Machine Learning Course in Kalyan Nagar

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Projects   4 +
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    Machine Learning Training Curriculum

    Curriculum Designed By Experts

    Expertly designed curriculum for future-ready professionals.

    Industry Oriented Curriculam

    An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company

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      30+  Case Studies & Projects
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      9+  Engaging Projects
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      10+   Years Of Experience
  • Overview of Machine Learning
  • Types of Machine Learning algorithms
  • Applications of Machine Learning in various domains
  • Introduction to Python and popular ML libraries
  • NumPy, Pandas, and scikit-learn
  • Data cleaning and handling missing values
  • Data transformation techniques (scaling, normalization)
  • Feature selection and engineering
  • Exploratory Data Analysis (EDA) using visualizations
  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • Naive Bayes Classifier
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Association Rule Learning (Apriori algorithm)
  • Anomaly detection
  • Training and testing datasets
  • Cross-validation techniques
  • Evaluation metrics (accuracy, precision, recall, F1-score)
  • Confusion matrix and ROC curves
  • Bias-Variance tradeoff
  • Bagging and Random Forests
  • Boosting and AdaBoost
  • Stacking and Blending
  • Hyperparameter tuning
  • Model selection techniques
  • Introduction to Neural Networks
  • Activation functions
  • Backpropagation and Gradient Descent
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Image Classification
  • Object Detection and Localization
  • Natural Language Processing (NLP)
  • Sequence-to-Sequence Models
  • Generative Adversarial Networks (GANs)
  • Markov Decision Processes (MDPs)
  • Q-Learning and Value Iteration
  • Policy Gradient Methods
  • Deep Q-Networks (DQN)
  • Applications of Reinforcement Learning
  • Transfer Learning
  • Explainable AI and Interpretability
  • Time Series Analysis
  • Autoencoders and Variational Autoencoders
  • Model Deployment and serving with cloud platforms
  • Show More

    Machine Learning Training Projects

    Become a Machine Learning Expert With Practical and Engaging Projects.

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      Practice essential Tools
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      Designed by Industry experts
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      Get Real-world Experience

    Classification of Iris

    Sort several iris flower varieties according to their unique characteristics by using the Iris collection. Classifier assessing, data preparation, and categorization.

    Prediction for Titanic Survival

    A variety of characteristics like as ages, genders, and passenger class, determine what percentage of people experienced the Titanic catastrophe.

    Recognition of Digits in Handwritten Text

    The proportion of persons who were affected by the Titanic disaster depends on a number of factors, including age, gender, and passenger class.

    Identifying Spam Emails

    Design a model for using machine learning methodologies in email classification to check whether an email is spam or not. Use NLP, text Classification, and Feature Extraction.

    Customer Churn Prediction

    Which clients, according to their usage and demographics, would likely stop using the service or product. Model evaluation, feature engineering, and classification.

    Recognition of Facial Emotions

    Create a model that uses picture data to identify emotions from facial expressions. CNNs, Deep Learning, and Image Classification.

    Autonomous driving simulation

    Develop a car-driven model for independent driving in a simulated environment using reinforcement learning. Reinforcement Learning, Simulation, and Deep Learning.

    GANs for Image Generation

    GANs can create realistic visuals - new artworks or improving low-resolution photographs. GANs, Deep Learning, and Image Synthesis.

    Machine Translation System

    Design a neural network model for machine-based text translation from one language to another. This is in regard to sequence-to-sequence models, natural language processing, and deep learning.

    Key Features

    Practical Training

    Global Certifications

    Flexible Timing

    Trainer Support

    Study Material

    Placement Support

    Mock Interviews

    Resume Building

    Batch Schedule

    Weekdays Regular (Class 1Hr - 1:30Hrs) / Per Session

    • 11-Nov-2024 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
    • 13-Nov-2024 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)

    Weekend Regular (Class 3Hrs) / Per Session

    • 09-Nov-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)

    Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session

    • 10-Nov-2024 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)

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      • Live projects guided by instructors are a characteristic of the virtual learning environment.
      • The curriculum includes of full-day lectures, practical exercises, and case studies.

      Machine Learning Training Overview

      Potential Career Paths for Programmers in Machine Learning

      A programming machine learning professional programmer can pursue various kinds of interesting career opportunities, ranging from technical proficiency in analyzing data and algorithm creation to software creation. Among the most common occupations are data scientist, who interprets complex statistics into business decision factors; machine learning engineer, who designs and releases predictive models; and AI researcher, who advances the field with innovative algorithms and methodologies. They may be used in financial services, healthcare, and automated software applications like diagnostic instruments, fraud identification systems, and autonomous vehicle technologies. There is always the prospect of finding employment in consultancy, academia, or product management, which provides opportunities for a difficult and rewarding career using AI solutions.

      What Does a Machine Learning Training Require?

      • Data Collection: Assemble a pertinent dataset that exemplifies the issue you're trying to resolve. For supervised learning, this might involve labelled data, or it can involve unlabeled data (for unsupervised learning).
      • Data processing: Tidy up and prepare the information. To do this, it might be necessary to handle missing values, normalize or standardize features, encode categorical variables, and divide the data into test, validation, and training sets.
      • Feature engineering and selection: Determine which features are most important or develop additional features that might enhance model performance.
      • Choosing a Model: Depending on the kind of problem, choose a suitable method or model architecture (e.g., regression, classification, clustering).
      • Model Training: The training dataset is used to train the model. This means feeding the data into the model and adjusting its variables utilizing optimization techniques like gradient.
      • Monitoring and Maintenance: Keep an eye on how well the model is working in the actual world and update or retrain it as needed to take into account fresh information or modifications to the underlying issue.

