Machine Learning is a data-driven technology that mimics the cognitive capabilities of the human brain by learning from behavioral patterns and providing suitable insights. It comprises of algorithms that automatically improves the system through experience.  

Machine learning technology collects vast amounts of data and then analyses them to learn and improve from experiences.  Advances in artificial intelligence and cloud platforms and the sheer volume and complexity of big data have been primary catalysts behind the thriving machine learning market. 

Companies today are on a race to leverage the immense potential of machine learning to gain business intelligence, reach more customers, enhance the omnichannel experience, and drive more revenue. They are setting up in-house machine learning teams and infrastructure to save cost, achieve scalability, and foster innovation. Thus there is and will be a significant increase in the hiring of certified professionals who can handle big data analytics and machine learning.

Machine Learning can be broadly categorized into five approaches.

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Deep learning
• Meta learning

Choosing appropriate algorithms is the key to enhance machine learning models and implementation. Listed below are the top 10 machine learning algorithms available in the market.

• Linear Regression
• Logical Regression
• Decision Tree
• Support Vector Machine (SVM)
• Naïve Bayes
• K- Nearest Neighbors (KNN)
• K-Means
• Random Forest
• Dimensionality Reduction Algorithms
• Gradient Boosting and AdaBoost

There are seven steps in a machine learning lifecycle, that aids efficient problem solving and application development. They are as follows.

• Gathering data – collect data from databases, devices, and internet
• Data preparation – understand data quality and preprocess for analysis
• Data Wrangling – cleansing and transforming data into a usable format
• Data Analysis – use analytics to build models
• Train the model – improve performance through various algorithms
• Test the model – hands-on test for accuracy
• Deployment – implement the model in a real-world system

The practical applications of machine learning transcend industries and functions. Companies across industries are rapidly adopting this ground-breaking technology to provide enhanced customer experience, achieve high levels of customer satisfaction, and reach business goals. According to the Technavio research report, the global machine learning market will grow by USD 11.16 billion during 2020-2024.

The list below provides a small sample of sectors that have embraced machine learning and are benefitting immensely from its applications.

• Media and Entertainment–Predicts what the individual customer will prefer and enjoy through recommendation and suggestion engines
• Search, Mobile, and Social – Sorts, tags and categorizes search results, posts, photos and videos
• Automotive, Transportation, and Logistics – Enables autonomous self-driving vehicles to sense the surrounding, make sense of the data gathered and operate accordingly
• Education – Provides gamified learning experiences and predicts when the user will retake the lessons
• Retail – Supports customer satisfaction and engagement metrics of suggestion engines that influence buying decisions
• Healthcare – Analyses patient information to predict when the patient will get sick and suggests apt healthcare facilities
• Finance – Scrutinizes creditworthiness and detects fraudulent activities
• Marketing – Customizes and personalizes content for targeted marketing campaigns
• Agriculture – Supports harvest, weed removal and spraying of fertilizers and pesticides

According to leading job search portals, Monster.com, and Indeed.com,  machine learning is among the hottest skills that are in demand today.

Advances in digitization and computing power have triggered data scientists to train the computers with algorithms that analyze data and behavior, establish patterns, make predictions, and enable insight-driven decision making.

Companies are in dire need of professionals who can decode, develop, and monetize this ground-breaking technology to achieve business goals. They are actively hiring certified machine learning professionals who can build and support machine learning applications in the capacities below.

• Machine Learning Engineer
• Machine Learning Developer
• Business Intelligence Developer
• Data Scientist
• NLP Scientist
• Human-Centered Machine Learning Designer
• Data Modeler

The demand for skilled machine learning professionals is at its peak now. Research shows that this hiring spree will only grow in the future.  So now is the right time to enroll yourself in the Machine Learning course at AICL in Chennai.  

We design our course content and curriculum with the latest technological advancements and industry requirements in mind. The following are the benefits of the Machine Learning course at AICL in Chennai. 

• Lays a strong foundation for critical concepts of artificial intelligence, machine learning, and deep learning technologies
• Thorough coverage of machine learning algorithms
• Step-by-step guidance and hands-on training in setting up virtual environments in supervised, unsupervised and reinforced learning approaches
• Mentorship from faculty with rich industry experience
• 100% job placements guarantee

The first requirement is the passion for making it big in the machine learning domain. The other prerequisites are coding ability in Python and basic knowledge of algebra, probability, and calculus.