Machine Learning with Python, scikit-learn and TensorFlow

Machine Learning with Python, scikit-learn and TensorFlow, available at $19.99, has an average rating of 2.95, with 111 lectures, based on 23 reviews, and has 187 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone interested in entering the data science stream with Machine Learning. or Software engineers who want to understand how common Machine Learning algorithms work. or Data scientists and researchers who want to learn about the scikit-learn API. It is particularly useful for Anyone interested in entering the data science stream with Machine Learning. or Software engineers who want to understand how common Machine Learning algorithms work. or Data scientists and researchers who want to learn about the scikit-learn API.

Enroll now: Machine Learning with Python, scikit-learn and TensorFlow

Summary

Title: Machine Learning with Python, scikit-learn and TensorFlow

Price: $19.99

Average Rating: 2.95

Number of Lectures: 111

Number of Published Lectures: 111

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 111

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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) Liuis 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 Azeemis 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.

Course Curriculum

Chapter 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 Na?ve Bayes

Lecture 14: The Mechanics of Na?ve Bayes

Lecture 15: The Na?ve 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

Chapter 2: 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

Chapter 3: Machine Learning with TensorFlow

Lecture 1: The Course Overview

Lecture 2: Introducing Deep Learning

Lecture 3: Installing TensorFlow on Mac OSX

Lecture 4: Installation on Windows – Pre-Reqeusite Virtual Machine Setup

Lecture 5: Installation on Windows/Linux

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 6 votes

4 stars: 4 votes

5 stars: 6 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!