Machine Learning

Internship course in Machine Learning, provides hands-on exercises, practical coding assignments, and projects to apply their knowledge and skills in machine learning. This course is designed to provide a foundational understanding of machine learning and its practical applications in a relatively short timeframe.

  • 4.4
  • 68,395 Students
  • Placement Assistance
  • 6 Weeks, 1 hr/day (flexible timings)

    Key Course Features

    8 Weeks, 1 hr/day (flexible timings)
    Lesson Videos: 40
    Internship Certification
    Placement Assistance
    Affordable fee and EMI facility
    Live Classes and Self-Paced Learning modes
    Industry live projects under the supervision of 15+ experienced trainer
    Course material, quizzes, assignments to assist in learning

    Why to take Machine Learning Course:

    • Gain a solid understanding of core machine learning concepts, including supervised and unsupervised learning, neural networks, and reinforcement learning.
    • Acquire skills in data collection, cleaning, and preprocessing.
    • Apply supervised learning algorithms for regression and classification tasks.
    • Develop the ability to build, train, and evaluate deep neural networks using frameworks like TensorFlow or PyTorch.
    • Apply machine learning techniques to real-world problems and case studies.
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    Industry recognized Machine Learning certification

    1. Upskill Intern certification is trusted by 10,000+ companies in industry for hiring
    2. Get physical copy of certificate to your address

    Course Curriculum

    • Overview of machine learning and its applications.
    • Types of machine learning: supervised, unsupervised, and reinforcement learning.
    • Introduction to key concepts: data, features, labels, and models.
    • Data collection, cleaning, and preprocessing techniques.
    • Data visualization and exploration using libraries like Matplotlib and Seaborn.
    • Handling missing data and outliers.
    • Introduction to supervised learning.
    • Linear regression for regression problems.
    • Classification algorithms, including logistic regression, decision trees, and k-nearest neighbors.
    • Techniques for model evaluation, including cross-validation and train-test split.
    • Metrics for regression (e.g., mean squared error) and classification (e.g., accuracy, precision, recall).
    • Overfitting and underfitting, and methods to address them.
    • Introduction to unsupervised learning.
    • Clustering algorithms like K-Means and hierarchical clustering.
    • Dimensionality reduction techniques such as Principal Component Analysis (PCA).
    • Techniques for feature engineering to improve model performance.
    • Feature selection methods, including filter, wrapper, and embedded approaches.
    • Importance of domain knowledge in feature engineering.
    • Introduction to artificial neural networks.
    • Building and training deep neural networks using libraries like TensorFlow or PyTorch.
    • Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
    • Preparing models for deployment in production environments.
    • Introduction to cloud-based machine learning platforms.
    • API development and model serving.
    • Understanding bias and fairness in machine learning.
    • Ethical considerations in data collection, model development, and deployment.
    • Regulatory and compliance issues.
    • Exploration of real-world machine learning applications, such as recommendation systems, natural language processing, and computer vision.
    • Case studies and examples of machine learning in various industries.
    • Recap of key concepts and takeaways from the crash course.
    • Recommendations for further reading, resources, and online courses on machine learning.
    Rajesh Kumar

    About Trainer

    Machine Learning Trainer with a passion for unraveling the complexities of artificial intelligence, is a prominent figure in the Indian tech landscape. With over a decade of experience in the field, Rajesh has honed his expertise in teaching and mentoring aspiring data scientists and engineers. His LinkedIn profile showcases a rich tapestry of accomplishments, including a Master's degree in Computer Science from a renowned Indian university and a string of successful machine learning projects at top-tier tech companies. Rajesh's dedication to knowledge dissemination is evident through his engaging online courses, workshops, and insightful articles on cutting-edge ML techniques. His commitment to nurturing the next generation of AI talent is truly inspiring, making him a sought-after figure in the Indian machine learning community.

    Reviews from students

    Priya
    Chennai, Tamil Nadu

    I found the Machine Learning course to be incredibly insightful. The instructors were knowledgeable and provided hands-on experience that helped me grasp complex concepts. It's definitely worth the investment.

    Arjun
    Bangalore, Karnataka

    As a computer science student, this Machine Learning course was a game-changer. It equipped me with the skills needed to work on real-world projects. I highly recommend it to anyone interested in AI and ML.

    Nandini
    Hyderabad, Telangana

    The Machine Learning course exceeded my expectations. The practical focus allowed me to develop algorithms and models. I appreciated the diverse cohort that added to the overall learning experience.

    Rajesh
    Delhi, NCR

    The Machine Learning course was top-notch. The instructors were experts in the field, and the curriculum was well-structured. It provided a strong foundation in AI and ML, which has been valuable in my career.

    Ayesha
    Jaipur, Rajasthan

    I was initially skeptical about enrolling in the Machine Learning course, but it turned out to be an excellent decision. It opened up career opportunities in the tech industry that I hadn't imagined before.

