| 1. Data Visualization - Starting With Matplotlib.mp4 | 11.5 MB | ||
| 1. Data Visualization - Starting With Matplotlib.srt | 2.6 KB | ||
| 1. Defining Numbers In Python.mp4 | 10.9 MB | ||
| 1. Defining Numbers In Python.srt | 6.4 KB | ||
| 1. Discriminant Analysis - Lecture 1.mp4 | 101.8 MB | ||
| 1. Discriminant Analysis - Lecture 1.srt | 12.5 KB | ||
| 1. Ensemble Classifiers- Random Forest - Lecture 1.mp4 | 46.9 MB | ||
| 1. Ensemble Classifiers- Random Forest - Lecture 1.srt | 12.8 KB | ||
| 1. File Handling In Python - Lecture 1.mp4 | 17 MB | ||
| 1. File Handling In Python - Lecture 1.srt | 3.2 KB | ||
| 1. Fundamentals Of Decision Tree - Lecture 1.mp4 | 46.6 MB | ||
| 1. Fundamentals Of Decision Tree - Lecture 1.srt | 14.2 KB | ||
| 1. Installing Python.mp4 | 32.6 MB | ||
| 1. Installing Python.srt | 2 KB | ||
| 1. Introduction To Deep Learning.mp4 | 62.1 MB | ||
| 1. Introduction To Deep Learning.srt | 6.9 KB | ||
| 1. Introduction.mp4 | 23.2 MB | ||
| 1. Introduction.srt | 1.8 KB | ||
| 1. Regression Tree.mp4 | 11.5 MB | ||
| 1. Regression Tree.srt | 9.3 KB | ||
| 1. Ridge Regression - Lecture 1.mp4 | 39.4 MB | ||
| 1. Ridge Regression - Lecture 1.srt | 7.1 KB | ||
| 1. Starting With NumPy Library.mp4 | 29.4 MB | ||
| 1. Starting With NumPy Library.srt | 2.9 KB | ||
| 1. Starting With Pandas.mp4 | 36.6 MB | ||
| 1. Starting With Pandas.srt | 5.1 KB | ||
| 1. Support Vector Machines - Lecture 1.mp4 | 42.9 MB | ||
| 1. Support Vector Machines - Lecture 1.srt | 7.5 KB | ||
| 1. Understanding Linear Regression.mp4 | 45.4 MB | ||
| 1. Understanding Linear Regression.srt | 6.4 KB | ||
| 1. Understanding Logistic Regression Problems.mp4 | 10.2 MB | ||
| 1. Understanding Logistic Regression Problems.srt | 3 KB | ||
| 1. Understanding Naïve Bayes Classifier.mp4 | 62.4 MB | ||
| 1. Understanding Naïve Bayes Classifier.srt | 7.7 KB | ||
| 10. Decision Tree Hands-on With Python - Lecture 5.mp4 | 38.3 MB | ||
| 10. Decision Tree Hands-on With Python - Lecture 5.srt | 4.2 KB | ||
| 2. Basic Operations On Numbers.mp4 | 16.3 MB | ||
| 2. Basic Operations On Numbers.srt | 2.9 KB | ||
| 2. Data Visualization - The Dataset.mp4 | 11.8 MB | ||
| 2. Data Visualization - The Dataset.srt | 1.6 KB | ||
| 2. Discriminant Analysis - Lecture 2.mp4 | 57.2 MB | ||
| 2. Discriminant Analysis - Lecture 2.srt | 6.9 KB | ||
| 2. Ensemble Classifiers- Random Forest - Lecture 2.mp4 | 37.3 MB | ||
| 2. Ensemble Classifiers- Random Forest - Lecture 2.srt | 12.1 KB | ||
| 2. Estimating Coefficients Of Linear Regression.mp4 | 30.4 MB | ||
| 2. Estimating Coefficients Of Linear Regression.srt | 5.8 KB | ||
| 2. File Handling In Python - Lecture 2.mp4 | 38.2 MB | ||
| 2. File Handling In Python - Lecture 2.srt | 4.1 KB | ||
| 2. Fundamentals Of Decision Tree - Lecture 2.mp4 | 37.3 MB | ||
| 2. Fundamentals Of Decision Tree - Lecture 2.srt | 13.4 KB | ||
| 2. Installing Jupyter Notebook.mp4 | 13.6 MB | ||
| 2. Installing Jupyter Notebook.srt | 2.2 KB | ||
| 2. Introduction To Artificial Neural Networks (ANNs).mp4 | 71.4 MB | ||
| 2. Introduction To Artificial Neural Networks (ANNs).srt | 7.4 KB | ||
| 2. Regression Tree With Python.mp4 | 41.8 MB | ||
| 2. Regression Tree With Python.srt | 4.7 KB | ||
| 2. Ridge Regression - Lecture 2.mp4 | 27 MB | ||
| 2. Ridge Regression - Lecture 2.srt | 4.6 KB | ||
| 2. Slicing Of A Sequence.mp4 | 21.3 MB | ||
| 2. Slicing Of A Sequence.srt | 3.2 KB | ||
| 2. Supervised Machine Learning.mp4 | 36.8 MB | ||
| 2. Supervised Machine Learning.srt | 5.1 KB | ||
| 2. Support Vector Machines - Lecture 2.mp4 | 70.7 MB | ||
| 2. Support Vector Machines - Lecture 2.srt | 10 KB | ||
| 2. Understanding Data For Logistic Regression.mp4 | 10.5 MB | ||
| 2. Understanding Data For Logistic Regression.srt | 3 KB | ||
| 2. Understanding KNN Algorithm.mp4 | 23.4 MB | ||
| 2. Understanding KNN Algorithm.srt | 5 KB | ||
