Machine Learning

Machine learning is a subset of Artificial Intelligence. Machines have the capability of learning on their own based on observations and experiences when huge data is fed to interpret. Machine Learning uses Deep learning in combination with statistical tools and models to train machines to build a self-learning capability.  

Why should you learn this course?

  • Machine Learning Engineers are one of the highly paid Engineers in the Industry.

  • Machine learning enables machines to learn and improve the algorithms on their own thus eliminating human intervention.

  • Every domain has a huge scope of applying machine learning which opens many opportunities for Machine Learning Data Scientists in every domain.

Supervised Learning

Linear Regression

Multiple Linear Regression

Logistic Regression

Unsupervised Learning

Clustering and Principal Component Analysis

NLP in Python

Data Visualization with Matplotlibs

Web Scrapping

Machine Learning Modelling flow

How to treat data in Machine Learning

Naive Bayes Classifier

Performance Measures

Overfitting and Underfitting

Optimization Techniques

Time series Forecasting

Dimensionality reduction

K-nearest Neighbour

Decision Tree

Support Vector Machine

Image Detection and Image Analysis

Ensemble Learning Models

ANN overview

Obtaining a certification in Machine Learning holds several advantages. It validates your expertise and knowledge in the field, making you more competitive in the job market. Certification demonstrates your commitment to continuous learning and showcases your ability to apply ML concepts to real-world problems, making you an attractive candidate for employers seeking skilled professionals in this rapidly evolving domain.
A certification in Machine Learning can significantly enhance career prospects. It provides you with a recognized credential that can open doors to various roles, such as Machine Learning Engineer, Data Scientist, or AI Researcher. Employers often prioritize certified individuals during recruitment, as it assures them of your competence and practical skills in working with machine learning algorithms, data analysis, and model deployment.
A comprehensive Machine Learning certification program usually covers a range of topics. These may include fundamentals of supervised and unsupervised learning, deep learning architectures, feature engineering, model evaluation, optimization techniques, and real-world case studies. Additionally, topics related to ethics, bias mitigation, and responsible AI may be included to emphasize the importance of ethical considerations in machine learning applications.
Prerequisites for enrolling in a Machine Learning certification program can vary. However, a foundational understanding of programming (Python is commonly used in ML), statistics, linear algebra, and calculus is often recommended. Some programs may require prior experience with data analysis or coding, while others provide introductory modules to bridge any knowledge gaps.
Reputable Machine Learning certification programs are designed to stay up-to-date with the evolving nature of the field. They often incorporate regular updates to course content, reflecting the latest advancements in algorithms, tools, and industry practices. Additionally, many certification providers encourage ongoing learning through alumni networks, webinars, and access to resources, ensuring that certified individuals can stay current in the dynamic landscape of Machine Learning.
Enrolled: 36 students
Level: Beginner

Working hours

Monday 9:30 am - 7.30 pm
Tuesday 9:30 am - 7.30 pm
Wednesday 9:30 am - 7.30 pm
Thursday 9:30 am - 7.30 pm
Friday 9:30 am - 7.30 pm
Saturday 10:00 am - 7.00 pm
Sunday 10:00 am - 7.00 pm