Machine Learning With Python, Scikit-Learn And Tensorflow

What you'll learn
Solve interesting, real-world problems using machine learning with Python
Evaluate the performance of machine learning systems in common tasks
Create pipelines to deal with real-world input data
Traverse from concept to a production-ready machine learning setup/pipeline capable of real-world usage
Use Python to visualize data spread across multiple dimensions and extract useful features to implement machine learning classification and regression algorithms from scratch in Python
Predict the values of continuous variables using linear regression and K Nearest Neighbors to classify documents and images using logistic regression and support vector machines
Requirements
Familiarity with Machine Learning fundamentals will be useful.
A basic understanding Python programming is assumed.
Description
Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you'll tackle data-driven problems. The effective blend of Machine Learning with Python, scikit-learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model.
This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks
Contents and Overview
This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.
This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, scikit-learn and TensorFlow.
The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you'll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You'll build your own models from scratch.
The second course, Machine Learning with Scikit-learn, covers effective learning algorithms to real-world problems using scikit-learn. You'll 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.
The third course, Machine Learning with TensorFlow, covers hands-on examples with machine learning using Python. You'll cover the unique features of the library such as data flow Graphs, training, and visualization of performance with TensorBoard—all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section.
By the end of this training program you'll be able to tackle data-driven problems and implement your solutions as well as build efficient models with the powerful yet simple features of Python, scikit-learn and TensorFlow.
About the Authors
Yuxi (Hayden) Liu is currently an applied research scientist focused on developing machine learning models and systems for given learning tasks. He has worked for a few years as a data scientist, and applied his machine learning expertise in computational advertising. He earned his degree from the University of Toronto, and published five first-authored IEEE transaction and conference papers during his research. His first book, titled Python Machine Learning By Example, was ranked the #1 bestseller in Amazon India in 2017. He is also a machine learning education enthusiast.
Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad, Pakistan. He has a great interest in the computer science field, and he started his journey with Android development. Now, he's pursuing his career in Machine Learning, particularly in deep learning, by doing medical-related freelancing projects with different companies. He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE International Conference, ROBIO as a co-author of Designing of motions for humanoid goalkeeper robots.
Overview
Section 1: Step-by-Step Machine Learning with Python
Lecture 1 The Course Overview
Lecture 2 Introduction to Machine Learning
Lecture 3 Installing Software and Setting Up
Lecture 4 Understanding NLP
Lecture 5 Touring Powerful NLP Libraries in Python
Lecture 6 Getting the Newsgroups Data
Lecture 7 Thinking about Features
Lecture 8 Visualization
Lecture 9 Data Preprocessing
Lecture 10 Clustering
Lecture 11 Topic Modeling
Lecture 12 Getting Started with Classification
Lecture 13 Exploring Naïve Bayes
Lecture 14 The Mechanics of Naïve Bayes
Lecture 15 The Naïve Bayes Implementation
Lecture 16 Classifier Performance Evaluation
Lecture 17 Model Tuning and cross-validation
Lecture 18 Recap and Inverse Document Frequency
Lecture 19 The Mechanics of SVM
Lecture 20 The Implementations of SVM
Lecture 21 The Kernels of SVM
Lecture 22 Choosing Between the Linear and the RBF Kernel
Lecture 23 News topic Classification with Support Vector Machine
Lecture 24 Fetal State Classification with SVM
Lecture 25 Brief Overview of Advertising Click-Through Prediction
