Introduction to Complete guide to Machine learning

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Course Overview

The MachineLearning Speciality course is a comprehensive program designed to equip learners with a deep understanding of data science and machine learning concepts. It is structured in various modules, starting with an Introduction to DataScience & Machine Learning, covering the essentials such as analytics types, project lifecycle, and required skills. The course then delves into practical skills with Python for Data Analysis & PreProcessing, teaching the use of popular libraries and data handling techniques. Subsequent modules focus on Supervised Machine Learning for both regression and classification, where learners gain hands-on experience with models like linear regression, logistic regression, SVMs, decision trees, and more. The course emphasizes the importance of Feature Selection and Dimensionality ReductionCross-Validation & Hyperparameter Tuning, and introduces Deep Learning fundamentals. Additionally, learners explore Clustering techniques to uncover patterns in data. By the end of the course, participants will have mastered the key concepts and tools necessary for a career in machine learning, including Python programming, data preprocessing, model evaluation, and advanced algorithms. This course offers a blend of theoretical knowledge and practical application, ensuring learners are well-prepared for real-world data science challenges.


Learning Objectives

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In the Machine Learning Speciality course, students will gain comprehensive knowledge and skills in data science and machine learning, from fundamentals to advanced techniques, including hands-on experience with real-world applications.

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  • Understand the necessity of data science and machine learning in solving complex problems and enhancing decision-making.
  • Differentiate between descriptive, predictive, and prescriptive analytics and their applications.
  • Master the data science project lifecycle, from conception to deployment.
  • Acquire the essential skills required for a data scientist role, including statistical knowledge and programming expertise.
  • Explore various types of machine learning such as supervised, unsupervised, and reinforcement learning.
  • Gain proficiency in Python and its libraries for data analysis, visualization, and machine learning model building.
  • Conduct exploratory data analysis (EDA) and apply various data preprocessing techniques, including handling missing values and categorical data.
  • Develop and evaluate machine learning models using regression and classification techniques, understanding key concepts like overfitting and model selection.
  • Implement feature selection, and dimensionality reduction, and understand their impact on model performance.
  • Apply cross-validation and hyperparameter tuning to optimize model performance, and gain hands-on experience with these techniques.
  • Dive into deep learning, construct neural networks using Keras and TensorFlow, and comprehend essential concepts like activation functions and optimization algorithms.

Course Prerequisites

To ensure that our students are well-prepared to take on the challenges of the Machine Learning Specialty course, the following prerequisites are recommended:

  • Basic Understanding of Programming: Familiarity with any programming language, preferably Python, as it is commonly used in data analysis and machine learning.
  • Fundamentals of Mathematics: Knowledge of high school level mathematics, including algebra and statistics, to understand the algorithms and methods used in machine learning.
  • Analytical Skills: Ability to think analytically and solve problems as machine learning involves a lot of data analysis and interpretation.
  • Understanding of Basic Data Handling: Exposure to handling and manipulating data, even at a basic level, will be beneficial for modules involving data preprocessing and exploratory data analysis.

We designed our course to be accessible to individuals with diverse backgrounds, and we provide introductory lessons to bridge knowledge gaps. Our goal is to empower learners with the skills needed to excel in the field of machine learning, without overwhelming them with excessive prerequisites.


Target Audiance

  • Research Scientists
  • AI Enthusiasts
  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • Software Developers interested in ML
  • IT Professionals looking to transition into data roles

Schedule Dates

Introduction to Complete guide to Machine learning
15 July 2024 - 19 July 2024
Introduction to Complete guide to Machine learning
21 October 2024 - 25 October 2024
Introduction to Complete guide to Machine learning
27 January 2025 - 31 January 2025
Introduction to Complete guide to Machine learning
28 April 2025 - 02 May 2025

Course Content

  • Applications of machine learning
  • What is machine learning?
  • Supervised learning
  • Unsupervised learning
  • Linear regression model part 1
  • Linear regression model part 2
  • Gradient descent
  • Implementing gradient descent
  • Gradient descent for linear regression
  • Running gradient descent

  • Feature scaling part 1
  • Feature scaling part 2
  • Checking gradient descent for convergence
  • Choosing the learning rate
  • Feature engineering
  • Polynomial regression

  • Logistic regression
  • Decision boundary
  • Cost function for logistic regression
  • The problem of overfitting
  • Addressing overfitting
  • Regularized linear regression

  • What is clustering?
  • K-means intuition
  • K-means algorithm
  • Optimization objective
  • Choosing the number of clusters
  • Gaussian (normal) distribution
  • Anomaly detection algorithm
  • Developing and evaluating an anomaly detection system
  • Anomaly detection vs. supervised learning
  • Choosing what features to use

  • 49999 New York taxi trips
  • Cleaning the taxi data
  • It's time. More predictors.
  • One more tree!
  • One tree is not enough
  • Plotting the predicted fare
  • Plotting the actual fare
  • Predicting taxi fares using a tree project
  • Where do people spend the most?


Machine learning is a subset of artificial intelligence where computer systems can learn from data and improve their performance over time without being explicitly programmed. In Dubai, it’s crucial due to its potential to enhance various sectors such as finance, healthcare, transportation, and more, aligning with Dubai’s vision of becoming a smart city.

Yes, several industries in Dubai benefit significantly from machine learning, including finance for fraud detection and risk assessment, healthcare for personalized medicine and diagnosis, transportation for traffic optimization, and retail for demand forecasting and customer analytics.

Dubai boasts several institutions and companies actively involved in machine learning research and application, including the Dubai Future Foundation, Dubai Data Establishment, and various startups focusing on AI and data analytics.

Individuals interested in machine learning careers can pursue relevant academic degrees, certifications, or attend workshops and boot camps offered by institutions like the Dubai Institute of Design and Innovation (DIDI), Dubai Data Science Training Institute, and various online platforms like Coursera and Udacity.

Challenges include data privacy concerns, the need for skilled professionals, regulatory frameworks, and ensuring the ethical use of AI technologies.

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