A Machine Learning course provides individuals with the knowledge and skills necessary to understand and apply machine learning techniques to solve re...Read more

Helix Tech

Helix Tech

Helix Tech

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Course Highlights


Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computer systems to learn and make predictions or decisions from data.

This Machine Learning course offers a comprehensive introduction to the principles, algorithms, and practical applications of machine learning. Participants will gain a solid foundation in supervised and unsupervised learning, reinforcement learning, and deep learning. The course includes hands-on exercises and projects to reinforce learning and practical implementation.

Applying machine learning techniques to real-world datasets and problems.Advanced machine learning topics such as generative adversarial networks (GANs), transfer learning, and more, depending on course depth.


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At Helix Tech IT Solutions Inc, we believe that the success of a company is rooted in the talents and aspirations of its employees. We are committed to creating a positive impact on the tech industry by bridging the gap between top tech talent and companies while empowering job seekers to achieve their career aspirations. We believe in creating a thriving ecosystem where technology and innovation comes to life.


Course Curriculum

  • Definition of machine learning and its significance
  • Types of machine learning
  • Matplotlib
  • Seaborn
  • Plotly
  • Final EDA Project
  • Linear Regression
  • Logistic Regression
  • Gradient Boosting
  • Decision Tree
  • Time Series
  • Final Project
  • CNN
  • LSTM
  • RNN
  • Reinforcement Learning
  • Final Project
  • Regression
  • Logistic regression
  • decision trees
  • support vector machines
  • Basics of artificial neural networks (ANNs)
  • Deep learning architectures: convolutional neural networks (CNNs)
  • recurrent neural networks (RNNs)
  • deep learning frameworks
  • Text preprocessing and tokenization
  • Sentiment analysis
  • Strategies for deploying machine learning models in production
  • Containerization and cloud deployment

Professional Certificate

Beginner level

No previous experience necessary

Course Session

25 Days 2 Hours per day

Flexible schedule

Learn at your own pace

Course Key Features

You'll gain a solid understanding of fundamental machine learning concepts, including supervised learning, unsupervised learning, reinforcement learning, and deep learning

You'll learn how to clean, preprocess, and transform data, which is a critical step in preparing data for machine learning models

Skills Covered
  • Deep Learning
  • Hyperparameter Tuning
  • Reinforcement Learning
Job Roles
  • Machine Learning Engineer
  • Data Scientist
  • Computer Vision Engineer


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Interview Question

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. In traditional programming, rules are explicitly defined, while in ML, models learn patterns and rules from data.
Decision trees are a type of supervised learning algorithm used for both classification and regression. They partition data into subsets based on feature values and make decisions at each node. The tree is constructed to maximize information gain or minimize impurity.
Feature engineering involves selecting, transforming, or creating input features to improve a model's performance. It's important because the quality and relevance of features can significantly impact a model's ability to learn patterns in data.

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