Last updated 11/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.53 GB | Duration: 17h 32m

Machine Learning in practice with Python's own scikit-learn on real-world datasets!

What you'll learn
Predict the values of continuous variables using linear regression and K Nearest Neighbors.
Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically.
Build a portfolio of tools and techniques that can readily be applied to your own projects.
Use Support Vector Machines to learn how to train your model to predict the chances of heart disease.
Analyze the population and generate results in line with ethnicity and other factors using K-Means Clustering.
Understand the buying behavior of your customers using Customer Segmentation to drive the sales of your products.
Requirements
You need to have a very basic understanding of Machine Learning and Data Analytics. However, no knowledge of scikit-learn is needed. Python programming knowledge and a basic understanding of Numpy and the Pandas library are assumed.
Description
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. scikit-learn is arguably the most popular Python library for Machine Learning today. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for scikit-learn are in high demand, both in industry and academia.scikit-learn is one of the most powerful Python Libraries with has a clean API, and is robust, fast and easy to use. It solves real-world problems in the areas of health, population analysis, and figuring out buying behavior, and more!This comprehensive 4-in-1 course is an easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of algorithms for Machine Learning. You'll firstly learn how to build and evaluate the performance of efficient models using scikit-learn. Observe data from multiple angles and use machine learning algorithms to solve real-world problem to make your projects successful. Use Regression Trees, Support Vector Machines, K-Means Clustering, and customer segmentation algorithms in real world situations. Finally, apply your knowledge to practical real-world projects using ML models to get insightful solutions!By the end of this course, you'll build a strong foundation for entering the world of Machine Learning and data science with Python's own scikit-learn the help of this comprehensive guide!Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Machine Learning with scikit-learn, covers learning to implement and evaluate machine learning solutions with scikit-learn. 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.The second course, Fundamentals of Machine Learning with scikit-learn, covers building strong foundation for entering the world of Machine Learning and data science. In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.The third course, Hands-on scikit-learn for Machine Learning, covers Machine Learning projects with Python's own scikit-learn on real-world datasets. If you're an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on scikit-learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by scikit-learn. By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.The fourth course, Real-World Machine Learning Projects with scikit-learn, covers prediction of heart disease, customer-buying behaviors, and much more in this course filled with real-world projects. In this course you will build powerful projects using scikit-learn. Using algorithms, you will learn to read trends in the market to address market demand. You'll delve more deeply to decode buying behavior using Classification algorithms; cluster the population of a place to gain insights into using K-Means Clustering; and create a model using Support Vector Machine classifiers to predict heart disease. By the end of the course you will be adept at working on professional projects using scikit-learn and Machine Learning algorithms.By the end of this course, you'll build a strong foundation for entering the world of Machine Learning and data science with Python's own scikit-learn the help of this comprehensive guide!About the AuthorsGiuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learning solution design, management, and delivery. He got his MSc Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, and NLP.Farhan Nazar Zaidi has 25 years' experience in software architecture, big data engineering, and hands-on software development in a variety of languages and technologies. He is skilled in architecting and designing networked, distributed software systems and data analytics applications, and in designing enterprise-grade software systems. Farhan holds an MS in Computer Science from University of Southern California, Los Angeles, USA and a BS in Electrical Engineering from University of Engineering, Lahore, Pakistan. He has worked for several Silicon-Valley companies in the past in the US as a Senior Software Engineer, and also held key positions in the software industry in Pakistan. Farhan works as consultant, solutions developer, and in-person trainer on big data engineering, microservices, advanced analytics, and Machine Learning.Nikola Zivkovic is a software developer with over 7 years' experience in the industry. He earned his Master's degree in Computer Engineering from the University of Novi Sad in 2011, but by then he was already working for several companies. At the moment he works for Vega IT Sourcing from Novi Sad. During this period, he worked on large enterprise systems as well as on small web projects. Also, he frequently talks at meetups and conferences and he is a guest lecturer at the University of Novi Sad.
Overview
Section 1: Machine Learning with Scikit-learn
Lecture 1 The Course Overview
Lecture 2 Defining Machine Learning
Lecture 3 Training Data, Testing Data, and Validation Data
Lecture 4 Bias and Variance
Lecture 5 An Introduction to Scikit-learn
Lecture 6 Installing Pandas, Pillow, NLTK, and Matplotlib
Lecture 7 What Is Simple Linear Regression?
