Machine Learning
Machine Learning
Start date
May 2023
Duration
10 Weeks
Enrol now
Book your seat
What is this course about?
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).
What is this course about?
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).
Learning Outcomes:
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.
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.
Become an Machine Learning
Expert in just 10 weeks
Become an Machine Learning
Expert in just 10 weeks
Course Curriculum
Here is what's included in this Machine Learning 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
Course Curriculum
Here is what's included in this Machine Learning 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
Entry Requirements
Basic knowledge of Python programming language is required for this course
Meet Our Trainers
Our experienced Machine Learning expert are here to help you for your successful career
Programming languages and tools
Benefits of the course
Conduct specialized research to advance current technologies
Execute Industry-specific data mining and data analysis to create more value
Understand Software design and information architecture
