Python Machine Learning Solutions

Python Machine Learning Solutions

4.5 Hours
Deal Price$10.00
Suggested Price
$99.99
You save 89%
Python Machine Learning Solutions
$10.00$99.9989% OFF
Python Machine Learning Solutions

97 Lessons (4.5h)

  • The Realm of Supervised Learning
    The Course Overview4:09
    Preprocessing Data Using Different Techniques6:04
    Label Encoding2:25
    Building a Linear Regressor4:25
    Regression Accuracy and Model Persistence3:41
    Building a Ridge Regressor2:41
    Building a Polynomial Regressor2:33
    Estimating housing prices3:45
    Computing relative importance of features1:54
    Estimating bicycle demand distribution4:35
  • Constructing a Classifier
    Building a Simple Classifier3:40
    Building a Logistic Regression Classifie4:50
    Building a Naive Bayes’ Classifier2:11
    Splitting the Dataset for Training and Testing1:23
    Evaluating the Accuracy Using Cross-Validation4:06
    Visualizing the Confusion Matrix and Extracting the Performance Report4:14
    Evaluating Cars based on Their Characteristics5:12
    Extracting Validation Curves2:49
    Extracting Learning Curves1:37
    Extracting the Income Bracket3:36
  • Predictive Modeling
    Building a Linear Classifier Using Support Vector Machine4:23
    Building Nonlinear Classifier Using SVMs1:47
    Tackling Class Imbalance2:53
    Extracting Confidence Measurements2:36
    Finding Optimal Hyper-Parameters2:16
    Building an Event Predictor3:45
    Estimating Traffic2:39
  • Clustering with Unsupervised Learning
    Clustering Data Using the k-means Algorithm3:07
    Compressing an Image Using Vector Quantization3:37
    Building a Mean Shift Clustering2:35
    Grouping Data Using Agglomerative Clustering3:04
    Evaluating the Performance of Clustering Algorithms2:55
    Automatically Estimating the Number of Clusters Using DBSCAN3:34
    Finding Patterns in Stock Market Data2:34
    Building a Customer Segmentation Model2:21
  • Building Recommendation Engines
    Building Function Composition for Data Processing3:25
    Building Machine Learning Pipelines3:54
    Finding the Nearest Neighbors1:56
    Constructing a k-nearest Neighbors Classifier4:18
    Constructing a k-nearest Neighbors Regressor2:43
    Computing the Euclidean Distance Score2:08
    Computing the Pearson Correlation Score1:55
    Finding Similar Users in a Dataset1:35
    Generating Movie Recommendations2:34
  • Analyzing Text Data
    Preprocessing Data Using Tokenization3:00
    Stemming Text Data2:22
    Converting Text to Its Base Form Using Lemmatization2:11
    Dividing Text Using Chunking2:03
    Building a Bag-of-Words Model2:58
    Building a Text Classifier4:43
    Identifying the Gender2:17
    Analyzing the Sentiment of a Sentence3:09
    Identifying Patterns in Text Using Topic Modelling4:52
  • Speech Recognition
    Reading and Plotting Audio Data2:34
    Transforming Audio Signals into the Frequency Domain2:09
    Generating Audio Signals with Custom Parameters1:45
    Synthesizing Music2:10
    Extracting Frequency Domain Features2:06
    Building Hidden Markov Models2:19
    Building a Speech Recognizer3:12
  • Dissecting Time Series and Sequential Data
    Transforming Data into the Time Series Format3:07
    Slicing Time Series Data1:31
    Operating on Time Series Data1:42
    Extracting Statistics from Time Series2:29
    Building Hidden Markov Models for Sequential Data4:15
    Building Conditional Random Fields for Sequential Text Data4:27
    Analyzing Stock Market Data with Hidden Markov Models2:25
  • Image Content Analysis
    Operating on Images Using OpenCV-Python3:07
    Detecting Edges2:47
    Histogram Equalization2:30
    Detecting Corners and SIFT Feature Points3:46
    Building a Star Feature Detector1:34
    Creating Features Using Visual Codebook and Vector Quantization4:10
    Training an Image Classifier Using Extremely Random Forests2:30
    Building an object recognizer1:53
  • Biometric Face Recognition
    Capturing and Processing Video from a Webcam1:58
    Building a Face Detector using Haar Cascades2:40
    Building Eye and Nose Detectors1:54
    Performing Principal Component Analysis2:17
    Performing Kernel Principal Component Analysis2:02
    Performing Blind Source Separation2:16
    Building a Face Recognizer Using a Local Binary Patterns Histogram4:14
  • Deep Neural Networks
    Building a Perceptron2:40
    Building a Single-Layer Neural Network1:37
    Building a deep neural network2:19
    Creating a Vector Quantizer1:40
    Building a Recurrent Neural Network for Sequential Data Analysis2:23
    Visualizing the Characters in an Optical Character Recognition Database1:48
    Building an Optical Character Recognizer Using Neural Networks2:28
  • Visualizing Data
    Plotting 3D Scatter plots2:42
    Plotting Bubble Plots1:16
    Animating Bubble Plots1:56
    Drawing Pie Charts1:33
    Plotting Date-Formatted Time Series Data1:33
    Plotting Histograms1:05
    Visualizing Heat Maps1:15
    Animating Dynamic Signals2:06
Python Machine Learning Solutions
$10.00$99.9989% OFF
View similar items
DescriptionInstructorImportant DetailsRelated Products

Learn How to Perform Various Machine Learning Tasks in the Real World

PP
Packt PublishingPrateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines.

His tech blog (www.prateekjoshi.com) has received more than 1.2 million page views from 200 over countries and has over 6,600+ followers. He frequently writes on topics such as artificial intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from University of Southern California with a master’s degree specializing in artificial intelligence. He has worked at companies such as Nvidia and Microsoft Research. You can learn more about him on his personal website at www.prateekj.com.

Description

Machine learning is pervasive in the modern, data-driven world. It's used in search engines, robotic, self-driving cars, and many more instances. In this course, you'll learn how to perform various machine learning tasks in many different environments. Focusing on real-life scenarios, you'll learn how to solve real problems and use Python to implement algorithms.

  • Access 97 lectures & 4.5 hours of content 24/7
  • Deal w/ various types of data & explore the differences between machine learning paradigms
  • Cover a range of regression techniques, classification algorithms, predictive modeling, & more
  • Use real-world examples to solve real-life problems

Specs

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
Your Cart
Your cart is empty. Continue Shopping!
Processing order...