Machine Learning
Designed for
- Learners
Start Date
Spring 2021
Price
$500
Duration
2 classes per week for 10 weeks
Start Date
Spring 2021
Price
$500
Duration
2 classes per week for 10 weeks
The purpose of this course is to enable learners to develop an in-depth understanding of the machine learning algorithms, principals, and techniques with a special focus on the underlying mathematics and statistics. This course will enable the learners to develop hands-on skills in the implementation of various machine learning algorithms. The learner will be able to analyze a real-world problem, design a solution and implement it using an appropriate machine learning algorithm(s).
On successful completion of this course the student will be able to:
- Understand and appreciate the underlying issues and challenges of machine learning algorithms for implementation and application in challenging real-world scenarios.
- Analyze and relate the mathematical and statistical background of machine learning models for finding optimal solutions for the industry.
- Implement various supervised and unsupervised machine learning algorithm for dynamic real-world problems.
- Evaluate the results of a model and conclude the possible steps for evolving them into a usable application.
Course Name
Class Timings
Course Location
Course Level
Assessment
Accreditation/Awarding Body
Machine Learning
2 classes per week for 10 weeks
Online/Blended
Professional/ Expert Level
Not applicable
ICE
Basic knowledge of Python programming language is required for this course
Course Name
Machine Learning
Class Timings
2 classes per week for 10 weeks
Course Location
Online/Blended
Course Level
Professional/ Expert Level
Assessment
Not applicable.
Accreditation/Awarding Body
ICE
Entry Requirements
Basic knowledge of Python programming language is required for this course
Topics
The list below provides a overview of the topics covered in this course.
- Supervised learning
- Unsupervised learning
- Cost function
- Gradient Descent (single and multi-variables)
- Normal equation
- Classification (Binary)
- Hypothesis representation
- Decision Boundary
- Cost Function
- Optimization
- Classification (Multi-Class)
- Data fitting (over fitting vs under fitting)
- Regularized Linear Regression
- Regularized Logistic Regression
- Optimization
- Margin classification
- Kernels
- K-means
- Why to reduce the dimension of a problem
- Principal component Analysis
- Principal component selection (Number of PC)
- Gaussian Distribution
- Anomaly detection system
- Multivariate Gaussian distribution
- Why we need Neural networks (Non-Linear Hypotheses)
- Model Representation
- Multiclass Classification
- Back-propagation algorithm
- Random initialization
- Model selection
- Accuracy Measures(precision and recall)
- Content Based recommendation
- Collaborative filtering
- Vectorization
- Mean normalization
- Stochastic gradient descent
- Convergence of SGD
- Introduction to MapReduce
- Project work
Certificate
Upon completion of the Ethical Hacking & Penetration Testing course, you will also receive the certificate awarded by ICE
All certificate images are for illustrative purposes only and may be subject to change at the discretion of ICE.
Certificate
Upon completion of the Ethical Hacking & Penetration Testing course, you will also receive the certificate awarded by ICE
All certificate images are for illustrative purposes only and may be subject to change at the discretion of ICE.