Our Machine Learning Program Duration and Fees

Machine Learning programs typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Professional Certificate Course in Generative AI and Machine Learning

Cohort Starts: 8 Jan, 2025

11 months$ 2,500
AI & Machine Learning Bootcamp

Cohort Starts: 22 Jan, 2025

24 weeks$ 8,000

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Machine Learning Courses Learner's Reviews

  • Hans Friedhoff

    Hans Friedhoff

    Senior Manager, Digital Communications

    After completing the bootcamp, I transitioned to a Machine Learning Engineer within my organization. I was also able to obtain an 18% salary increase.

  • Fritz Canon

    Fritz Canon

    Project Manager Business Development

    My overall experience with Simplilearn was extremely positive and enriching. One aspect that stood out to me was the comprehensive curriculum offered by Simplilearn. The course content was well-structured and covered all the essential topics related to my chosen field of study.

  • Tony Vigna

    Tony Vigna

    The overall experience was outstanding. Since I am a working professional, I enjoy a flexible schedule. The classes were on weekends and did not collide with the working hours. So, it was conducive. The best part is that they take feedback regularly and work on it to improve the learning experience for the students.

  • Diego Sabajo

    Diego Sabajo

    Co-Founder

    My learning experience with Simplilearn was outstanding. The course material is very thoughtfully designed. The way of explaining was simple and easy to understand. The instructors were good; the support was really helpful. I recommend this course to others who want to start something in the AI domain.

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Know more about Machine Learning

What is ML? How is it different from AI?

Machine learning is a subset of artificial intelligence that focuses on developing algorithms and statistical models that enable computers to learn from and make predictions based on data. While AI encompasses a broader range of technologies to simulate human intelligence, ML specifically involves training models to improve their performance over time without being explicitly programmed for each task. In essence, all machine learning is AI, but not all AI is machine learning, as AI can also include rule-based systems and expert systems that do not learn from data.

Why is it important to enroll in machine learning courses?

Machine learning courses help professionals upskill and stay competitive in the job market. As industries increasingly leverage AI to enhance decision-making and improve efficiency, understanding ML concepts and techniques becomes a vital skill set. These machine learning programs provide practical knowledge and hands-on experience, enabling learners to develop algorithms, analyze data, and create predictive models. 

Additionally, with the demand for skilled professionals in this field rising, completing a machine learning training program can significantly boost your career prospects, opening doors to roles in tech, finance, healthcare, and beyond.

Recent trends in machine learning across industries

Machine learning is reshaping industries with tangible outcomes and significant impact. Recent trends in ML are transforming industries by enabling more sophisticated, data-driven insights and automation. In healthcare, ML models improve diagnostics and personalized treatments, while in finance, they enhance fraud detection and algorithmic trading. 

Retail is leveraging ML for personalized customer experiences and optimized inventory management, while manufacturing sees advancements in predictive maintenance and quality control through ML-powered systems. Additionally, natural language processing (NLP) has become essential across sectors for chatbots, virtual assistants, and sentiment analysis. These trends highlight how ML continues to evolve, providing tailored, actionable solutions that boost efficiency, enhance customer satisfaction, and drive competitive advantage across industries.

Simplilearn’s benefit for Machine Learning certification

  1. Comprehensive curriculum: Well-structured, industry-focused curriculum covering foundational and advanced machine learning topics, designed by experts.

  2. Capstone project: Real-world projects to translate theory into practice and apply ML tools professionally.

  3. University masterclass: Additional masterclasses led by university instructors.

  4. Flexible learning options: Self-paced and live online classes catering to various learning styles and schedules.

  5. Globally recognized certificate: Earn a certificate that highlights your expertise in Machine learning, which is respected by employers and peers alike.

  6. Career support: Resume-building assistance, interview prep, and job placement support, enhancing your ability to showcase your certification and secure career opportunities.

  7. Networking and community: Connect with a network of peers and industry professionals passionate about ML.

FAQs on Machine Learning Courses

  • What are the highest paying Machine Learning Certificates?

    ML is a popular course, especially in developed nations. To set up a flourishing career in this domain, learners must possess machine learning certifications. It is a prerequisite to significant earnings and a position in a reputable firm. Whether a student or a skilled individual, the recognition enhances job prospects.