Lecture 8 Evaluating the Model
Lecture 9 KNN, Lazy Learning, and Non-Parametric Models
Lecture 10 Classification with KNN
Lecture 11 Regression with KNN
Lecture 12 Extracting Features from Categorical Variables
Lecture 13 Standardizing Features
Lecture 14 Extracting Features from Text
Lecture 15 Multiple Linear Regression
Lecture 16 Polynomial Regression
Lecture 17 Regularization
Lecture 18 Applying Linear Regression
Lecture 19 Gradient Descent
Lecture 20 Binary Classification with Logistic Regression
Lecture 21 Spam Filtering
Lecture 22 Tuning Models with Grid Search
Lecture 23 Multi-Class Classification
Lecture 24 Multi-Label Classification and Problem Transformation
Lecture 25 Bayes' Theorem
Lecture 26 Generative and Discriminative Models
Lecture 27 Naive Bayes with Scikit-learn
Lecture 28 Decision Trees
Lecture 29 Training Decision Trees
Lecture 30 Decision Trees with Scikit-learn
Lecture 31 Bagging
Lecture 32 Boosting
Lecture 33 Stacking
Lecture 34 The Perceptron-Basics
Lecture 35 Limitations of the Perceptron
Lecture 36 Kernels and the Kernel Trick
Lecture 37 Maximum Margin Classification and Support Vectors
Lecture 38 Classifying Characters in Scikit-learn
Lecture 39 Nonlinear Decision Boundaries
Lecture 40 Feed-Forward and Feedback ANNs
Lecture 41 Multi-Layer Perceptrons and Training Them
Lecture 42 Clustering
Lecture 43 K-means
Lecture 44 Evaluating Clusters
Lecture 45 Image Quantization
Lecture 46 Principal Component Analysis
Lecture 47 Visualizing High-Dimensional Data and Face Recognition with PCA
Section 2: Fundamentals of Machine Learning with scikit-learn
Lecture 48 The Course Overview
Lecture 49 Machine Types and Learning Methods
Lecture 50 Data Formats
Lecture 51 Learnability
Lecture 52 Statistical Learning Approaches
Lecture 53 Elements of Information Theory
Lecture 54 Splitting Datasets
Lecture 55 Managing Data
Lecture 56 Data Scaling and Normalization
Lecture 57 Principal Component Analysis
Lecture 58 Linear Models and Its Example
Lecture 59 Linear Regression with scikit-learn
Lecture 60 Ridge, Lasso, and ElasticNet
Lecture 61 Regression Types
Lecture 62 Logistic Regression
Lecture 63 Stochastic Gradient Descent Algorithms
Lecture 64 Finding the Optimal Hyperparameters
Lecture 65 Classification Metrics
Lecture 66 ROC Curve
Lecture 67 Bayes' Theorem
Lecture 68 Naive Bayes' in scikit-learn
Lecture 69 scikit-learn Implementation
Lecture 70 Controlled Support Vector Machines
Lecture 71 Binary Decision Trees
Lecture 72 Decision Tree Classification with scikit-learn
Lecture 73 Ensemble Learning
Lecture 74 Clustering Basics
Lecture 75 DBSCAN and Spectral Clustering
Lecture 76 Evaluation Methods Based on the Ground Truth
Lecture 77 Agglomerative Clustering
Lecture 78 Implementing Agglomerative Clustering
Lecture 79 Connectivity Constraints
Lecture 80 User-Based Systems
Lecture 81 Content-Based Systems
Section 3: Hands-on Scikit-learn for Machine Learning
Lecture 82 The Course Overview
Lecture 83 Course Objectives, Software Installation, and Setup
Lecture 84 Overview of Scikit-learn
Lecture 85 Scikit-learn Programming Workflow Example
Lecture 86 Applying a KNN Model on Cancer Dataset
Lecture 87 Improving the KNN Performance on Cancer Dataset
Lecture 88 Linear and Logistic Regression
Lecture 89 Evaluating Classification Models
Lecture 90 Logistic Regression and Evaluation with Scikit-learn
Lecture 91 Decision Trees
Lecture 92 Bagging, Boosting, and Random Forests
Lecture 93 Applying Ensemble Methods with Scikit-learn
Lecture 94 Support Vector Machines
Lecture 95 Applying Support Vector Machines Classifier with Scikit-learn
