Artificial Intelligence and Machine Learning: What’s the difference?

Artificial Intelligence (AI) and Machine Learning (ML) are two of the most talked-about and rapidly evolving fields in technology today. AI refers to the ability of machines to perform tasks that typically require human intelligence, such as natural language processing, decision-making, and problem-solving.

On the other hand, ML is a subset of AI that involves the use of algorithms to enable machines to learn from data, identify patterns, and make decisions without explicit instructions. In this blog post, we’ll take a closer look at the differences between AI and ML, how they are related, and how they are transforming various industries.

Artificial Intelligence:

AI, or Artificial Intelligence, refers to the ability of machines or computer programs to perform tasks that would typically require human intelligence, such as reasoning, problem-solving, decision-making, understanding natural language, and recognizing patterns. 

AI involves the development of algorithms, models, and systems that can simulate human cognition and behavior, allowing machines to perform complex tasks more efficiently and effectively. 

AI has various applications in fields such as healthcare, finance, education, transportation, and many others, and is transforming the way we live and work.

Machine Learning:

ML, or Machine Learning, is a subset of AI that focuses on the development of algorithms and statistical models that allow machines to learn from data, identify patterns, and make decisions without being explicitly programmed. 

In other words, ML involves building computer programs that can automatically improve their performance on a specific task by learning from experience, without being explicitly programmed to do so. ML algorithms are designed to automatically identify patterns in data, learn from those patterns, and make predictions or decisions based on that learning. 

ML has many practical applications, such as image and speech recognition, natural language processing, recommendation systems, fraud detection, and predictive maintenance, among others.

Difference Between AI and ML:

AI and ML are two of the most exciting fields in technology today, and are often used interchangeably. However, they are not the same thing. AI refers to the capability of machines to perform tasks that usually require human intelligence, such as decision-making, problem-solving, and natural language processing. 

Think of AI as the parent category, and ML as one of its children. While both are related, they have some fundamental differences. AI focuses on creating intelligent machines that can think and work like humans, whereas ML focuses on teaching machines to learn from data and improve their performance over time.

Another key difference is their level of autonomy. AI systems can operate independently and make decisions on their own, while ML algorithms require input and guidance from humans to improve their performance.

Overall, AI and ML are transforming the way we live and work, and their applications are increasingly present in our daily lives. From chatbots to self-driving cars, from voice assistants to personalized recommendations, AI and ML are making our lives easier, more efficient, and more exciting. So buckle up, and let’s explore these fascinating fields together!

The key differences between AI and ML:

AI (Artificial Intelligence) and ML (Machine Learning) are two related but distinct concepts. AI refers to the ability of machines to perform tasks that typically require human intelligence, such as decision-making, problem-solving, and natural language processing. 

ML, on the other hand, is a subset of AI that focuses on developing algorithms that enable machines to learn from data, identify patterns, and make decisions without explicit instructions.

Take your  ML skills to the next level and enroll in the SkillVertex Machine Learning Course 

• Learn everything you need to know about creating automated learning systems and understanding ML algorithms with the Skillvertex Machine Learning Course.
• Explore the various applications of AI and learn how to create dashboards, storytelling, and deploy models.
• Intern with Skillvertex on live projects and develop a strong skill set for personal and professional growth.
• Gain insight into the exciting field of AI and become familiar with which algorithms and models to implement for different problem statements.

Application differences between AI and ML:

Let’s explore this fascinating world of AI and machine learning together.

In this table, we’ll take a closer look at how these two cutting-edge technologies are used in various applications. From robots that can learn from experience to virtual assistants that can understand human language, the table will explore the exciting differences in how AI and ML are changing the way we work, play, and live. 

So, buckle up and get ready for an informative and entertaining journey through the exciting applications of AI and ML!

