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Machine Learning with Scikit-learn

Video Description

Learn to implement and evaluate machine learning solutions with scikit-learn

About This Video

  • Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks
  • Learn how to build and evaluate the performance of efficient models using scikit-learn
  • A practical guide to master the basics and learn from real-life applications of machine learning

In Detail

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It also discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. By the end of this course, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

Table of Contents

  1. Chapter 1 : The Fundamentals of Machine Learning
    1. The Course Overview 00:03:49
    2. Defining Machine Learning 00:04:16
    3. Training Data, Testing Data, and Validation Data 00:02:27
    4. Bias and Variance 00:03:25
    5. An Introduction to Scikit-learn 00:03:30
    6. Installing Pandas, Pillow, NLTK, and Matplotlib 00:02:47
  2. Chapter 2 : Simple Linear Regression
    1. What Is Simple Linear Regression? 00:08:00
    2. Evaluating the Model 00:02:22
  3. Chapter 3 : Classification and Regression with k-Nearest Neighbors
    1. KNN, Lazy Learning, and Non-Parametric Models 00:04:08
    2. Classification with KNN 00:07:38
    3. Regression with KNN 00:04:13
  4. Chapter 4 : Feature Extraction
    1. Extracting Features from Categorical Variables 00:01:45
    2. Standardizing Features 00:01:48
    3. Extracting Features from Text 00:15:41
  5. Chapter 5 : From Simple Linear Regression to Multiple Linear Regression
    1. Multiple Linear Regression 00:04:31
    2. Polynomial Regression 00:02:24
    3. Regularization 00:02:16
    4. Applying Linear Regression 00:06:07
    5. Gradient Descent 00:02:06
  6. Chapter 6 : From Linear Regression to Logistic Regression
    1. Binary Classification with Logistic Regression 00:02:48
    2. Spam Filtering 00:07:36
    3. Tuning Models with Grid Search 00:02:04
    4. Multi-Class Classification 00:04:15
    5. Multi-Label Classification and Problem Transformation 00:04:35
  7. Chapter 7 : Naive Bayes
    1. Bayes' Theorem 00:03:33
    2. Generative and Discriminative Models 00:02:05
    3. Naive Bayes with Scikit-learn 00:04:04
  8. Chapter 8 : Nonlinear Classification and Regression with Decision Trees
    1. Decision Trees 00:01:55
    2. Training Decision Trees 00:10:09
    3. Decision Trees with Scikit-learn 00:06:03
  9. Chapter 9 : From Decision Trees to Random Forests and Other Ensemble Methods
    1. Bagging 00:03:54
    2. Boosting 00:02:56
    3. Stacking 00:02:33
  10. Chapter 10 : The Perceptron
    1. The Perceptron–Basics 00:06:50
    2. Limitations of the Perceptron 00:02:12
  11. Chapter 11 : From the Perceptron to Support Vector Machines
    1. Kernels and the Kernel Trick 00:04:38
    2. Maximum Margin Classification and Support Vectors 00:03:36
    3. Classifying Characters in Scikit-learn 00:06:45
  12. Chapter 12 : From the Perceptron to Artificial Neural Networks
    1. From the Perceptron to Artificial Neural Networks 00:02:09
    2. Feed-Forward and Feedback ANNs 00:01:36
    3. Multi-Layer Perceptrons and Training Them 00:08:30
  13. Chapter 13 : K-means
    1. Clustering 00:01:33
    2. K-means 00:05:29
    3. Evaluating Clusters 00:01:47
    4. Image Quantization 00:02:27
  14. Chapter 14 : Dimensionality Reduction with Principal Component Analysis
    1. Principal Component Analysis 00:06:38
    2. Visualizing High-Dimensional Data and Face Recognition with PCA 00:05:30