Machine Learning Tutorial for beginners

Machine Learning Tutorial for beginners

Machine learning is the fastest-growing technology that allows the machine to learn from training and experience without being explicitly programmed. In this machine-learning tutorial, you can understand the fundamental concepts to begin your career in this field.

Introduction to Machine Learning

Writing code is not the goal of machine learning; rather, it is about providing data to an algorithm and allowing the computer to generate reasoning from the data. This machine learning tutorial covers the following:

• Overview of Machine Learning
• How does machine learning work?
• Types of Machine Learning
• Applications of Machine Learning
• Demand for Machine Learning

Overview of Machine Learning

Have you ever noticed that while you shop for products online, it suggests similar products or with the tagline “The person who bought this product also bought this?”. Do you wonder how this happens? Yes. It’s all because of machine learning.

Machine learning is the subdivision of artificial intelligence technology that focuses on making machines learn from experiences to predict products or services based on those experiences. 

It allows machines to make data-driven decisions without being explicitly programmed to perform a particular task. Machine learning is performed based on the algorithms that are designed to learn and improve when encountering new data. Enroll in our data science courses to enrich your profile for in-demand jobs.

Evolution of Machines

Generally, humans are used to learning from previous experiences to make better decisions for the future. Same way, it is time to make machines and robots do them like human beings. At present, machines are programmed to follow pre-defined instructions for performing a particular task. 

But what if machines have started learning and performing on their own from experience? Have you ever imagined that machines can do tasks more accurately than humans? It is the beginning of it.

How does machine learning work?

The machine learning algorithm utilizes a training data set for creating an ML model. It makes predictions based on the model when new input data is introduced to the ML algorithm. 

If the accuracy is acceptable when evaluating the prediction, the particular machine learning algorithm will be deployed, or else the ML algorithm will be trained again and again with an augmented data set. Following is the simple flow chart that explains how ML works.

If you are a serious job seeker, join our data science full stack job seeker program and find a wide range of opportunities. 

Types of Machine Learning

Machine learning is classified into three types and they have supervised learning that requires training, unsupervised learning that is self-sufficient for learning, and reinforcement learning that follows its own instructions and rules.

Supervised Learning

It is where we must train the machine with a dataset that serves as a trainer. A dataset’s purpose is to train the model. It will start making predictions when new data arrives after the training has been done. 

Regression algorithms such as linear and polynomial, decision tree algorithms, random forest, and classification algorithms like KNN, trees, logistic regression, naïve Bayes, and SVM are being used in the supervised learning model.

Unsupervised Learning

The model finds structures and patterns in the given data set through experiences and observations. It automatically finds patterns in the dataset by grouping the data into clusters. 

• However, it can’t add labels to the cluster. In an unsupervised learning model, clustering algorithms like SVD, PCA, and K-means algorithms will be used for continuous processes. 
• Association analysis algorithms like Apriori, FP-Growth, and the Hidden Markov Model are being used for categorical models.

Reinforcement Learning

It is the model that can interact according to the environment and find what is an accurate outcome. It follows the hit-and-run concept and the agent will be accepted or avoided with a point for a correct or wrong answer. Once it is trained, it will be ready to predict when the new data arrives.

Gain AI skills for a promising career with our artificial intelligence course.

Use Cases of Machine Learning

Machine learning will be implemented in the following areas:

• Fraud detection for secure banking transactions.
• Algorithmic-Based Trading: Sentiment Analysis.
• Financial Advisory and Portfolio Management.
• Email monitoring.
• Machine learning against bots.
• Detecting Drive to avoid vulnerabilities.
• Customer Journey Optimization.
• Content Curation.
• Customer experience enhancement.
• Skin cancer diagnosis.
• Mortality Risk Predictor.
• Administrative tasks with NLP.
• recommendation engines.
• Dynamic Pricing in the Revenue Department.
• Demand Forecasting and Stocking.

Begin your data science career with our Python training in Chennai.

Applications of Machine Learning

Machine learning algorithms are applied in environments where the solution is needed to improve the frequent post-deployment. It is widely used in medium-skilled human efforts for the required circumstances, such as customer service executives, chatbots, natural language processing, and customer query responding.

• Facebook is using machine learning algorithms to process the news feed in the Facebook and Instagram apps as per the relevant searches of users. This can be used to advertise brands according to their requirements.
• Netflix uses machine learning algorithms to collect user data and recommend movies and web series based on user preferences.
• Google utilizes machine learning algorithms for structuring the results of surfers and recommending video preferences for YouTube users.
• Amazon implements machine learning algorithms to showcase relevant products for user viewing to maximize conversion rates by recommending products for the users to buy.

Machine learning algorithms are used in numerous other use cases for understanding the patterns for recommending their products and showing them as per their user interests.

Top 10 Organizations Using Machine Learning in Innovative Ways

• Facebook for offering the best chatbot army
• Twitter for providing curated timelines
• Google for implementing neural networks and dream machines
• Edgecase for improving conversion rates of the eCommerce process
• Baidu for increasing voice search
• HubSpot for improving smarter sales
• IBM for providing better healthcare
• Salesforce for generating intelligent CRMs
• Pinterest for improving content delivery
• Yelp for image caution scaling.

Enhance your skills with our data science with machine learning course. 

Demand for Machine Learning

There is a growing need for machine learning. Machine learning is necessary because it can carry out activities that are too complicated for a person to carry out directly. 

• Because humans cannot manually access large volumes of data, we are dependent on computer systems, which is where machine learning comes in to make our lives easier.
• We can train machine learning algorithms by giving them a lot of data and letting them automatically examine the data, create models, and forecast the desired result. 
• The performance of the machine learning algorithm and the quantity of data can be ascertained using the cost function. By employing machine learning, we can save money and time.

The following are some salient features that demonstrate the significance of machine learning:

• The rapid increase in the amount of data produced.
• Resolving complicated issues that are challenging for people.
• Making decisions in a variety of industries, such as finance.
• Identifying hidden trends and deriving practical knowledge from data.

Machine Learning Today

Machine learning has advanced significantly in the last several years, and there are many uses for it, such as recommender systems, self-driving cars, Amazon Alexa, and catboats. It includes both supervised and unsupervised learning, as well as clustering, classification, decision trees, SVM algorithms, and reinforcement learning.

Artificial intelligence (AI) models of today can be used to predict various things, such as the weather, illness, financial exchange, and more.

The Future of Machine Learning

Machine learning can learn more effectively to deploy AI to recognize that it improves internal architecture and minimizes human supervision.

• Global businesses are compelled to use machine learning algorithms to limit cyberattacks through automation processes, such as DDoS attacks and database breaches.
• Improvements in generative modeling depend on machine learning algorithms to increase sophisticated images in browsers like Baidu.

Conclusion

Machine learning has a bright future as the world is fully transformed into a data era. The study of the machine learning process is useful for the aspirants to start their journey that has rapid career growth. We hope this machine learning tutorial helps you begin your learning journey. Develop your skills by enrolling in our machine learning course in Chennai at SLA.