    Since this industry is expanding swiftly, it has given many employment prospects for dedicated students. If they wish to know about the highest-paying Machine Learning credentials according to earnings, check this table:

    CERTIFICATEOFFERING COMPANYAVERAGE EARNINGSEXPENSE
    IBM Recognized Expert - AI Industry Workflow V1

    IBM Enterprise

    $173,600

    $200

    Azure AI Engineer Assistant

    Microsoft Enterprise

    $164,770

    $165

    Proficient Machine Learning Expert

    Google Inc.

    $112,710

    $200

    AWS Recognized Machine Learning - Profession

    Amazon

    $97,360

    $300

    IBM Recognized Data Science Course - ML Expert V1

    IBM Enterprise

    $97,360

    $200

  • Which language should I learn first - AI or ML?

    Machine Learning is a component of Artificial Intelligence involving the designing and distribution of data from past statistics and references. It teaches computers by feeding information and statistical approaches to assist them in improving their job. AI for decision-making is the simulation of mortal intellect procedures through computers. Its apps comprise expert systems, NLP, speech knowledge, and Machine Learning.

    Learners must understand that Machine Learning comes under Artificial Intelligence. It means if they wish to excel in data science, begin with the best machine learning course. The online course will enhance your performance and accuracy of Machine learning techniques, design workflow, and create a portfolio to resolve business complexities and optimize production.

  • What are the best machine learning courses?

  • Is R or Python better for Machine Learning?

    If learners have no idea which machine learning certifications are ideal for employment growth, the following table will help clear the doubts.

     

    Basis of Difference

    R

    Python

    Nature of PL

    Statistical

    General purpose

    Degree of Adaptability

    It is extremely rigid.

    It is quite adaptable.

    Suitability

    It is ideal for statistical research and data intelligence.

    It is ideal for various jobs such as web development, input manipulation, and ML.

    Ease of Understanding

    It is a little difficult to understand because of the complex syntax.

    It is simpler to comprehend because of the relatively simple syntax.


    Although both languages are open-source, Python is universally a more effective language. On the other hand, R’s scope is confined to statistical analysis. Since both languages are nearly identical, the selection relies on the preference and employment needs.

    However, it has been observed that Python performs adequately in input manipulation and monotonous jobs. It is a more suitable choice if willing to create a virtual device based on Machine Learning. But if the desire is to construct a machine for ad-hoc research, R is a better choice.

  • Which Coding language is highly relevant for Machine Learning?

    Machine Learning is a complicated but fascinating domain. Several data engineering and analytics experts have dedicated their jobs to comprehending it. Since plenty of programming expressions are accessible, it is tough to pick the most suitable one. But, we have made the decision easier by specifying the 5 best machine learning courses, high in demand, at the moment.

    R

    It is a practical language adopted by statisticians and analysts to analyze and visualize inputs. It can consolidate data-heavy ML jobs and uphold other expressions.

    C++

    It is an object-oriented dialect perfect for performance-critical assignments and memory manipulation. Since it operates at a lower level, it can connect with computers in their aboriginal codes and provides a steeper knowledge arc.

    Java

    This language is among the machine learning courses with a complex syntax, ideal for constructing varied types of applications that can work on any medium.

    JavaScript

    It is a superior-style language that grew into a general-purpose dialect within a few years. It is ideal for fronted jobs and stretches to the backend in the form of an API.

    Python

    It is another superior programming language whose reputation has spiked in the last few years. It has straightforward syntax and increased speed which makes it perfect for rapid prototyping and the favorite of Machine Learning practitioners and data analytics professionals.

     

  • How do AI and ML differ from each other?

    AI and ML courses are valuable for companies of all scopes and are employed in multiple ways to automate repeated processes. Although they have many similarities and are used in similar industries such as healthcare, manufacturing, retail, telecommunications, and financial services, they are not identical.

    If learners want to know how these languages vary from each other, check the following table:

     

    Basis of Difference

    Artificial Intelligence (AI)

    Machine Learning (ML)

    GOAL

    Its goal is to build a machine that can mimic human intelligence.

