{"id":108262,"date":"2026-06-06T13:14:17","date_gmt":"2026-06-06T07:44:17","guid":{"rendered":"https:\/\/staging.nanoschool.in\/wp\/course\/machine-learning-ai-fundamentals-course\/"},"modified":"2026-06-29T11:04:43","modified_gmt":"2026-06-29T05:34:43","slug":"machine-learning-ai-fundamentals-course","status":"publish","type":"product","link":"https:\/\/staging.nanoschool.in\/wp\/course\/machine-learning-ai-fundamentals-course\/","title":{"rendered":"Machine Learning & AI Fundamentals Course"},"content":{"rendered":"
\n
\n
Attribute<\/div>\n
Detail<\/div>\n
Format<\/div>\n
Online (e-LMS)<\/div>\n
Level<\/div>\n
Beginner to Advanced<\/div>\n
Duration<\/div>\n
6 Months<\/div>\n
Certification<\/div>\n
e-Certification + e-Marksheet<\/div>\n
Tools<\/div>\n
Python, R, TensorFlow, Keras, scikit-learn<\/div>\n<\/div>\n

About the Machine Learning Course<\/h3>\n
Machine Learning & AI Fundamentals Course: Beginner to Advanced dives deep into Machine Learning & Ai Beginner To.<\/div>\n
Gain comprehensive expertise through our structured curriculum and hands-on approach.<\/div>\n
\n

Program Highlights<\/h3>\n
\n
\u2022 Comprehensive coverage of Machine Learning from fundamentals to advanced applications<\/div>\n
\u2022 Hands-on projects and real-world case studies in AI and Machine Learning<\/div>\n
\u2022 Expert-curated curriculum aligned with current industry standards<\/div>\n
\u2022 Access to recorded lectures and e-LMS platform for flexible, self-paced learning<\/div>\n
\u2022 e-Certification and e-Marksheet upon successful completion<\/div>\n
\u2022 Dedicated mentor support and interactive doubt-clearing sessions<\/div>\n
\u2022 Practical experience with tools: Python, R, TensorFlow, Keras<\/div>\n
\u2022 Career-oriented training for academic and professional growth in AI and Machine Learning<\/div>\n<\/div>\n<\/div>\n

Course Curriculum<\/h3>\n
\n
\n

Module 1: AI Fundamentals, Mathematics, and Machine Learning Foundations<\/h4>\n
    \n
  • Develop a comprehensive understanding of the mathematical prerequisites for machine learning, including linear algebra and calculus<\/li>\n
  • Analyze the fundamental concepts of artificial intelligence, including machine learning, deep learning, and neural networks<\/li>\n
  • Design a basic machine learning model using a supervised learning approach, including data preprocessing and feature selection<\/li>\n<\/ul>\n<\/div>\n
    \n

    Module 2: Data Engineering, Preprocessing, and Feature Pipelines<\/h4>\n
      \n
    • Configure a data pipeline using Apache Beam, including data ingestion, processing, and storage<\/li>\n
    • Implement data preprocessing techniques, including handling missing values, data normalization, and feature scaling<\/li>\n
    • Evaluate the effectiveness of different feature engineering techniques, including feature extraction and selection<\/li>\n<\/ul>\n<\/div>\n
      \n

      Module 3: Model Architecture, Algorithm Design, and Machine Learning Methods<\/h4>\n
        \n
      • Design a convolutional neural network (CNN) architecture for image classification, including convolutional and pooling layers<\/li>\n
      • Develop a recurrent neural network (RNN) model for natural language processing, including long short-term memory (LSTM) and gated recurrent units (GRU)<\/li>\n
      • Analyze the performance of different machine learning algorithms, including support vector machines (SVM) and k-nearest neighbors (KNN)<\/li>\n<\/ul>\n<\/div>\n
        \n

        Module 4: Training, Hyperparameter Optimization, and Evaluation<\/h4>\n
          \n
        • Implement a grid search algorithm for hyperparameter tuning, including learning rate and batch size optimization<\/li>\n
        • Evaluate the performance of a machine learning model using metrics, including accuracy, precision, and recall<\/li>\n
        • Develop a strategy for handling overfitting and underfitting, including regularization and early stopping<\/li>\n<\/ul>\n<\/div>\n
          \n

          Module 5: Deployment, MLOps, and Production Workflows<\/h4>\n
            \n
          • Configure a machine learning model for deployment using Docker, including containerization and orchestration<\/li>\n
          • Implement a continuous integration and continuous deployment (CI\/CD) pipeline using Jenkins, including automated testing and deployment<\/li>\n
          • Develop a monitoring and logging strategy for machine learning models in production, including metrics and alerts<\/li>\n<\/ul>\n<\/div>\n
            \n