Take your AI skills to the next level and enroll in the Skillvertex Artificial Intelligence Course

What you’ll learn in the Skillvertex Artificial Intelligence course

• Introduction to Artificial Intelligence and Machine Learning
• Programming automated learning systems using Python
• Understanding various ML algorithms and their use in setting up a learning environment
• Learning about the various applications of AI
• Creating dashboards, storytelling, and deploying models
• Interning on live projects to gain practical experience
• Developing AI skills for personal and career growth
• Gaining insights into Artificial Intelligence
• Knowing which algorithm/model to implement for different problem statements

Table of Contents

Data Science Courses After 12th: Data Science has emerged as one of the hottest career options in recent times. With the rapid growth of technology and the increasing demand for data-driven solutions, companies are always on the lookout for skilled data scientists who can analyze complex data sets and make business decisions based on insights. As a 12th pass student, you might be wondering what data science course you should take to enhance your knowledge and skills. 

We’ll explore the best data science courses for 12th students today..

What is Data Science ?

Data science is a field that uses scientific methods to extract insights and knowledge from data. It involves various stages of the data lifecycle, including data collection, cleaning, analysis, visualization, and communication of insights. 

Data scientists use tools like machine learning algorithms, data mining, and visualization to provide data-driven solutions to real-world problems. It has applications in fields such as finance, healthcare, and e-commerce, and is changing the way we approach problems and make decisions.

Why Data Science is Important?

In today’s world, data is everywhere. Every time you browse a website, use a mobile app or make a purchase online, you generate data. But what happens to all that data? How can businesses and organizations make sense of it all?

Think about it – when you order something from an online store, have you ever noticed how they suggest other products that you might be interested in? That’s the power of data science in action. 

By analysing your past purchases and browsing history, the online store can personalize its offerings to better match your interests.

For example, a food delivery company could use data science to optimize its delivery routes and reduce the amount of time and fuel it takes to make deliveries. Or, a healthcare company could use data science to identify patterns in patient data and develop new treatments for diseases.

In short, data science is a game-changer for businesses and organizations. It provides them with insights that they might not have been able to uncover otherwise, and helps them make informed decisions that can improve their operations and profitability. So, if you’re a business owner or thinking of pursuing a career in data science, understanding the importance of data science is crucial for your success.

Best Data Science Courses After 12th by Skill Vertex:

Data Science is a valuable field of study for 12th Pass students because it provides many job opportunities, gives them a competitive edge in the job market, promotes innovation, can be applied to various industries, and fosters personal development. 

Skill Vertex provides an extensive Data Science course that will help you become an expert in interpreting data. The course will help you develop skills in data analysis, data visualization, machine learning, and statistical modeling. With this training, you’ll be able to extract insights and knowledge from data, make data-driven decisions, and predict future trends.

In this blog post, we will dive deeper into the content and benefits of Skillvertex’s Data Science Course, and how it can help to 12th Pass students jump-start their careers in the digital marketing industry.

Why Should the Choose SkillVertex Data Science Course Over Others?

It can be a daunting and exciting experience to 12th Pass from university. With so many career paths to choose from, it’s essential to choose a field that is in demand and has excellent career prospects. 

One such field is Data Science, and if you’re interested in pursuing a career in this industry, Skill Vertex’s Data Science course is the perfect place to start. 

Here’s why you should choose Skill Vertex over other providers:

• Comprehensive Learning Experience
• Hands-on Experience
• Work on Capstone projects
• Experienced Faculty
• Career Support

Skill Vertex’s Data Science course is an excellent choice for 12th Pass students looking to pursue a career in Data Science. With a comprehensive learning experience, hands-on experience, experienced faculty, internship opportunities, and career support, Skill Vertex has everything you need to succeed in this exciting field. 

Enroll today and take the first step towards a fulfilling career in Data Science.

Key highlights :

• 48+ hours of video lectures
• Lifetime LMS access on mobile or laptop
• Dedicated mentorship assistance

Choose live or recorded sessions and take the first step toward a rewarding career in Data Science!

Course overview and Features:

• Learn hardware and OS management with Linux
• Gain project management skills using YARN, GitHub, and Git Bash
• Kickstart your learning with Python
• Master Data Science and Analytics techniques using Python programming
• Learn Mathematics and Statistics concepts required to work with data
• Master machine learning techniques
• Intern with live projects to enhance practical skills.