    Ankit
    Lucknow, Uttar Pradesh

    This Machine Learning course was a great learning experience. The practical assignments challenged me to think critically and creatively. I'm grateful for the knowledge I gained and the career doors it has opened for me.

    FAQs

    What is machine learning, and why should I learn it?

    • Machine learning is a subfield of artificial intelligence (AI) that focuses on developing algorithms and models that can learn from data and make predictions or decisions. Learning machine learning is valuable because it enables you to solve complex problems, make data-driven decisions, and build intelligent applications across various domains.

    Do I need a strong background in mathematics to take a machine learning course?

    While a background in mathematics, particularly in linear algebra, calculus, and probability, can be helpful, many machine learning courses cater to individuals with varying levels of mathematical expertise. You can find introductory courses that start with the basics and gradually delve into more advanced concepts.

    What programming languages are commonly used in machine learning courses?

    Python is the most widely used programming language in the field of machine learning due to its extensive libraries and frameworks like TensorFlow, PyTorch, and scikit-learn. Most machine learning courses will use Python for demonstrations and hands-on exercises.

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

    The duration of a machine learning course can vary significantly depending on the level of the course, your prior knowledge, and the depth of the material. Introductory courses can be completed in 8 weeks.

    What are the prerequisites for enrolling in a machine learning course?

    • Prerequisites can vary, but a typical machine learning course may require a foundational understanding of programming, basic statistics, and mathematics. Some advanced courses may have additional prerequisites, such as knowledge of linear algebra and calculus.

    What is the difference between online and in-person machine learning courses?

    Online machine learning courses offer the flexibility to learn at your own pace and from anywhere with an internet connection. In-person courses, on the other hand, provide a structured classroom experience with face-to-face interaction with instructors and peers. The choice depends on your learning style and availability.

    What can I expect to learn in a typical machine learning course?

    In a machine learning course, you can expect to learn about fundamental concepts such as supervised and unsupervised learning, neural networks, model evaluation, and optimization techniques. You'll also gain practical experience in applying these concepts to real-world problems through hands-on projects and exercises.

    What is an online internship?

    • An online internship is a type of educational program that is delivered over the internet. Students can access course materials, lectures, and assignments online, allowing them to study and learn from their own locations.

    What are the benefits of taking this internship?

    • Benefits of this courses include flexibility in scheduling, the ability to learn from anywhere, a wide range of course options, and the opportunity to balance education with work or other commitments.

    Can I interact with instructors?

    • Yes, we provide opportunities for interaction with instructors. This may include email communication, discussion forums, live video sessions.

    Is there a specific schedule for internship?

    • We offer flexibility in terms of when you study. Some have set deadlines for assignments and exams, while others are entirely self-paced, allowing you to complete coursework on your schedule.

    How do online internship work?

    • Online internship typically provide students with access to a learning management system (LMS) where they can find course materials, watch lectures, complete assignments, take quizzes or exams, and interact with instructors and fellow students through discussion forums or chat tools.

    How do I enroll in an online internship?

    • To enroll in an online internship, you typically need to visit the website, complete the registration process, pay any required fees, and follow the provided instructions for accessing the course materials.

    How are assignments and exams conducted?

    • Assignments and exams in online courses can vary. Some courses use online quizzes, written assignments, and discussion participation for assessment. Others may require students to take proctored exams, either in-person or online with remote proctoring services.

    How do I access technical support for online internship?

    • We provide technical support to help with issues related to course access, software, and hardware. Contact the course provider or institution's support team for assistance.

    What types of job placement assistance typically available for trained students?

    • Job placement assistance can vary but may include access to job boards, career counseling, resume review, interview preparation, and networking opportunities. We also have partnerships with companies that hire our graduates.

    Do employers value Upskill Intern certifications?

    • Many employers value Upskill Intern certifications, especially if they are relevant to the job role and skills needed. However, the recognition and value of online courses can vary by industry and employer.

    Is job placement guaranteed after completing internship?

    Learning management system, combines a wide range of features to present a class setting without having the students come into a physical classroom. It all depends on the WordPress plugin you go with, but in general.

    What if I need further career guidance or counseling after completing internship?

    • If you need additional career guidance or counseling, consider reaching out to career centers, career coaches, or mentors. Our institutions and organizations offer such services to help individuals navigate their career paths.

    How much does the internship course cost?

    • The cost of an internship varies widely depending course of selection, course content, and duration.

    Can I get financial aid or scholarships for internship?

    • Check with the HR for financial aid office to inquire about available options.

    What payment methods are accepted?

    • We accept various payment methods, including credit/debit cards, electronic bank transfers, and payment processors like Razorpay.

    Is there a payment plan available?

    • We offer payment plans that allow you to pay for the course in installments rather than in a lump sum. Check with HR for information on available payment plans.

    Is there a refund policy?

    • We have refund policies that specify deadlines and conditions for requesting a refund. Be sure to understand the refund policy before enrolling in a course.

    When do I need to make the payment?

    • You need to make part payment at the time of registration. Make sure to adhere to the specified deadlines to secure your enrollment.
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