Machine Learning and AI In
Machine Learning Basics

This Machine Learning Basics course is designed to introduce students to the fundamental concepts of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. Students will learn how to implement machine learning algorithms using popular libraries such as scikit-learn and TensorFlow.

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Overview

Machine Learning Basics

Course Learning Outcomes (CLOs) and SLOs

Course Learning Outcomes (CLOs) typically include:

  • Understanding Machine Learning: Develop a solid understanding of the core concepts and principles of machine learning, including supervised, unsupervised, and reinforcement learning.
  • Algorithms and Techniques: Learn various machine learning algorithms and techniques, such as linear regression, decision trees, clustering, and neural networks.
  • Evaluation and Validation: Understand methods for evaluating and validating machine learning models to ensure robust performance and generalization.
  • Application of Machine Learning: Explore practical applications of machine learning across different domains, such as healthcare, finance, and natural language processing.
    Student Learning Outcomes (SLOs) are specific goals for students, such as:
  • Algorithm Implementation: Implement and apply machine learning algorithms using programming languages like Python or R.
  • Model Selection and Tuning: Select appropriate models for different types of data and tasks, and optimize model parameters for improved performance.
  • Data Preprocessing: Preprocess and prepare data for machine learning tasks, including feature engineering and handling missing values.
  • Project Development: Develop and execute machine learning projects, from data acquisition and preprocessing to model training, evaluation, and deployment.

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    Study contents

    Contents

    Section 1: Introduction to Machine Learning


  • What is Machine Learning?
  • History of Machine Learning
  • Applications of Machine Learning
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

    Section 2: Supervised Learning


  • Definition and types of supervised learning (Classification, Regression)
  • Loss functions (Mean Squared Error, Cross-Entropy) Optimization algorithms (Gradient Descent, Stochastic Gradient Descent)
  • Evaluation metrics (Accuracy, Precision, Recall, F1 Score)

    Section 3: Unsupervised Learning


  • Definition and types of unsupervised learning (Clustering, Dimensionality Reduction)
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

    Section 4: Reinforcement Learning


  • Definition and types of reinforcement learning (Value-Based, Policy-Based)
  • Markov Decision Processes (MDPs)
  • Value-Based Methods (Q-Learning, SARSA)
  • Policy-Based Methods (Policy Gradient Methods, Deep Deterministic Policy Gradients)

    Section 5: Neural Networks


  • Introduction to Neural Networks Perceptron Model
  • Multilayer Perceptron (MLP)
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)

    Section 6: Deep Learning


  • Introduction to Deep Learning
  • Convolutional Neural Networks (CNNs) for Computer Vision
  • Recurrent Neural Networks (RNNs) for Natural Language Processing
  • Autoencoders and Generative Adversarial Networks (GANs)

    Section 7: Model Evaluation and Selection


  • Model Evaluation Metrics
  • Model Selection Techniques (Cross-Validation, Model Comparison)
  • Hyperparameter Tuning

    Section 8: Machine Learning Algorithms


  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Support Vector Machines (SVMs)
  • k-Nearest Neighbors (k-NN)

    Section 9: Advanced Topics


  • Transfer Learning
  • Attention Mechanisms
  • Explainability and Interpretability in Machine Learning
  • Adversarial Attacks and Defenses

    Section 10: Real-World Applications


  • Image Classification with Convolutional Neural Networks
  • Natural Language Processing with Recurrent Neural Networks
  • Recommendation Systems with Collaborative Filtering