Lecture 96 Multi-class Classification Example with Scikit-learn
Lecture 97 Downloading and Inspecting the Dataset
Lecture 98 Handling Categorical Features and Missing Values
Lecture 99 Creating Train and Test Sets and Finding Correlation
Lecture 100 Feature Scaling, Evaluating Regression Models, and Applying Linear Regression
Lecture 101 Regularization Techniques for Regression Analysis
Lecture 102 Applying Random Forest for Regression Analysis
Lecture 103 Multi-Layer Perceptron, Neural Networks, and Applying MLP with Scikit-learn
Lecture 104 Principle Component Analysis
Lecture 105 Applying PCA with Scikit-learn for Feature Reduction
Lecture 106 Applying PCA for a Regression Problem on a Large Dataset
Lecture 107 Nonlinear Methods of Feature Extraction - t-SNE and Isomap
Lecture 108 Applying Dimensionality Reduction Techniques to Images
Lecture 109 Introduction to Clustering and k-means Clustering
Lecture 110 Applying k-means with Scikit-learn
Lecture 111 Agglomerative Clustering
Lecture 112 DBSCAN Clustering Algorithm
Lecture 113 Applying DBSCAN with Scikit-learn
Lecture 114 Handling Missing Values and Data Cleaning
Lecture 115 Handling Missing Values and Scaling Numerical Features
Lecture 116 Handling Outliers and Removing Distribution Skew
Lecture 117 Handling Outliers and Removing Distribution Skew (Continued)
Lecture 118 Deriving Additional Features
Lecture 119 Evaluating Different Models and Cross- Validation
Lecture 120 Model Selection Strategies
Lecture 121 Feature Engineering for Classification
Lecture 122 Model Selection Strategies for Credit Risk Assessment
Lecture 123 Creating Processing Pipelines with Scikit-learn
Lecture 124 Using Pipelines on Our Credit Risk Assessment Dataset
Lecture 125 Advanced Model Selection Techniques
Lecture 126 Practicing Pipelines with a Time-Series Dataset
Lecture 127 Bag-of-Words Model and Sentiment Analysis
Lecture 128 Using Stop-Words and TF-IDF for Sentiment Analysis
Lecture 129 Using N-Grams to Improve Model Performance for Sentiment Analysis
Lecture 130 Using Stemming and Lemmatization for Sentiment Analysis
Lecture 131 Topic Modeling with TruncatedSVD and Latent Dirichlet Allocation
Section 4: Real-World Machine Learning Projects with Scikit-Learn
Lecture 132 The Course Overview
Lecture 133 Exploring the Dataset and Identifying the Problem
Lecture 134 Multiple Linear Regression
Lecture 135 Implementing the Solution
Lecture 136 Evaluating and Improving the Model
Lecture 137 Analyzing the Results
Lecture 138 Exploring the Dataset and Identifying the Problem
Lecture 139 Decision Trees and Random Forest
Lecture 140 Feature Analysis and Engineering
Lecture 141 Implementing the Solution
Lecture 142 Analyze the Results
Lecture 143 Exploring the Dataset and Identifying the Problem
Lecture 144 Support Vector Machines
Lecture 145 Feature Analysis and Engineering
Lecture 146 Implementing the Solution
Lecture 147 Analyze the Results
Lecture 148 Exploring the Dataset and Identifying the Problem
Lecture 149 K-Means Clustering
Lecture 150 Feature Analysis and Engineering
Lecture 151 Implementing the Solution
Lecture 152 Analyze the Results
Lecture 153 Exploring the Dataset and Identifying the Problem
Lecture 154 Hierarchical Clustering
Lecture 155 Feature Analysis and Engineering
Lecture 156 Implementing the Solution
Lecture 157 Analyze the Results
IT professionals, software developer, machine learning engineer, or data analyst who want to enter the field of data science use scikit-learn for different Machine Learning and analytics tasks.

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