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Machine Learning

Harness the Power of Machine Learning: Learn, Apply, Innovate

  2. Artificial Intelligence
  3. Machine Learning
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ABOUT THE PROGRAM

Our Machine Learning course provides a thorough introduction to the field, combining theoretical knowledge with practical skills. You'll learn essential concepts such as supervised and unsupervised learning, data preprocessing, and model evaluation. The course includes hands-on experience with popular tools and frameworks like Scikit-Learn, TensorFlow, and PyTorch. You'll also explore advanced topics like deep learning and natural language processing. By course completion, you'll be ready to address real-world challenges and innovate with machine learning techniques.

 
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DELIVERY METHODS


Classroom Training

Online Training

Virtual Training

Onsite Training

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Reach us at +971-503735593, Building A1, Dubai Digital Park, Dubai Silicon Oasis, Dubai, United Arab Emirates or info@thehubofknowledge.com for more information.

PREREQUISITES

  • Basic knowledge of programming (preferably Python)
  • Understanding of basic statistics and probability
  • Familiarity with linear algebra and calculus is beneficial but not mandatory
  • Curiosity and willingness to engage with complex problems

TARGET AUDIENCE

  • Aspiring data scientists and machine learning engineers
  • Software developers interested in integrating machine learning into their applications
  • Data analysts looking to advance their skill set
  • Professionals in tech-related fields seeking to understand machine learning
  • Students and academics aiming to gain practical machine learning knowledge

WHAT WILL YOU LEARN?

  • Core Concepts of Machine Learning
  • Data Preprocessing Techniques
  • Model Development and Evaluation
  • Practical Implementation
  • Advanced Topics
  • Real-World Applications

PROGRAM OVERVIEW

Our Machine Learning course provides a thorough introduction to the field, combining theoretical knowledge with practical skills. You'll learn essential concepts such as supervised and unsupervised learning, data preprocessing, and model evaluation. The course includes hands-on experience with popular tools and frameworks like Scikit-Learn, TensorFlow, and PyTorch. You'll also explore advanced topics like deep learning and natural language processing. By course completion, you'll be ready to address real-world challenges and innovate with machine learning techniques.

 
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PROGRAM CONTENT

Machine Learning Course Outline

Module 1: Introduction to Machine Learning

  • Definition and scope

  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

  • Applications in real-world scenarios

  • Overview of ML pipeline

Tools: Python, Jupyter Notebook

Module 2: Python for Machine Learning

  • Numpy, Pandas, Matplotlib, Seaborn

  • Scikit-learn basics

  • Data cleaning and preprocessing

  • Exploratory Data Analysis (EDA)

Module 3: Supervised Learning - Regression

  • Linear Regression

  • Polynomial Regression

  • Ridge and Lasso Regression

  • Model evaluation metrics: MSE, RMSE, R²

Lab: Predicting housing prices

Module 4: Supervised Learning - Classification

  • Logistic Regression

  • k-Nearest Neighbors (k-NN)

  • Support Vector Machines (SVM)

  • Decision Trees and Random Forests

  • Evaluation: Confusion Matrix, Precision, Recall, F1 Score, ROC-AUC

Lab: Spam detection or diabetes prediction

Module 5: Unsupervised Learning

  • Clustering: K-Means, Hierarchical Clustering, DBSCAN

  • Dimensionality Reduction: PCA, t-SNE

  • Anomaly Detection

Lab: Customer segmentation

Module 6: Model Validation & Selection

  • Cross-validation

  • Bias-Variance tradeoff

  • Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

  • Underfitting vs Overfitting

Module 7: Neural Networks & Deep Learning (Intro)

  • Basics of neural networks

  • Perceptron, Activation Functions

  • Forward and Backpropagation

  • Introduction to TensorFlow/Keras or PyTorch

Lab: Digit recognition with MNIST

Module 8: Ensemble Learning

  • Bagging and Boosting

  • Random Forests, AdaBoost, Gradient Boosting, XGBoost

  • Stacking models

Lab: Titanic survival prediction

Module 9: Natural Language Processing (NLP)

  • Text preprocessing: Tokenization, Lemmatization, Stopwords

  • Bag of Words, TF-IDF

  • Naive Bayes classifier

  • Sentiment Analysis

Module 10: Time Series Forecasting

  • Understanding time series data

  • ARIMA models

  • Facebook Prophet

  • LSTM (optional, advanced)

Lab: Stock price prediction

Module 11: Reinforcement Learning (Optional Advanced Topic)

  • Markov Decision Processes (MDP)

  • Q-Learning

  • Deep Q-Networks (DQN)

Module 12: Capstone Project

  • Choose a real-world dataset

  • Apply complete ML pipeline

  • Present results (with visualization and performance metrics)

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