Cyber1Defense Communication Ltd > General Programs > Machine Learning – Beginner to Advanced

Machine Learning – Beginner to Advanced

The Machine Learning course is designed for individuals interested in gaining a comprehensive understanding of machine learning algorithms, techniques, and applications. This program covers the fundamental principles of machine learning, gradually progressing to advanced topics in deep learning, natural language processing, and reinforcement learning. Through hands-on exercises, practical projects, and real-world examples, students will develop the necessary skills to design, implement, and evaluate machine learning models effectively.


Instructor-led Course | On-Campus Course | Online

Monday, Tuesday, Friday : 3hrs (To Schedule)

4 weeks


Course Details

  • Module 1: Introduction to Machine Learning
    • Overview of machine learning and its applications
    • Different types of machine learning algorithms (supervised, unsupervised, reinforcement learning)
    • Understanding the machine learning workflow
    • Key concepts: features, labels, training, and testing

    Module 2: Data Preprocessing and Exploration

    • Data collection and preprocessing techniques
    • Handling missing data and outliers
    • Feature scaling and normalization
    • Exploratory data analysis and visualization
    • Feature engineering and selection

    Module 3: Supervised Learning Algorithms

    • Linear regression
    • Logistic regression
    • Decision trees and random forests
    • Support vector machines (SVM)
    • Naive Bayes classifiers

    Module 4: Unsupervised Learning Algorithms

    • Clustering algorithms (k-means, hierarchical clustering)
    • Dimensionality reduction techniques (Principal Component Analysis, t-SNE)
    • Association rule mining
    • Anomaly detection
    • Recommender systems

    Module 5: Neural Networks and Deep Learning

    • Introduction to neural networks
    • Activation functions and network architectures
    • Training neural networks (backpropagation, gradient descent)
    • Convolutional Neural Networks (CNNs) for image classification
    • Recurrent Neural Networks (RNNs) for sequence data

    Module 6: Natural Language Processing (NLP)

    • Text preprocessing techniques
    • Bag-of-Words and TF-IDF representations
    • Sentiment analysis and text classification
    • Named Entity Recognition (NER)
    • Language modeling and sequence generation

    Module 7: Reinforcement Learning

    • Introduction to reinforcement learning
    • Markov Decision Processes (MDPs)
    • Q-learning and Deep Q-learning
    • Policy gradient methods
    • Applications of reinforcement learning

    Module 8: Model Evaluation and Validation

    • Evaluation metrics (accuracy, precision, recall, F1 score)
    • Cross-validation techniques
    • Overfitting and regularization methods
    • Hyperparameter tuning and optimization
    • Model selection and ensemble learning

    Module 9: Model Deployment and Productionization

    • Model deployment strategies (cloud, containers)
    • RESTful APIs for model serving
    • Continuous integration and deployment (CI/CD)
    • Monitoring and performance evaluation
    • Ethical considerations in machine learning

    Module 10: Capstone Project and Practical Applications

    • Real-world machine learning project implementation
    • Capstone project focusing on designing, developing, and deploying a machine learning model
    • Presentation and documentation of the project

Problems Vs Solutions
How many times have you been stopped in your tracks by a problem? You look at the problem from all sides until you think you know that problem, but it still blocks your path. For centuries people have told us that a problem cannot exist without its solution existing at the same time, but the key is looking beyond the problem so that you can see the solution. When your focus is preventing you from seeing anything but the problem, meditations which relax your mind and guide you to finding solutions change from being a luxury to being a necessity.

Supported by a robust sales force and tight cost controls, Pharm Ltd. experienced sustained double-digit growth over a number of years, only to find that their supply chain struggled to keep pace. In particular, the initial state of the company’s sales and operations planning capabilities limited their ability to account for demand variability or raw material lead times in production and distribution. The work addressed three critical issues for Pharm Ltd.:

Here’s the key to super sales results in your coaching business. Stop selling “coaching” and find what makes your prospects jump into your shopping cart begging you for your rare coaching time slot.