Machine Learning I

Categories: Data Science
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Course Content

1.1 Introduction to Machine Learning

  • What is Machine Learning
    05:28
  • Supervised Learning – Regression
    06:57
  • Supervised Learning – Classification
    07:17
  • Unsupervised Learning – Clustering
    08:54
  • Unsupervised Learning – Dimensionality Reduction
    03:40
  • Machine Learning Fundamentals Quiz

1.2 Data Preparation with Scikit Learn

1.3 Hyperparameters & Model Validation

1.4 Feature Engineering
The previous sections outline the fundamental ideas of machine learning, but all of the examples assume that you have numerical data in a tidy, [n_samples, n_features] format. In the real world, data rarely comes in such a form. With this in mind, one of the more important steps in using machine learning in practice is feature engineering: that is, taking whatever information you have about your problem and turning it into numbers that you can use to build your feature matrix. In this section, we will cover a few common examples of feature engineering tasks: features for representing categorical data, features for representing text, and features for representing images. Additionally, we will discuss derived features for increasing model complexity and imputation of missing data. Often this process is known as vectorization, as it involves converting arbitrary data into well-behaved vectors.

1.5 Data Preparation Project

2.1 Linear Regression

2.2 Advanced Linear Regression

2.3 Regularisation

2.5 Regression Weekly Project

3.1 Naives Bayes & KNN
In this week, you are going to go through classification projects, starting with KNN, Logistic Regression, SVMs , Tree Based models, to ensemble learning models. For the python implementation of the models,we will use one notebook throughout the course, which has been included as a resource that you can download and follow along with in this course.

3.2 Logistic Regression

3.3 Support Vector Machines

3.4 Trees & Random Forests

3.4 More on Ensemble Learning Algorithms

3.5 Classification Weekly Project

4.1 Applying Machine Learning Models

4.2 Regression & Classification Metrics

4.3 Machine Learning System Design

4.5 Model Tuning Weekly Project

5.0 Conclusion and Next Steps

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