Python Machine Learning: Projects, Tips And Troubleshooting

Python Machine Learning: Projects, Tips And Troubleshooting
Last updated 4/2019
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Practical solutions to Machine Learning problems, avoiding roadblocks while working with Python data science ecosystem.

What you’ll learn

Use pre-written libraries in python to work with powerful algorithms.

Tips and tricks to speed up your modeling process and obtain better results.

Make predictions using advanced regression analysis with Python.

Modern techniques for solving supervised learning problems.

Build your own recommendation engine and perform collaborative filtering.

Eliminate common data wrangling problems in Pandas and scikit-learn.

Troubleshoot advanced models such as Random Forests and SVMs.

Wrangling with unsupervised learning and the curse of dimensionality.

Solving prediction visualization issues with Matplotlib.

Perform common natural language processing featuring engineering tasks.

Requirements

Prior Python programming experience is a requirement, whereas experience with Machine Learning concepts will be helpful.

Description

Overview

Section 1: Python Machine Learning in 7 Days

Lecture 1 The Course Overview

Lecture 2 Setting Up Your Machine Learning Environment

Lecture 3 Exploring Types of Machine Learning

Lecture 4 Using Scikit-learn for Machine Learning

Lecture 5 Assignment – Train Your First Pre-built Machine Learning Model

Lecture 6 Supervised Learning Algorithm

Lecture 7 Architecture of a Machine Learning System

Lecture 8 Machine Learning Model and Its Components

Lecture 9 Linear Regression

Lecture 10 Predicting Weight Using Linear Regression

Lecture 11 Assignment – Predicting Energy Output of a Power Plant

Lecture 12 Review of Predicting Energy Output of a Power Plant

Lecture 13 Logistic Regression

Lecture 14 Classifying Images Using Logistic Regression

Lecture 15 Support Vector Machines

Lecture 16 Kernels in a SVM

Lecture 17 Classifying Images Using Support Vector Machines

Lecture 19 Review of Classifying Images Using Support Vector Machines

Lecture 20 Model Evaluation

Lecture 21 Better Measures than Accuracy

Lecture 22 Understanding the Results

Lecture 23 Improving the Models

Lecture 24 Assignment – Getting Better Test Sample Results by Measuring Model Performance

Lecture 25 Review of Getting Better Test Sample Results by Measuring Model Performance

Lecture 26 Unsupervised Learning

Lecture 27 Clustering

Lecture 28 K-means Clustering

Lecture 29 Determining the Number of Clusters

Lecture 30 Assignment – Write Your Own Clustering Implementation for Customer Segmentation

Lecture 31 Review of Clustering Customers Together

Lecture 32 Why Neural Network

Lecture 34 Working of a Neural Network

Lecture 35 Improving the Network

Lecture 36 Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN

Lecture 37 Review of Building a Sentiment Analyser ANN

Lecture 38 Decision Trees

Lecture 39 Working of a Decision Tree

Lecture 40 Techniques to Further Improve a Model

Lecture 41 Random Forest as an Improved Machine Learning Approach

Lecture 42 Weekend Task – Solving Titanic Problem Using Random Forest

Section 2: Python Machine Learning Projects

Lecture 43 The Course Overview

Lecture 44 Sourcing Airfare Pricing Data

Lecture 45 Retrieving the Fare Data with Advanced Web Scraping Techniques

Lecture 46 Parsing the DOM to Extract Pricing Data

Lecture 47 Sending Real-Time Alerts Using IFTTT

Lecture 48 Putting It All Together

Lecture 49 The IPO Market

Lecture 50 Feature Engineering

Lecture 51 Binary Classification

Lecture 52 Feature Importance

Lecture 53 Creating a Supervised Training Set with the Pocket App

Lecture 54 Using the embed.ly API to Download Story Bodies

Lecture 55 Natural Language Processing Basics

Lecture 56 Support Vector Machines

Lecture 57 IFTTT Integration with Feeds, Google Sheets, and E-mail

Lecture 58 Setting Up Your Daily Personal Newsletter

Lecture 59 What Does Research Tell Us about the Stock Market?

Lecture 60 Developing a Trading Strategy
Lecture 61 Building a Model and Evaluating Its Performance

Lecture 62 Modeling with Dynamic Time Warping

Lecture 63 Machine Learning on Images

Lecture 64 Working with Images

Lecture 65 Finding Similar Images

Lecture 66 Building an Image Similarity Engine

Lecture 67 The Design of Chatbots

Lecture 68 Building a Chatbot

Section 3: Python Machine Learning Tips, Tricks, and Techniques

Lecture 69 The Course Overview

Lecture 70 Using Feature Scaling to Standardize Data

Lecture 71 Implementing Feature Engineering with Logistic Regression

Lecture 72 Extracting Data with Feature Selection and Interaction
Lecture 73 Combining All Together

Lecture 74 Build Model Based on Real-World Problems

Lecture 75 Support Vector Machines

Lecture 76 Implementing kNN on the Data Set

Lecture 77 Decision Tree as Predictive Model

Lecture 78 Tricks with Dimensionality Reduction

Lecture 79 Combining All Together

Lecture 80 Random Forest for Classification

Lecture 81 Gradient Boosting Trees and Bayes Optimization

Lecture 82 CatBoost to Handle Categorical Data

Lecture 83 Implement Blending

Lecture 84 Implement Stacking

Lecture 85 Memory-Based Collaborative Filtering

Lecture 86 Item-to-Item Recommendation with kNN

Lecture 87 Applying Matrix Factorization on Datasets

Lecture 88 Wordbatch for Real-World Problem

Lecture 89 Validation Dataset Tuning

Lecture 90 Regularizing Model to Avoid Overfitting

Lecture 91 Adversarial Validation

Lecture 92 Perform Metric Selection on Real Data

Section 4: Troubleshooting Python Machine Learning

Lecture 93 The Course Overview

Lecture 94 Splitting Your Datasets for Train, Test, and Validate

Lecture 95 Persist Your Hard Earned Models by Saving Them to Disk

Lecture 96 Calculate Word Frequencies Efficiently in Good ol’ Python

Lecture 98 Finding the Most Important Features in Your Classifier

Lecture 99 Predicting Multiple Targets with the Same Dataset

Lecture 100 Retrieving the Best Estimators after Grid Search

Lecture 101 Regress on Your Pandas Data Frame with Simple Statsmodels OLS

Lecture 102 Extracting Decision Tree Rules from scikit-learn

Lecture 103 Finding Out Which Features Are Important in a Random Forest Model

Lecture 104 Classifying with SVMs When Your Data Has Unbalanced Classes

Lecture 105 Computing True/False Positives/Negatives after in scikit-learn

Lecture 106 Labelling Dimensions with Original Feature Names after PCA

Lecture 107 Clustering Text Documents with scikit-learn K-means

Lecture 108 Listing Word Frequency in a Corpus Using Only scikit-learn

Lecture 109 Polynomial Kernel Regression Using Pipelines

Lecture 110 Visualize Outputs Over Two-Dimensions Using NumPy’s Meshgrid

Lecture 111 Drawing Out a Decision Tree Trained in scikit-learn

Lecture 112 Clarify Your Histogram by Labelling Each Bin

Lecture 113 Centralizing Your Color Legend When You Have Multiple Subplots

This course is perfect for:,Data Scientists, Developers who are familiar with basic Python programming and want to build efficient, faster, and progressive Machine Learning models to tackle real data.