HCTPL

Machine Learning Training

“covering topics like supervised learning, unsupervised learning, deep learning, reinforcement learning, and data preprocessing. Include practical examples, case studies, and hands-on projects to enhance learning outcomes.”

   Participants are introduced to the field of machine learning, its applications, and its importance in various industries. They learn about the basic principles and concepts underlying machine learning, including supervised learning, unsupervised learning, and reinforcement learning.

   Participants learn about different types of machine learning algorithms, including:

   – Supervised Learning: Algorithms learn from labeled data to make predictions or decisions.

   – Unsupervised Learning: Algorithms learn from unlabeled data to discover patterns or structures.

   – Semi-supervised Learning: Algorithms learn from a combination of labeled and unlabeled data.

   – Reinforcement Learning: Agents learn to interact with an environment to achieve a goal through trial and error.

   Participants learn about the importance of data preprocessing in machine learning. They understand how to clean, normalize, and transform data to make it suitable for training machine learning models. They also learn about techniques for handling missing values, outliers, and imbalanced datasets.

   Participants learn about feature engineering techniques for selecting, extracting, and transforming features from raw data. They understand how to create new features, encode categorical variables, and perform dimensionality reduction using methods such as principal component analysis (PCA) and feature scaling.

   Participants learn about different machine learning models and algorithms, including:

   – Regression: Linear regression, polynomial regression, ridge regression, and lasso regression.

   – Classification: Logistic regression, decision trees, random forests, support vector machines (SVM), and k-nearest neighbors (k-NN).

   – Clustering: K-means clustering, hierarchical clustering, and density-based clustering.

   – Neural Networks: Feedforward neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN).

   Participants learn about techniques for training machine learning models, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent. They understand how to optimize model hyperparameters using techniques such as grid search and random search, and how to prevent overfitting using regularization techniques.

 

   Participants apply their knowledge and skills to real-world machine learning projects and case studies. They work on hands-on exercises and projects to solve problems in domains such as healthcare, finance, e-commerce, and natural language processing.

 

Participants learn how to integrate Salesforce with other systems and applications using APIs, web services, and middleware tools. They explore the Salesforce AppExchange marketplace and understand how to install and configure third-party apps to extend Salesforce functionality.

Participants learn how to train and support Salesforce users within their organization. They understand how to provide ongoing training, documentation, and support resources to help users effectively utilize Salesforce for their day-to-day tasks.

Throughout the training program, participants receive guidance and practice materials to prepare for Salesforce certification exams. They learn exam objectives, study tips, and strategies to pass the exams and earn Salesforce certifications such as Salesforce Administrator (ADM 201).

 

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Refund Policy

At Hari Cornucopia Tech Private Limited, we prioritize customer satisfaction. Therefore, we offer a refund policy to ensure that participants have peace of mind when enrolling in our courses. If a participant is dissatisfied after the first class, they are eligible for a refund. However, once they attend the second class, the refund policy becomes void.