PG Diploma in AI and ML Course

A machine learning course can last from 3 months to as long as 4 years, depending on the type of degree you pursue. If one hasn’t opted for computer science as an elective or is unaware of the basic programming languages, then the duration for the completion of the course can extend to 6 years. However, with diligence and hard work, getting a degree in machine learning and bridging any gaps with pathways is possible. There is also the option for crash courses offered by various companies like Google or Amazon that train the students with specific skill sets that align with the company’s job profile.

1. Full Time (1 Year, 2 Year Machine Learning, etc.)

Machine learning can be taken as a full-time course. Undergraduate and postgraduate degrees are available as full-time courses. Students opting for the full-time course need to meet the credit hours required for that course within the given time frame.

It takes three years to complete the BSc. degree. This might vary from country to country, depending on how the credits are calculated. The B.Tech degree usually takes around four years to complete. But, the credits required to complete the degree remain the same.

The full-time postgraduate or Master’s degree is either a one-year or two-year long course, depending on the college and country.

2. Part-Time Machine Learning Course

The part-time machine learning course is ideal for working professionals or students. It is usually longer in duration, mostly double the tenure mentioned in the full-time course, and the credit earned is the same as the full-time course credits.

3. One-Year Machine Learning Course

For a diploma or certification program, one-year is the usual duration. These courses are focused on developing skills that are required for immediate job seekers. Professionals with experience in data science or those with a B Tech in artificial intelligence or an M Tech in artificial intelligence degree can take up a machine learning specialisation course to further their career prospects. The AI and ML courses are also done as one-year courses.

Other than that, the PGDM or postgraduate diploma course is a year-long course that can be completed online. It is primarily a self-paced and self-learning model that many educational service providers offer. It is ideal for people looking to enhance their skills and improve their career prospects.

4. Online Machine Learning Course

An online machine learning course is usually preferred by working professionals and even students who are seeking to get a degree from foreign universities like Stanford or Harvard, known to offer the best machine learning courses online. Many companies offer crash courses for bridging professional gaps. One such course is the AWS machine learning certification. The usual duration of these courses is between a few weeks to about 3 months.

Another popular course is the machine learning crash course. Many companies curate their curricula to suit the company’s job profile for specific roles that help students and professionals to get accustomed to the native artificial intelligence environment.

Machine learning is taught both as an offline and an online course. Many students prefer the offline traditional learning method. There’s no denying the fun college experience and socialising that comes with an offline course. But, the online course has its fair share of benefits. These are:

• More job opportunities

Students and professionals who opt for an online course can also get their certifications from esteemed universities and colleges. For example, IIT Madras also offers online machine learning certification. Hence, these courses can further your job prospects by many folds.

• Better networking options

Since the classes are not restricted to a specific geographic location, you can meet many people who have enrolled in the same class. The online classes are also not limited to specific age groups, making the classroom more diverse consisting of a freshly high school graduate student to a senior programming developer with years of experience. This increases bonding and also helps develop networking skills.

• Chance to work with machine learning bots

While most of the classes are conducted by human teachers, some are even held by chatbots. The introduction of chatbots into education enables the machine to gauge the understanding level of the student and curate the course to suit their pace. This helps students self-learn as they try and navigate the chatbot programming. This also helps one understand the concept of big data with real-life models to help practice better.

• More diverse curriculum

1. Skill Required for Machine Learning Course

To ace machine learning, it is essential to have basic knowledge of the core mathematical and computer science concepts. And one must opt in for science while in school to be considered eligible for pursuing machine learning as a degree. If any of these subjects were not opted for in a high school, opting for pathway programs will help overcome the education gap. These subjects or skills include:

• Statistics: Machine learning algorithms are based on statistics and the development of predictive models. Knowledge of statistical tools will help you accelerate your machine learning course and also aid in your professional betterment.
• Probability: Probability and chance, a mathematical discipline, helps in prediction. Mathematical equations in probability aid in predicting the future. Machine learning deals with uncertain conditions to make reliable decisions.
• Data Modeling: Formless data have to be analyzed and modeled to create algorithms. Data modeling is required in this case. Data modeling concepts have to be known to become a machine learning professional.
• Programming Skills: All computer science careers have one of their parts codings. Coding is one skill that every recruiter expects from a computer science candidate. Knowing different programming languages is a plus. Fundamentals skills like computer architecture, algorithms, and data structures have to be strong. One of the best machine learning with python courses focuses on these very aspects.
• Applied Mathematics: A deep understanding of mathematical subjects such as algebra, probability, statistics, calculus, etc. is very important in the field of machine learning. Whether opting for the right algorithms or deciding the validation strategies, mastery in maths is a must. Apart from mathematics, physics concepts also play a crucial role in machine learning.

Being part of the artificial intelligence and data science fields, machine learning is primarily included in the following core subjects:

• Statistics
• Python
• Data science
• Deep learning
• Artificial intelligence
• Programming for problem-solving
• Mathematics
• Engineering physics
• Application-based programming using Python
• Data Structures using C
• OOPS using JAVA
• Database Management System
• Pattern Recognition
• Computational learning theory

The syllabus for the machine learning course depends on the level of the degree. The crash courses and diploma certifications too are based on the specialization that the student wishes to attain.

1. Machine Learning Course Subjects

The core subjects taught in the machine learning course are:

• Data Science Tool kit
• Statistics & Exploratory Data Analytics
• Machine Learning-1
• Machine Learning-2
• Natural Language Processing
• Deep Learning
• Reinforcement Learning
• Deployment and Capstone Project

Apart from the above-mentioned subjects, basics like the ones listed below are also taught on an undergraduate level:

• Programming Languages like R, Python, C++, and Java.
• Machine Learning Algorithms and Techniques
• Relation between Artificial Intelligence and Machine Learning
• Artificial Neural Networks and their applications
• Reinforcement Learning and Deep Learning
• Natural Language Processing

Machine learning is considered to be a pioneer in the fields of artificial intelligence and computer gaming. The capability of a machine to think for itself has been a breakthrough. This made machine learning an essential part of any data-related field. The core function of machine learning is the classification of data based on the models that the algorithm depicts and the capability of making predictions based on the analysis of that collected data.
Today, machine learning can be used as a specialization subject in order to increase career prospects across a variety of fields. Some of the fields that make use of machine learning are:

1. Machine Learning in Artificial Intelligence

Artificial intelligence or AI helps make computers and electronic devices act smart. But, the basis of this development is machine learning and specific algorithms. As artificial intelligence focuses on the construction of computational systems, the function of machine learning is to make these systems work smoothly. Each variable added by the machine learning algorithm helps artificial intelligence to work more efficiently.

2. Machine Learning in Data Science

Being an interdisciplinary subject, data science deals with anything related to collecting and analyzing complex data. Since collection plays a vital role in data science, there is always the chance of redundant or unwanted data getting collected along with important data sets. This makes the task of analyzing much more difficult. With machine learning, data science predictive models can function better. The machine learning algorithm can act as a filter to not only prevent the collection of data but also to make real-time predictions related to business trends.

3. Machine Learning in Data Mining

Data mining is used to extract relatable and useful data from data sets. This is a task that helps with the discovery of both known and unknown data. But, when the company needs to focus solely on known data so that predictions can be made, the application of machine learning to data mining can help with this task.

4. Machine Learning in Mathematical Optimization

Mathematical optimization or mathematical programming refers to the utilization of specific mathematical criteria that will help make decisions better. In other words, with the use of mathematical functions like linear programming and probability, it will be easy to predict the future concerning business trends. Machine learning helps make these predictions more accurate by pitting the prediction against data history.

5. Machine Learning in Computational Intelligence

Computational Intelligence is a field of computer science that deals with the implementation of models that are based on biological and linguistic paradigms. Simply put, computational intelligence is more about the machine already learning for years. With machine learning, this development of computational intelligence is made more accurate.