What is Machine Learning?
Machine learning is a powerful tool for making highly accurate and actionable predictions about your products, customers, marketing efforts, or any number of other applications. Learning with data, use statistical technique without being explicitly program.

Why to learn Machine learning? 

• There is a growing need among companies for professionals to know ins and outs of machine learning.
• The machine learning market size is expected to grow USD 1.03 Billion to USD 8.81 Billion by 2022.

Acquire new skills with our Machine Learning Certifcation Training Course

• Master in concepts of machine learning
• Recommendation engine and time series modeling
• Gain practical knowledge over principles, algorithms, and applications of machine learning.
• Hands-on approach includes working on projects and hands-on exercises.
• Thorough knowledge on statistical approach.
• Validate machine learning models and decode various accuracy metrics. 
• Improve in models using optimization algorithms, include Boosting & Bagging techniques.
• Thorough knowledge on theoretical & fundamental concept & how they can relate each other with practical approach?

Course summary
In this course candidate learn how to automate data and doing analysis, how to enable computers to learn and adapt, to do specific tasks without explicit programming. Introduction to Machine learning, Artificial Intelligence and their usage in practical real world. The various theories used in modeling and tools for data and programming, types of machine learning, plotting and visualizing, statistical methods, data modeling, decision trees, clustering models, basics and real life examples of neural networks. After completion of training candidate has proficient knowledge on concepts of machine learning including supervised and unsupervised learning, algorithms, support vector machines, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.

Machine Learning Certification Training Course & Curriculum

Introduction to Machine learning

• What is machine learning?
• History of machine learning
• Uses of machine learning

Types of machine learning

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Transfer learning

Tools for machine learning

• Programming languages
• Data repositories
• Hierarchical databases
• Software used

Basics of Python programming

• Installing Python
• Matrix operations
• Data loading/unloading
• Plotting and visualizing
• Algorithms - Predicting and modeling

Statistical methods

• Graph theory
• Probability
• Bayes theorem
• Regression models

Data modeling - Linear regression

• Model representation
• Cost function
• Gradient descent for linear regression

Data modeling - Logistic regression

• Hypothesis representation
• Decision boundary

Decision trees

• Basics of decision trees
• Uses for decision trees
• Advantages and limitations
• How decision trees work

Decision trees example

• Create a decision tree
• Requirement
• Training the data

Classifiers & Support vector machines

• Classifiers
• Support vector machines
• Linear and non linear classification
• What are SVM?
• Where are SVM used?

Association rules learning

• What is ARL?
• Where are ASL rules used?
• Support, Confidence, lift and conviction

Clustering

• What is clustering?
• Where is clustering used?
• Clustering mode

Clustering K means Model example

• Preparing the data
• Workbench method
• Command-line method
• Coded method

Basics of neural networks

• Introduction to Neural Networks
• Why Study Neural Networks?
• Real life examples of neural network

Types of neural networks

• Perception
• Recurrent neural networks
• Convolution neural network

Additional topics

• Evaluating Model Performance
• Improving Model Performance
• Similarity between R and Python
• Specialized Machine Learning Topics

Student Take away

• Study Material
• Learning stuff
• Sample project for practice

Class Delivery

• Live Interactive classes with expert

Delivery Methodology

• We are using an experiential delivering methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject or course.