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Practical Machine Learning for Data Scientists

200.00 EGP

This course is a comprehensive introduction to AI and Machine Learning, targeting Data Scientists and Machine Learning engineers. It starts with setting the boundaries of Artificial Intelligence, Machine Learning, Deep Learning, and their relation to Data Science. What is expected as a member an AI team, and how to speak the same language. What is possible and what is not, and what defines a good AI project. The basics of supervised learning are covered, including the main ingredients of the Machine Learning problem, and the different solution setups. We cover both Linear models (Linear Regression, Logistic Regression, Support Vector Machines (SVM)) and Non-linear models (Polynomial Regression, Kernel SVM, Deep Neural Networks (DNN)). A universal approach is given to tackle any ML problem in a systematic way, covering data preparation, Exploratory Data Analysis (EDA), Model selection, Model evaluation, Model design, Fine tuning and Regularization. An end-to-end is given to illustrate this process with code in Google Colab Notebooks. We also cover the Machine Learning Meta algorithms and Ensemble methods: Voting, BAGGing, Boosting Decision Trees and Random Forests. Finally, we introduce unsupervised learning, covering dimensionality reduction algorithms, like Manifold Learning like Locally Linear Embedding (LLE) and Projection methods like Principal Component Analysis (PCA) and Clustering, like K-Means. Throughout the course, Python language is used. Popular Machine Learning libraries are used, like scikit-learn, in addition to pandas and keras.

    Pre-requisities

  • Python

  • Probability

  • Linear Algebra

    Topics Covered

  • Traditional programming vs. Statistical learning

  • AI vs. Machine learning vs. Deep learning

  • Different ML types: Supervised learning vs. Unsupervised learning vs. Self-supervised learning vs. Reinforcement learning

  • Linear models: Linear Regression, Logistic Regression, SVM

  • Non-Linear Classifiers: Polynomial Regression, Kernel SVM, Deep Neural Networks

  • Universal ML process: hyperparameters tuning, Regularization, Overfitting and underfitting

  • Evaluation protocols: Model Selection, Sampling, CrossValidation, Bootstrapping

  • Meta-Algorithms: Model Ensembles, Voting, BAGGing, Boosting, DecisionTrees, RandomForests

  • Unsupervised learning: clustering and dimensionality reduction

    What you will learn

  • Build solid knowledge necessary for data scientists about AI, Machine Learning and Deep Learning

  • Understand the basics and underlying dynamics of supervised learining models: LinearRegression, LogisiticRegression, SVM, DNN, DecisionTrees and RandomForests.

  • Get introduced to unsupervised learning approaches for dimensionality reduction and clustering.

  • Build practical Machine Learning models and pipelines using python, scikit-learn, pandas, keras and tensorflow

  • Solve practical problems like image classification, text classification, price prediction.