies we implemented and made practical with real-time projects that automate our students become expert in the concerned field and the method we applied in the computer for analysis to allow to read and confirm through practice to do special tasks without detail programming. You will understand machine learning theories and techniques including supervised and unsupervised training, scientific aspects, and hands-on experience to generate algorithms and provide you for the position of Machine Learning expert.
Key Features of Machine Learning Training Course
Machine Learning Training Certification program has actually constant applications in the coming future, as well as the very modern. Some of the key information you will receive within our Machine Learning Course are:
- Trending skills such as regression, classification, clustering, sci-kit learn, SciPy, and much more.
- 100% Placement Support
- Practical Experience in Real-Time Problems
- Receive Training from the Best Trainer over 10+ Years Industry Background
- Convert Machine Learning Expert Certified Professional
- Master the methods of Supervised & Unsupervised Techniques
Become A Machine Learning Certified Expert
Looking to Work on real-time projects and entire practical to get Professional Certification from 360EduKraft? Then Enroll Now to get the machine learning certification!
360EduKraft's Training Key Features:
Machine Learning with Python Course Description
Machine Learning has numerous applications in different sphere of technology, especially the ones where automation is imminent. Machine Learning with Python is already in great demand by most of the enterprises transitioning from manual to automated processes. Hence, to be future-ready, this course is vital for you; no more, no less.
– Anyone with basic knowledge of Python
– Any Python developer looking to expand their skillset.
– Learners familiar with concepts of Computer Science
Some of the notable advantages that you would gain by mastering Machine Learning with Python are:
– Strong fundamentals of Machine Learning.
– Advanced knowledge of Python
– Learn to combine Machine Learning with Python to accomplish automation tasks.
– Certified expertise in writing Machine Learning algorithms with Python.
Here are some of the perquisites for our certification course in Machine Learning in Python:
– Strong understanding of Mathematics
– A PC with Windows 7 and above/Mac OS X 10 and above, Intel i5 processor, minimum 4GB of RAM, and 3-5 GB of disk space.
How we standout among other training institutes ?
Machine Learning Online Training Curriculum
Machine Learning Course Topics
>> Introduction to Machine Learning
>> Introduction to Data Science
>> Introduction to Python
>> Iterative Operations & Functions in Python
>> Data summary & visualization in Python
>> Getting Started with Machine Learning
>> Back to Basics (Maths with Statistics)
>> Data Processing for Machine Learning
>> Advanced Machine Learning Algorithm
>> Case Study & Projects
>> Assignments
Topics:
- Key Elements of Data Science
- Data Warehousing
- Business Intelligence
- Data Visualization
- Data Mining
- Machine Learning
- Artificial Intelligence
- Cloud Computing
- Big Data
Topics:
- What is Artificial Intelligence & its importance
- Artificial Intelligence vs Machine Learning
Topics:
- What is Machine Learning (ML)?
- How machines learn
- Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement.
- Basics of Classification, Regression and Clustering algorithms
- Creating your first Prediction Model
- Training & Model evaluation
- Choosing Machine Learning algorithm
Topics:
- Linear Algebra (Vectors, Matrix, Eigen Values)
- Probability and Statistics
- Hypothesis testing
- Optimization
Topics:
- What is Python
- Working with Python
- Basic scripts on
- Read, write, data handling
- Loops
- Conditions (if-else)
- Function
- Code modularization
- Scikit-Learn package
- Basic visualization
Topics:
- Data Collection & Preparation
- Data Mugging
- Outlier Analysis
- Missing value treatment
- Feature Engineering
- Data Transformation
- Normalization vs Standardization
- Creating Dummies
- Dimensionality Reduction
- Principal Component Analysis
Topics:
- Supervised Machine Learning algorithms
- Linear Regression
- Logistic Regression
- Decision/Classification Tree
- Ensemble Models
- Bagging
- Boosting
- Random Forest
- K-Nearest Neighbours (KNN)
- Naive Bayes
- Neural Network (Deep Learning)
- Support Vector Machine
- Unsupervised Machine Learning algorithms
- Clustering with K-means Clustering
- Bias-Variance Trade off
- Regularization
- Parameter tuning & grid search optimization
Real life cases with Python
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