Click here to get the free trailer  of Data science Course by Skillvertex

Data Science Courses After 12th – Course Projects and Certification:

• The course includes two industrial projects (minor and major)
• The course will culminate with the students receiving a certificate of completion
• Internship opportunities will be provided to further enhance learning

Data Science Courses After 12th – Module Based Curriculum and Course Topics

The program curriculum is divided into seven modules.

Module 1 – Introduction to Python 

• Objects and Data Structure 
• Functions in Python Modules 
• packages Statements in Python 
• Basic built-in Python modules 

Module 1 of Skill Vertex’s Data Science program covers an introduction to Python, including objects and data structures, functions, modules, and statements. You’ll learn how to use basic built-in Python modules and be ready to tackle advanced data science topics.

Module 2 – Necessary Data Science Modules

• Numpy (vector procession)
• Pandas (Data Processing)
• Matplotlib (visualization)
• Seaborn (Visualization)
• SK-Learn

Module 2 of Skill Vertex’s Data Science program focuses on the necessary modules for data science, including NumPy for vector procession, Pandas for data processing, Matplotlib and Seaborn for visualization, and Scikit-Learn for machine learning. You’ll learn how to use these tools to manipulate and analyze data, visualize results, and build machine-learning models.

Module 3 – Data Pre-processing

• Acquiring and importing datasets
• Feature engineering and selection, including identifying important features
• Handling missing values
• Scaling and normalizing data
• Handling categorical features
• Data decomposition and splitting

In Module 3 of Skill Vertex’s Data Science program, students learn how to preprocess data for analysis. This module covers several essential data processing techniques, including acquiring and importing datasets, feature engineering, and selection, handling missing values, scaling and normalizing data, and handling categorical features.

Module 4 – EDA(Exploratory Data Analysis)

• Answering questions through data
• Data visualization (line, scatter plots, etc.)
• Analyzing various aspects of the data
• Statistical analysis
• Correlation analysis (positive and negative correlation, multicollinearity)

Learn to analyze data through visualizations such as line and scatter plots, perform statistical analysis, and evaluate correlation using techniques such as positive and negative correlation. Gain insights into various aspects of the data to answer questions through data analysis.

Module 5 – Modelling(Machine Learning Models)

• Learn the theory and implementation of various machine-learning models
• Implement regression models such as linear regression, polynomial regression, and multiple linear regression
• Implement classification models such as KNN, SVM, logistic regression, decision tree, random forest, and Naive Bayes
• Optimize model parameters using Grid Search.

This module is designed to teach you the theoretical and practical aspects of various machine learning models. You will learn to implement popular regression models, such as linear and polynomial regression, as well as classification models like KNN, SVM, logistic regression, decision trees, and Naive Bayes.

Module 6 – Evaluation

• Model Evaluation with various parameters
• SE, MSE, MAE, RMSE, R2 score, etc.
• Confusion Matrix, Accuracy, Precession, Recall, Fl Score
• K-fold Cross Validation

In Module 6, you will learn about the evaluation of machine learning models. You will explore various parameters used for model evaluation such as SE, MSE, MAE, RMSE, R2 score, etc. Additionally, you will learn about the Confusion Matrix, Accuracy, Precision, Recall, and F1 Score. Finally, you will learn about K-fold Cross Validation, which is a method for assessing the performance of a machine-learning model.

Module 7 – Dash Boarding Storytelling, Model Deployment

• Introduction to Dash boarding
• Creating interactive dashboards and reports
• Designing web apps for machine learning models
• Deploying machine learning models on localhost.

This module teaches the students about creating a web app for their machine learning model and deploying it into a local environment to make it accessible to others. Students will learn about various web app deployment techniques and technologies to make their models accessible to others in a user-friendly way.

Skill Vertex’s Data Science course Takeaways:

• Knowledge of Python programming for data science and machine learning
• Proficiency in necessary modules and libraries
• Data preprocessing and feature engineering
• EDA and statistical analysis
• Building machine learning models
• Model evaluation and deployment
• Creating interactive dashboards and reports
• Internship opportunities are provided to further enhance learning
• ISO Certification 

To learn more about the Skillvertex Data Science upskilling program and to register, click here