            Module 6: Ethics, Bias Mitigation, and Responsible AI Practices<\/h4>\n
              \n
            • Analyze the ethical implications of machine learning, including bias, fairness, and transparency<\/li>\n
            • Develop a strategy for mitigating bias in machine learning models, including data preprocessing and feature selection<\/li>\n
            • Evaluate the effectiveness of different techniques for ensuring fairness and accountability in AI systems<\/li>\n<\/ul>\n<\/div>\n
              \n

              Module 7: Industry Integration, Business Applications, and Case Studies<\/h4>\n
                \n
              • Develop a machine learning solution for a real-world business problem, including data collection and preprocessing<\/li>\n
              • Implement a machine learning model for predictive maintenance, including sensor data analysis and anomaly detection<\/li>\n
              • Evaluate the effectiveness of different machine learning algorithms for recommender systems, including collaborative filtering and content-based filtering<\/li>\n<\/ul>\n<\/div>\n<\/div>\n
                \n

                Tools, Techniques, or Platforms Covered<\/h3>\n
                Python<\/span>
                \nR<\/span>
                \nTensorFlow<\/span>
                \nKeras<\/span>
                \nscikit-learn<\/span><\/div>\n<\/div>\n

                Real-World Applications<\/h3>\n
                \n
                  \n
                • Apply Machine Learning skills directly to academic research, thesis work, and publications<\/li>\n
                • Build a professional portfolio showcasing practical AI and Machine Learning competencies<\/li>\n
                • Solve industry-relevant problems using Machine Learning methodologies and tools<\/li>\n
                • Contribute to open-source projects and collaborative research in AI and Machine Learning<\/li>\n
                • Prepare for competitive examinations, interviews, and professional certifications in AI and Machine Learning<\/li>\n<\/ul>\n<\/div>\n

                  Who Should Attend & Prerequisites<\/h3>\n
                  \n
                    \n
                  • Designed for Professionals.<\/li>\n
                  • Designed for Students.<\/li>\n
                  • No prior experience required. Basic interest in artificial intelligence is sufficient.<\/li>\n
                  • Mentorship by industry experts and NSTC faculty.<\/li>\n<\/ul>\n

                    Prerequisites:<\/strong><\/p>\n<\/div>\n

                    Frequently Asked Questions<\/h3>\n
                    \n
                    \n
                    1. What is the format of this Machine Learning & AI Fundamentals Course course?<\/div>\n
                    This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.<\/div>\n<\/div>\n
                    \n
                    2. Will I receive a certificate after completing this course?<\/div>\n
                    Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.<\/div>\n<\/div>\n
                    \n
                    3. What are the prerequisites for this course?<\/div>\n
                    No prior experience is required. This course is designed for beginners and takes you step by step from the basics to advanced topics.<\/div>\n<\/div>\n
                    \n
                    4. How long will I have access to the course materials?<\/div>\n
                    You will have access to all course materials for the duration of 6 Months. The self-paced format allows you to learn according to your own schedule through our online learning management system.<\/div>\n<\/div>\n
                    \n
                    5. Is mentor support available during the course?<\/div>\n
                    Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to AI and Machine Learning. Our mentors are industry experts and experienced professionals.<\/div>\n<\/div>\n<\/div>\n
                    Enroll in Machine Learning & AI Fundamentals Course today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering AI and Machine Learning skills that matter.<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

                    Machine Learning & AI Fundamentals Course: Beginner to Advanced is a Beginner-level, 3 Weeks online program by NSTC. Master AI certification, AI Course, AI Education through hands-on projects, real datasets, and expert mentorship. Earn your e-Certification + e-Marksheet in machine learning & ai beginner. Designed for students and professionals seeking practical artificial intelligence expertise in India.<\/p>\n","protected":false},"featured_media":108411,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_members_access_role":[],"_members_access_error":""},"product_brand":[9487],"product_cat":[4657],"product_tag":[4660,6053,6047,12156,6060],"class_list":["post-108262","product","type-product","status-publish","has-post-thumbnail","product_brand-nstc","product_cat-artificial-intelligence","product_tag-ai","product_tag-data-science","product_tag-machine-learning","product_tag-programming","product_tag-python","post-wrapper","thrv_wrapper","first","instock","sale","downloadable","virtual","taxable","purchasable","product-type-simple"],"acf":[],"_links":{"self":[{"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/product\/108262","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/comments?post=108262"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/media\/108411"}],"wp:attachment":[{"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/media?parent=108262"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/product_brand?post=108262"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/product_cat?post=108262"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/staging.nanoschool.in\/wp\/wp-json\/wp\/v2\/product_tag?post=108262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}