      Reasons to Consider Enrolling in Machine Learning Training

      There are plenty of benefits to signing up for a machine learning class-from helping enhance one's prospects and skills in one's professional career to perhaps the most important sectors of contemporary life: technological sectors transforming the face of many sectors-from healthcare and finance to create, among other things, a new gush of jobs. By structuring courses, acquiring foundational knowledge with hands-on experience in required algorithms and tools, you are able to not only understand complex concepts but apply them too. On top of this, you will acquire knowledge from the experts from the industry to bring you up to date with trends and best practices. The chances of interaction with peers and instructors can facilitate cooperation because they present channels for future projects or job placements. In totality, a machine learning Training equips the learner with skills to navigate and survive in this data-driven world; therefore, worthwhile in the investment of time as professional development.

      Techniques and Trends in Machine Learning Development

      • Deep Learning: Multi-layered neural networks are mostly used in the field of deep learning, and they perform incredibly well in many kinds of applications, including natural language processing, picture recognition, and speech recognition. RNNs and CNNs are widely used.
      • Transfer Learning: Transfer learning makes use of a pre-trained model, which then learns for the application of specific tasks. It reduces not just the quantity of data required but also shrinks the duration for training.
      • Reinforcement Learning: Reinforcement learning, in which the models are learnt on the right process of taking a decision by rewarding good actions and punishing bad ones. It finds its application most often in robotics and game playing.
      • Explainable AI (XAI): Explainable AI (XAI) Transparency is the rising norm in machine learning. XAI depends on making the model more interpretable, and this is needed in gaining confidence to be used in applications such as health care and finance.
      • Federated Learning: Learning for decentralized models where the model is spread across many devices and learns on data kept local on each device. That preserves privacy and security.
      • Edge AI: It brings machine learning capabilities to edge devices like IoT, which cuts latency and bandwidth usage while allowing real-time processing in so many applications.

      The Most Recent Machine Learning Tools

      The landscape of machine learning tools is in a state of constant change, and some of the recent innovations have gained ample popularity among data scientists and developers. TensorFlow 2.0 made life easier by improving the model development process with a user-friendly Keras API, which promoted fast prototyping and deployment. PyTorch remains a darling for most research due to its dynamic computation graph and support from a strong community. Here, Hugging Face's Transformers library brought significant change in natural language processing through offering pre-trained models that can easily be fine-tuned for one task or the other. This particularly bumps up model selection, therefore AutoML tools, including Google Cloud AutoML and H2O.ai, automate this, therefore, allowing more people to have access to machine learning and not only experts. MLflow is another popularly used open source for managing a lifecycle in the process of machine learning, from the experimentation phase up to deployment.

      Add-Ons Info

      Career Opportunities  After Machine Learning

      Machine Learning Engineer

      A machine learning engineer develops and designs systems and models for machine learning to solve complex problems. His interests go towards algorithms and handling large quantities of data.

      Data Scientist

      A data specialist uses artificial intelligence algorithms and statistical techniques to draw insights from the data so that they can come up with recommendations for finding the outcomes.

      AI Research Scientist

      AI research scientists conduct high-level research to advance artificial intelligence and machine learning; they develop new algorithms, techniques, and models, thereby contributing to academic paper.

      Machine Learning Analyst

      The machine learning analysts will apply machine learning techniques on business data so as to generate actionable insights. It focuses on predictive modelling.

      Deep Learning Engineer

      Deep Learning engineers focus much on the design and development of deep neural networks for applications in recognition of images, natural language processing.

      Business Intelligence (BI) Developer

      BI Developers Design the data-driven business intelligence solutions using machine learning and data analysis. Develop dashboards, reports, and data visualizations.


      Skill to Master
      Data Preprocessing and Cleaning
      Feature Engineering
      Algorithm Selection
      Model Training and Evaluation
      Hyperparameter Tuning
      ACross-Validation
      Dimensionality Reduction
      Model Deployment
      Data Visualization
      Statistical Analysis
      Natural Language Processing (NLP)
      Deep Learning Techniques
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      Tools to Master
      TensorFlow
      PyTorch
      scikit-learn
      Keras
      XGBoost
      LightGBM
      H2O.ai
      Apache Spark MLlib
      RapidMiner
      Weka
      Microsoft Azure Machine Learning
      Google Cloud AI Platform
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      Our Instructor

      Learn from certified professionals who are currently working.

      instructor
      Training by

      Arjun , having 10 yrs of experience

      Specialized in: Computer Vision, Image Processing, Deep Learning, and Big Data Technologies.

      Note: Arjun has experience in a wide range of image recognition and object identification tasks. He is committed to providing practical methods for creating and implementing machine learning models in practical contexts. He possesses substantial expertise in OpenCV and Keras.

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      Machine Learning Certification

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    • Google Professional Machine Learning Engineer
    • Microsoft Certified Azure AI Engineer Associate
    • IBM Data Science Professional Certificate
    • AWS Certified Machine Learning
    • Certified Machine Learning Professional (CMLP)