    It aims to teach a machine the art of performing a specific task and generating outcomes by determining the patterns.

    EXTENT OF SCOPE

    It has a broader scope.

    It has a narrower scope.

    WORKING CAPACITY

    It can work with structured, unstructured, and semi-structured data.

    It can only work with structured and semi-structured data.

    RELIANCE

    These systems rely on logic and decision trees to learn.

    These systems rely on statistical models to learn.

  • Is Coding necessary for Machine Learning?

    To pursue a profession in Artificial Intelligence and Machine Learning, students must learn the art of coding, as both languages execute via coding. If they know how to implement a code, it will give them a better grasp on the operation, monitoring, and optimization of algorithms.

    Out of R, C++, Java, Python, JavaScript, Prolog, and Lisp, students can learn any ML through the best machine learning course, depending on the industry they are working in. But, learn about the underlying concepts of Machine Learning before starting with coding.

  • How to get a Machine Learning Certification?

    To obtain machine learning certifications, they must appear for and clear the two hours examination. It includes around 50 to 60 MCQs, covering topics such as framing ML general issues, structuring solutions, and creating various models. This certification is valid for two years. When this duration is over, they must re-appear for the exam to maintain the certification.

  • What is covered in Machine Learning courses?

    Machine learning courses majorly teach the fundamentals of building artificially intelligent algorithms and cover topics like supervised and unsupervised learning, deep learning, data processing, and model deployment, often including hands-on projects to build practical skills. Simplilearn’s machine learning courses are very comprehensive, ensuring that a learner with no background in coding can learn ML efficiently.. These courses also typically teach programming languages like Python and ML libraries like TensorFlow and scikit-learn.

  • What are the prerequisites for a machine learning course?

    The prerequisites for a machine learning course generally include a basic understanding of programming, particularly in Python, and foundational knowledge in statistics and mathematics (especially linear algebra and probability). However, Simplilearn’s ML courses are beginner-friendly and do not require you to have coding knowledge. 

    But, familiarity with data analysis and handling tools like Excel, SQL, or basic data visualization techniques can help you understand the topics better. Simplilearn also provides introductory modules to cover essential concepts, which you can self-learn before diving into ML topics.

  • How long does it typically take to complete a machine learning course?

    Simplilearn offers different types of machine learning courses. Depending on the course structure, these ML courses can take anywhere from 3 to 11 months to complete. Beginner courses focus on core ML concepts and often last around 3 months with part-time study. More comprehensive programs covering advanced topics like deep learning and real-world projects may take 6-11 months or longer. Self-paced courses offer flexibility, allowing learners to progress faster or slower based on their schedules.

  • What skills can I gain from a machine learning course?

    An ML course equips you with skills in data processing, which involves cleaning and preparing data, and model building, where you learn to design algorithms for supervised and unsupervised learning. You’ll gain proficiency in programming with Python and use ML libraries like TensorFlow and scikit-learn. Simplilearn’s ML courses also cover model evaluation techniques, helping you assess accuracy and optimize performance, along with deployment skills to implement ML models in real-world applications.

  • Is prior programming knowledge required for machine learning?

    While prior programming knowledge is generally beneficial for learning ML, it is not mandatory. Simplilearn’s machine learning courses are designed to be suitable for beginners. However, familiarity with languages like Python or R is often recommended, as these are widely used in ML for implementing algorithms, manipulating data, and using ML libraries (such as TensorFlow or scikit-learn).

  • What job roles can I pursue after completing an online machine learning course?

    Completing an online machine learning course will help you pursue roles such as machine learning engineer, data scientist, AI engineer, data analyst, and research scientist, amongst others. These roles are in demand across sectors like finance, healthcare, technology, and retail.

  • Which industries use machine learning the most?

    Machine learning is most commonly used in the following industries: manufacturing, for quality control and predictive maintenance; healthcare, for better diagnostics and tailored treatments; retail and e-commerce, for better customer experience and recommendation engines; finance, for fraud detection and risk analysis; and technology, where ML propels advancements in AI, data processing, and automation. These industries use machine learning to increase productivity, reduce expenses, and provide better goods and services.

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