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09 May 2022

Machine Learning Syllabus: A Brief Overview

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Skill-Lync

Machine Learning (ML) is one of the leading applications of Artificial Intelligence (AI) and it is now taking the world by storm and enabling computers to tackle jobs that have, until now, been carried out by human beings.

From decoding speech to driving cars, machine learning is forcing the abilities of artificial intelligence – to assist software make sense of the chaotic and unexpected real world. 

 

machine learning syllabus

 

Understand the Concept of Machine Learning

While many people often get confused between machine learning and artificial intelligence, machine learning refers to training a computer network on how to make concrete projections when fed data. Also, machine learning presents computers and modern machines with the power to understand and improve automatically without getting programmed explicitly. 

The machine learning process begins with data observations to look for similar patterns in the information and make more prudent judgments in the future based on the standards provided. Machine learning further involves examining massive amounts of data as its outcomes get delivered quickly and more accurately, assisting in determining threats or possibilities that yield profit. 

 

machine learning syllabus course

 

Being proficient in machine learning and artificial intelligence is imperative for developing modern business fields as algorithms in machine learning are challenging. It comprises inventiveness, experimentation, perseverance, and guidance from the best learning institute that will help students earn better job prospects. 

In addition, the machine learning online course in machine learning and artificial intelligence is designed for Engineers from all streams to get familiarised with essential mathematical conceptions and help clear their fundamentals of machine learning

 

Syllabus of Machine Learning Course

If you apply for a machine learning course on top online platforms like Skill-Lync, the course syllabus is divided into different modules to make learning effortless for the students. In addition, some of the core subjects that students learn in the machine learning course are as follows: 

  • Programming for problem-solving
  • Engineering physics.
  • Math.
  • Application-based programming with Python.
  • OOPS, using JAVA.
  • Data Structures using C.
  • Database Management System.
  • Pattern Recognition.
  • Computational learning theory.

Apart from this, the machine learning course syllabus is as follows: 

 

Basics of Probability and Statistics

It is the initial module where learners get introduced to the ideas of machine learning, statistics, and artificial intelligence. Also, they get familiarised with the following topics. 

  • Basics of Probability.
  • Basics of Statistics.
  • What are Machine Learning and Artificial Intelligence?

 

Basics of Machine Learning and Artificial Intelligence

In this module, students will begin with the concept of supervised learning and study the following topics. 

  • Introduction to the standard normal distribution and normal distribution.
  • Introduction to business moments.
  • Artificial Intelligence.

 

Supervised Learning - Prediction

In this supervised learning module, students learn the following topics. 

  • Introduction to supervised learning.
  • One hot encoding.
  • What is linear regression?

 

Cost Function and Gradient Descent Supervised Learning - Classification 

Under this module, students start with learning the classification algorithm.

  • Introduction to classification problems.
  • What is logistic regression?
  • Gradient descent and cost function.

 

Supervised Learning - Additional Classification Algorithms

In this machine learning course, students get introduced to some additional classification algorithms and the topics like: 

  • Decision tree.
  • Entropy.
  • Information gain
  • Mathematical intuition behind SVM. 
  • Support Vector Machines.
  • How support vehicle machines are different from other classifiers

 

Model Evaluation and Random Forest

Under this section, students learn how to assess algorithms and the following concepts.

  • Bootstrapping and majority rule.
  • Random Forest.
  • Evaluation of classifiers. 

 

Unsupervised Learning - Kmeans

This section introduces students to the concept of cluttering and the following topics.

  • What is clustering?
  • Why is clustering important?
  • Elbow curve and Kmeans.

 

Unsupervised Learning - Hierarchical

Under this module, students will learn the following: 

  • Hierarchical Clustering.
  • Dendrogram.
  • Evaluation of clustering algorithms.

 

Unsupervised Learning - PCA

In this module, students get introduced to some feature selection methods.

  • Principal Component Analysis.
  • Feature Selection.
  • Mathematical intuition behind PCA.

 

Supervised Learning - Neural Network Classification

Students under this section get introduced to a neural network and learn the following concept.

  • Deep learning.
  • Artificial Neural Network.
  • Understanding backpropagation.
  • Different activation functions.

 

Upcoming Trends in Machine Learning 

In the present times, data security is the necessity of the hour. Data remains guarded, incorporating Artificial Intelligence and machine learning approaches. In addition, machine learning practices help you overcome redundant manual jobs and an example of this is a personal voice assistant which is an evolving trend and assists in experimenting with machine learning principles to understand and improve the client experience. Machine learning likewise helps filter valuable data from any given information online to help save your valuable time. 

Also, machine learning (ML) deals with online fraud without any manual intervention and machine learning principles perform forecasting and quick decision-making. It assists in recent trends and approaches, keeping data secure and satisfying customers. 

 

What Are the Essential Skillsets for Machine Learning?

Here under are some of the top skill sets required for becoming a machine learning expert. 

 

Statistics

The algorithms built on machine learning principles require statistics such as research of variance and theory testing. 

In addition, statistics play a crucial role in developing machine learning algorithms. Therefore, if you want to become a machine learning expert, it is vital to gain knowledge of statistical mechanisms to accelerate your career

 

Programming Aptitudes 

Every computer science course has one thing in common, i.e. coding, and it is no different when it comes to machine learning. Therefore, coding is one essential skill that every company expects from a machine learning candidate. In addition, students pursuing machine learning course requires Python coding knowledge.

Apart from this, Python coding knowledge assists in scripting online web pages and other actions machine learning requirements. Also, your fundamental aptitudes like algorithms,  computer architecture, and data structures must be strong.

If you are looking for a reputed online learning institute to pursue a machine learning course, look no further than Skill-Lync. Skill-Lync is among the pioneer educational institute that can help you gain comprehensive knowledge on machine learning and help you upskill your career for better job opportunities. 

So what are you waiting for? Enrol yourself in the machine learning course now.

 


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Anup KumarH S


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