    • Admission

    Admission Criteria

    Prerequisites: Basic understanding of programming concepts (Python, R, or any other programming language) Familiarity with mathematics and statistics (linear algebra, calculus, probability) Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib) Admission Process: Step 1: Understand the Basics of Machine Learning Learn about supervised and unsupervised learning, regression, classification, clustering, and neural networks Familiarize yourself with popular machine learning algorithms (e.g., Linear Regression, Decision Trees, Random Forests) Study the concept of bias-variance tradeoff and overfitting Step 2: Choose a Programming Language Select a programming language for machine learning (Python is a popular choice) Install necessary libraries and tools (e.g., scikit-learn, TensorFlow, Keras) Step 3: Get Familiar with Data Preprocessing Learn about data preprocessing techniques (e.g., data cleaning, feature scaling, feature selection) Understand the importance of data quality and handling missing values Practice working with datasets to prepare them for machine learning models Step 4: Learn Model Evaluation and Selection Understand how to evaluate the performance of machine learning models (e.g., accuracy, precision, recall, F1-score) Learn how to compare and select the best model for a given problem Study techniques for hyperparameter tuning and model selection Step 5: Practice with Real-World Datasets Find publicly available datasets (e.g., UCI Machine Learning Repository, Kaggle Datasets) Practice implementing machine learning models on real-world datasets Experiment with different algorithms and hyperparameters to improve model performance Step 6: Learn Advanced Topics Study advanced topics in machine learning (e.g., deep learning, natural language processing, computer vision) Learn about state-of-the-art techniques and recent advancements in the field Step 7: Join Online Communities and Take Online Courses Join online communities (e.g., Kaggle, Reddit's r/MachineLearning) to stay updated with the latest developments Take online courses or attend webinars to further your knowledge and skills

    Careers

    Career Path

    Data Scientist: Work with large datasets to identify patterns, build predictive models, and make data-driven decisions. Machine Learning Engineer: Design, develop, and deploy machine learning models for various applications, such as natural language processing, computer vision, or recommender systems. Artificial Intelligence (AI) Researcher: Conduct research in AI and machine learning to develop new algorithms, techniques, and models that can be applied to various fields. Predictive Modeling Specialist: Build predictive models using machine learning to forecast outcomes, identify trends, and make informed decisions. Business Analyst: Apply machine learning techniques to analyze business data and make data-driven recommendations to improve business operations. Computer Vision Engineer: Work on image and video analysis, object detection, and recognition using machine learning algorithms. Natural Language Processing (NLP) Specialist: Develop NLP models for text analysis, sentiment analysis, language translation, and chatbots. Recommendation System Developer: Design and implement personalized recommendation systems for e-commerce, social media, or other applications. Automated Decision Systems (ADS) Developer: Build systems that use machine learning to automate decision-making processes in areas like finance, healthcare, or marketing. Quantitative Analyst: Apply machine learning techniques to financial modeling, risk analysis, and portfolio optimization in finance. Operations Research Analyst: Use machine learning and optimization techniques to improve operational efficiency in industries like logistics, supply chain management, or energy management. Healthcare Data Analyst: Analyze healthcare data using machine learning to identify trends, predict patient outcomes, and improve treatment plans. Marketing Analyst: Use machine learning to analyze customer behavior, predict consumer preferences, and optimize marketing campaigns. Risk Analysis Specialist: Apply machine learning to identify and mitigate risks in areas like finance, cybersecurity, or environmental sustainability. Education Technology (EdTech) Specialist: Develop educational software using machine learning to personalize learning experiences for students. Digital Forensics Analyst: Use machine learning to analyze digital evidence in criminal investigations and cybersecurity incidents. Robotics Engineer: Combine machine learning with robotics to develop intelligent robots that can perceive their environment and interact with humans. Game Developer: Apply machine learning to create more realistic game environments, NPCs (non-player characters), and game mechanics. Cybersecurity Specialist: Use machine learning to detect and prevent cyber attacks by analyzing network traffic patterns and identifying anomalies. Academic Researcher: Pursue a career in academia, conducting research in machine learning and its applications in various fields.

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    Tuition fees

    Machine Learning Basics (Duration 4 Weeks)

    This opens up chances for Machine Learning engineers, data scientists, data mining specialists, and data engineers in various industries. Therefore, if you aspire to be among the industry's most valuable professionals, you must learn machine learning.

    350 $

    200 $ / Total Cost
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