Artificial Intelligence and Machine Learning Courses Archives - Intellipaat Academy

Artificial Intelligence and Machine Learning Courses

As per reports, the global Machine Learning and AI market is expected to reach USD $ 30 Billion by the year 2024 at a CAGR of 43%. The domains of Artificial Intelligence and Machine Learning are ever evolving and have a rewarding career to offer for those skilled in these fields. Intellipaat’s varied range of AI and ML courses aim at making you market-ready and gain the necessary skills for a rewarding career in Artificial Intelligence and Machine Learning.

3,00,000+

Job opportunities by 2025*

INR 12,00,000+

Average salary at entry level

400%

Highest Salary Hike

70,000+

Career transitions in varied domains

What Learners has to say

57% Average Salary Hike

$1,14,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

Free Courses

Register For Our Courses, Upskill Yourself and Get a Hike!

We’ve helped 1.3 Million+ professionals like you!

Degree and Certificate Programs

  • Learn from top faculty
  • 3 Guaranteed Interviews
  • Career mentorship
  • Industry-aligned curriculum
  • Certification from Top Universities

70000+

Career Transitions Till Now

1.3 Million+

Learners Across the Globe

One-on-One

Mentorship

No Cost

Emi Options

Testimonials

Register For Our Courses, Upskill Yourself and Get a Hike!

We’ve helped 1.3 Million+ professionals like you!

Tutorials

View All Tutorials

Interview Questions

View All

Register For Our Courses, Upskill Yourself and Get a Hike!

We’ve helped 1.3 Million+ professionals like you!

Frequently Asked Questions

What is AI?

Artificial intelligence or AI, as it is widely known, is a broad branch of computer science that is concerned with building smart machines. AI machines are capable of performing tasks that otherwise require human intelligence. Some examples of AI are smart assistants, such as Siri, Alexa, and Google Assistant, self-driving cars, robo-advisors, conversational bots, email spam filters, etc.

Machine learning or ML is a branch of AI and computer science that involves the use of data and algorithms to imitate human learning, without explicit programming, and gradually improve its accuracy. ML is an important component of data science. Using statistical methods, algorithms are trained to make predictions or classifications that can help uncover key insights in data mining projects. These generated insights drive decision-making within applications and businesses that positively impact key growth metrics.

The key differences between AI and ML are:

AI ML
AI enables a machine to simulate human behavior ML is a subset of AI and automatically enables a machine to learn from past data without explicit programming
AI’s goal is to make a smart, humanlike computer system to solve complex problems ML’s goal is to enable machines to learn from data and yield accurate output
AI builds intelligent systems to perform tasks like humans ML teaches machines with data to perform particular tasks and generate accurate results
AI’s two main subsets are ML and deep learning ML’s main subset is deep learning
AI includes learning, reasoning, and self-correction ML includes learning and self-correction when introduced to new data
AI deals with all types of data ML deals with structured and semi-structured data
AI’s main applications are automated personal assistants, customer support using chatbots, online gaming, intelligent humanoid robots, etc.
ML’s main applications are online recommender systems, Google search algorithms, Facebook auto friend tagging suggestions, etc.

We provide free resources, such as blogs, tutorials, and videos, as well as a community where you can learn a lot about AI and ML from subject matter experts. We also provide free courses related to AI and ML that you can start learning to build up your basics.

Deep learning is a subset of ML. Deep learning is concerned with algorithms that are inspired by the structure and function of the brain. These systems are known as artificial neural networks.

The core of deep learning is the capability of training large neural networks due to the present existence of fast enough computers and sufficient data. As larger neural networks are constructed and trained with more and more data, their performance improves significantly. Deep learning is generally different from other ML techniques that reach a plateau in their performance.

There are no prerequisites mandated to start learning AI and ML. As long as you have a good internet connection, you can start taking these free courses.

You can start your journey by learning the fundamentals of AI such as NLP using Python, neural networks and deep learning, logistic regression, linear regression, ML with TensorFlow, time series analysis, etc. Learning the fundamentals will help you get started in your journey to become an AI Engineer in no time.

Today, AI has either conquered or is knocking on the door of almost every industry. In the near future, nearly 80% of emerging technologies will be based on AI.

ML, especially deep learning, is used by AI-powered recommender systems, chatbots, and search engines for online movie recommendations and several other applications.
Therefore, AI and ML can be a rewarding career option. Gaining all the right competencies will help you get a high-paying job in top MNCs around the world.

Many companies are seeking skilled candidates in ML for their AI-powered projects. With the rapid growth of AI and ML in every area, the future for professionals in this domain looks promising.

According to Glassdorr, AI engineers in India earn an average of about ₹949,364 p.a. and about US$119,297 p.a. in the USA.

According to Glassdoor, ML engineers in India earn an average of about ₹898,509 p.a. and US$131,001 p.a. in the USA.

Some of the job responsibilities of an AI engineer are:

  • Evaluating and comparing algorithm performances based on large datasets
  • Data mining from multiple sources and deriving insights
  • Designing and implementing ML algorithms
  • Accelerating existing algorithms and models
  • Establishing scalable model validation, model implementation, and large-scale data analysis
  • Conducting research to determine technical solutions at scale for real-world challenges in various scenarios

Some of the job responsibilities of an ML engineer are:

  • Designing ML systems
  • Researching and implementing algorithms and tools
  • Performing statistical analysis
  • Verifying data quality
  • Executing ML tests
  • Choosing appropriate datasets
  • Extending ML libraries
  • Developing ML apps as per client requirements
  • Transforming and converting data science prototypes
  • Using results to improve models
  • Training and retraining systems when required
  • Choosing the appropriate data representation methods
  • Recognizing differences in data distribution that impact
  • model performance
View More