Lecture 26 Decision Tree Classifier
Lecture 27 The Implementations of Decision Tree
Lecture 28 Click-Through Prediction with Decision Tree
Lecture 29 Random Forest - Feature Bagging of Decision Tree
Lecture 30 One-Hot Encoding - Converting Categorical Features to Numerical
Lecture 31 Logistic Regression Classifier
Lecture 32 Click-Through Prediction with Logistic Regression by Gradient Descent
Lecture 33 Feature Selection via Random Forest
Lecture 34 Brief Overview of the Stock Market And Stock Price
Lecture 35 Predicting Stock Price with Regression Algorithms
Lecture 36 Data Acquisition and Feature Generation
Lecture 37 Linear Regression
Lecture 38 Decision Tree Regression
Lecture 39 Support Vector Regression
Lecture 40 Regression Performance Evaluation
Lecture 41 Stock Price Prediction with Regression Algorithms
Lecture 42 Best Practices in Data Preparation Stage
Lecture 43 Best Practices in the Training Sets Generation Stage
Lecture 44 Best Practices in the Model Training, Evaluation, and Selection Stage
Lecture 45 Best Practices in the Deployment and Monitoring Stage
Section 2: Machine Learning with Scikit-learn
Lecture 46 The Course Overview
Lecture 47 Defining Machine Learning
Lecture 48 Training Data, Testing Data, and Validation Data
Lecture 49 Bias and Variance
Lecture 50 An Introduction to Scikit-learn
Lecture 51 Installing Pandas, Pillow, NLTK, and Matplotlib
Lecture 52 What Is Simple Linear Regression?
Lecture 53 Evaluating the Model
Lecture 54 KNN, Lazy Learning, and Non-Parametric Models
Lecture 55 Classification with KNN
Lecture 56 Regression with KNN
Lecture 57 Extracting Features from Categorical Variables
Lecture 58 Standardizing Features
Lecture 59 Extracting Features from Text
Lecture 60 Multiple Linear Regression
Lecture 61 Polynomial Regression
Lecture 62 Regularization
Lecture 63 Applying Linear Regression
Lecture 64 Gradient Descent
Lecture 65 Binary Classification with Logistic Regression
Lecture 66 Spam Filtering
Lecture 67 Tuning Models with Grid Search
Lecture 68 Multi-Class Classification
Lecture 69 Multi-Label Classification and Problem Transformation
Lecture 70 Bayes' Theorem
Lecture 71 Generative and Discriminative Models
Lecture 72 Naive Bayes with Scikit-learn
Lecture 73 Decision Trees
Lecture 74 Training Decision Trees
Lecture 75 Decision Trees with Scikit-learn
Lecture 76 Bagging
Lecture 77 Boosting
Lecture 78 Stacking
Lecture 79 The Perceptron–Basics
Lecture 80 Limitations of the Perceptron
Lecture 81 Kernels and the Kernel Trick
Lecture 82 Maximum Margin Classification and Support Vectors
Lecture 83 Classifying Characters in Scikit-learn
Lecture 84 Nonlinear Decision Boundaries
Lecture 85 Feed-Forward and Feedback ANNs
Lecture 86 Multi-Layer Perceptrons and Training Them
Lecture 87 Clustering
Lecture 88 K-means
Lecture 89 Evaluating Clusters
Lecture 90 Image Quantization
Lecture 91 Principal Component Analysis
Lecture 92 Visualizing High-Dimensional Data and Face Recognition with PCA
Section 3: Machine Learning with TensorFlow
Lecture 93 The Course Overview
Lecture 94 Introducing Deep Learning
Lecture 95 Installing TensorFlow on Mac OSX
Lecture 96 Installation on Windows – Pre-Reqeusite Virtual Machine Setup
Lecture 97 Installation on Windows/Linux
Lecture 98 The Hand-Written Letters Dataset
Lecture 99 Automating Data Preparation
Lecture 100 Understanding Matrix Conversions
Lecture 101 The Machine Learning Life Cycle
Lecture 102 Reviewing Outputs and Results
Lecture 103 Getting Started with TensorBoard
Lecture 104 TensorBoard Events and Histograms
Lecture 105 The Graph Explorer
Lecture 106 Our Previous Project on TensorBoard
Lecture 107 Fully Connected Neural Networks
Lecture 108 Convolutional Neural Networks
Lecture 109 Programming a CNN
Lecture 110 Using TensorBoard on Our CNN
Lecture 111 CNN Versus Fully Connected Network Performance
Anyone interested in entering the data science stream with Machine Learning.,Software engineers who want to understand how common Machine Learning algorithms work.,Data scientists and researchers who want to learn about the scikit-learn API.


Homepage

https://www.udemy.com/course/machine-learning-with-python-scikit-learn-tensorflow/

Machine Learning With Python, Scikit-Learn And Tensorflow Fast Download
Machine Learning With Python, Scikit-Learn And Tensorflow Full Download