Machine Learning Using Python Training

Cybersecurity Training
  • Online (Microsoft Teams)
  • +971 562069465
  • Machine Learning using Python is a training program that teaches individuals how to apply machine learning concepts and techniques using the Python programming language. Machine learning is a field of artificial intelligence that focuses on developing algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed.

    The course is designed to provide participants with a practical understanding of machine learning concepts and the skills needed to implement machine-learning solutions using Python. These courses often include a mix of lectures, hands-on coding exercises, projects, and assessments to ensure a well-rounded learning experience.

  • By the end of this course, participants should be able to:

    • Define machine learning and its various types (supervised, unsupervised).
    • Explain the key concepts such as features, labels, training, and testing.
    • Utilize Python for data manipulation, analysis, and visualization.
    • Familiarize with essential libraries like NumPy, Pandas, and Matplotlib.
    • Clean and preprocess data, handling missing values and outliers.
    • Encode categorical variables and scale/normalize features.
    • Implement linear regression for regression tasks.
    • Apply logistic regression for binary classification.
    • Apply k-means clustering for grouping similar data points.
    • Implement hierarchical clustering for creating a tree of clusters.
    • Understand and apply cross-validation techniques.
    • Evaluate models using metrics like accuracy, precision, recall, and F1 score.
    • Optimize model performance through hyperparameter tuning.
    • Understand the basics of neural networks.
    • Present and interpret the results of machine learning models effectively.
  • Training will be conducted via Microsoft Team Meeting. Meeting invites will be shared on the day before the first day of training.

    • 13 days (78 hours)
    • Presentation Slides
    • Training Recordings
    • Study References
  • Upon successful completion of training, participants will receive a certificate.

  • This course is appropriate for a wide range of professionals but not limited to:

    • Software Developers and Programmers
    • Business Analysts and Decision Makers
    • Engineers and Researchers
    • Students and Graduates
    • Data Scientists and Analysts
    • Entrepreneurs and Business Owners
    • Anyone Interested in Machine Learning
  • Basic Programming Knowledge

    • Having experience in programming, preferably in Python.

    Mathematics and Statistics Foundation

    • Basic understanding of mathematical and statistical concepts, though not always mandatory.

    Curiosity and Motivation

    • Enthusiasm to learn and explore machine learning concepts and applications.
  • Participants can avail a discount of either an early bird or group discount whichever is higher with an additional discount when signing up for 2 or more courses.

    Group Discount (same company only)

    • 15% Discount for groups of 5 or more
    • 10% Discount for groups of 3-4

    Bundle Discount

    • Sign up for 2 courses and get an extra 10% off
    • Sign up for 3 courses and get an extra 15% off
how can we help you?

Contact us at the Velosi office nearest to you or submit a business inquiry online.

Fees + VAT as applicable

Tax Registration Number: 100442245500003

(including coffee breaks and a buffet lunch daily)

Course Outline

  • Module 1: Introduction to Machine Learning

    • What is Machine Learning?
    • Types of Machine Learning: Supervised, Unsupervised
    • Applications and Case Studies
  • Module 2: Python Basics for Machine Learning

    • Introduction to Python
    • NumPy, Pandas, and Matplotlib for Data Manipulation and Visualization
    • Jupyter Notebooks for Interactive Coding
  • Module 3: Data Preprocessing

    • Data Cleaning and Handling Missing Values
    • Encoding Categorical Data
    • Feature Scaling and Normalization
    • Train-Test Splitting
  • Module 4: Supervised Learning Algorithms

    • Linear Regression
    • Polynomial regression
    • KNN for regression
    • Logistic Regression
    • Decision Trees and Random Forests for classification
    • KNN for classification
    • Naïve Bayes for classification
  • Module 5: Unsupervised Learning Algorithms

    • Clustering concept
    • K-Means Clustering
    • Hierarchical Clustering
  • Module 6: Model Evaluation and Hyperparameter Tuning

    • Confusion matrix
    • Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
    • Hyperparameter Tuning Techniques
  • Module 7: Natural Language Processing (NLP)

    • Basics of NLP
    • Text Processing and Feature Extraction
    • Sentiment Analysis Project
  • Module 8: Introduction to Deep Learning

    • Basics of Neural Networks
    • Introduction to TensorFlow and Keras
    • Building and Training Neural Networks using ANN algorithm