Welcome to Machine learning Training @ Ether Infotech
Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications spanning from business intelligence to homeland security, from analyzing biochemical interactions to structural monitoring of aging bridges, and from emissions to astrophysics, etc. This class will familiarize students with a broad cross-section of models and algorithms for machine learning, and prepare students for research or industry application of machine learning techniques.
What you’ll learn
- Supervised learning (generative/discriminative learning, parametric/nonparametric learning, neural networks, and support vector machines)
- Unsupervised learning (clustering, dimensionality reduction, kernel methods)
- Learning theory (bias/variance tradeoffs; VC theory; large margins) and
- Reinforcement learning and adaptive control.
Popular models : Train/Test Split, Root Mean Squared Error, and Random Forests.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Syllabus
Module 1
- Introduction to Machine Learning
- Applications of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
Module 2
- Regression
- Linear Regression
- Non-linear Regression
- Model evaluation methods
Module 3
- Classification
- K-Nearest Neighbour
- Decision Trees
- Logistic Regression
- Support Vector Machines
- Model Evaluation
Module 4
- Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Density-Based Clustering
Module 5
- Recommender Systems
- Content-based recommender systems
- Collaborative Filtering
Regular Batches :
- 2 Months : Mon – Fri – 2 hrs per day
Weekend Batch :
- 2 Months : Saturday –Sunday – 5 hrs per day
Fast Track :
- 10 Days : Mon –Fri – 4 hrs per day
Who can study Python course :
- College Students / Graduates / Working professionals / Non IT Professionals who want to switch to IT
- By the end of the course, students should be able to: Develop an appreciation for what is involved in learning models from data.
- Understand a wide variety of learning algorithms.
- Understand how to evaluate models generated from data.
- Apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.
COURSE FEATURES
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Our preparation programs are adaptable and redone to ensure that each individual picks up the most extreme out of the preparation.
FAQ
Not a problem even if you miss a live Machine Learning session for some reason. Missed Class Will Be Provided.
- Classroom Training
- One To One Training
- Fasttrack Training
- Customized Training
- Live Instructor Classroom Training