| 2. Working With Arrays Using NumPy Library.mp4 | 66.7 MB | ||
| 2. Working With Arrays Using NumPy Library.srt | 6.3 KB | ||
| 3. Advantages And Disadvantages Of KNN.mp4 | 6.6 MB | ||
| 3. Advantages And Disadvantages Of KNN.srt | 2.2 KB | ||
| 3. Architecture And Training Of ANN.mp4 | 61.7 MB | ||
| 3. Architecture And Training Of ANN.srt | 12.9 KB | ||
| 3. Data Analysis And Visualization.mp4 | 49.9 MB | ||
| 3. Data Analysis And Visualization.srt | 5.5 KB | ||
| 3. DataFrame In Pandas.mp4 | 13.7 MB | ||
| 3. DataFrame In Pandas.srt | 4.1 KB | ||
| 3. Decision Tree, Impurity Gain Ratio.mp4 | 21.5 MB | ||
| 3. Decision Tree, Impurity Gain Ratio.srt | 8.6 KB | ||
| 3. Explaining A Logistic Regression Model.mp4 | 10.1 MB | ||
| 3. Explaining A Logistic Regression Model.srt | 2.5 KB | ||
| 3. File Handling In Python - Lecture 3.mp4 | 27.6 MB | ||
| 3. File Handling In Python - Lecture 3.srt | 3.7 KB | ||
| 3. Installing PyCharm.mp4 | 27.8 MB | ||
| 3. Installing PyCharm.srt | 2.8 KB | ||
| 3. Random Forest Classifier In Python - Lecture 1.mp4 | 46.4 MB | ||
| 3. Random Forest Classifier In Python - Lecture 1.srt | 5.5 KB | ||
| 3. Support Vector Machines - Lecture 3.mp4 | 48.6 MB | ||
| 3. Support Vector Machines - Lecture 3.srt | 7.4 KB | ||
| 3. The Built-in Python Functions.mp4 | 28.8 MB | ||
| 3. The Built-in Python Functions.srt | 5.2 KB | ||
| 3. Understanding t-value and p-value.mp4 | 5.3 MB | ||
| 3. Understanding t-value and p-value.srt | 3.4 KB | ||
| 3. Unsupervised Machine Learning.mp4 | 43.5 MB | ||
| 3. Unsupervised Machine Learning.srt | 5.4 KB | ||
| 4. Data Slicing And Grouping.mp4 | 30.8 MB | ||
| 4. Data Slicing And Grouping.srt | 3.3 KB | ||
| 4. Decision Tree, Numerical Attributes - Lecture 1.mp4 | 42 MB | ||
| 4. Decision Tree, Numerical Attributes - Lecture 1.srt | 7.5 KB | ||
| 4. Difference Between Supervised And Unsupervised Machine Learning.mp4 | 12.6 MB | ||
| 4. Difference Between Supervised And Unsupervised Machine Learning.srt | 2.7 KB | ||
| 4. Fitting A Logistic Regression Model.mp4 | 33.8 MB | ||
| 4. Fitting A Logistic Regression Model.srt | 6.6 KB | ||
| 4. Installing Python Libraries.mp4 | 25.8 MB | ||
| 4. Installing Python Libraries.srt | 6.1 KB | ||
| 4. Multiple R-square And Residual Standard Error.mp4 | 21.3 MB | ||
| 4. Multiple R-square And Residual Standard Error.srt | 4.3 KB | ||
| 4. Perceptron With Python.mp4 | 62.4 MB | ||
| 4. Perceptron With Python.srt | 6 KB | ||
| 4. Random Forest Classifier In Python - Lecture 2.mp4 | 44.4 MB | ||
| 4. Random Forest Classifier In Python - Lecture 2.srt | 5.3 KB | ||
| 4. SVM With Python.mp4 | 60.2 MB | ||
| 4. SVM With Python.srt | 6.7 KB | ||
| 5. ANN With Back Propagation.mp4 | 65.2 MB | ||
| 5. ANN With Back Propagation.srt | 9.2 KB | ||
| 5. Decision Tree, Numerical Attributes - Lecture 2.mp4 | 34.5 MB | ||
| 5. Decision Tree, Numerical Attributes - Lecture 2.srt | 9.5 KB | ||
| 5. Filtering And Sorting Data.mp4 | 35.4 MB | ||
| 5. Filtering And Sorting Data.srt | 3.8 KB | ||
| 5. Linear Regression With Python.mp4 | 142.7 MB | ||
| 5. Linear Regression With Python.srt | 12.5 KB | ||
| 5. Logistic Regression With Python.mp4 | 105 MB | ||
| 5. Logistic Regression With Python.srt | 12.4 KB | ||
| 5. Python Programs In Many Ways.mp4 | 42.5 MB | ||
| 5. Python Programs In Many Ways.srt | 4.9 KB | ||
| 6. Back Propagation With Python.mp4 | 61.7 MB | ||
| 6. Back Propagation With Python.srt | 5.9 KB | ||
| 6. Decision Tree Hands-on With Python - Lecture 1.mp4 | 30.6 MB | ||
| 6. Decision Tree Hands-on With Python - Lecture 1.srt | 3.4 KB | ||
| 7. Decision Tree Hands-on With Python - Lecture 2.mp4 | 48.9 MB | ||
| 7. Decision Tree Hands-on With Python - Lecture 2.srt | 5.2 KB | ||
| 8. Decision Tree Hands-on With Python - Lecture 3.mp4 | 30.6 MB | ||
| 8. Decision Tree Hands-on With Python - Lecture 3.srt | 3.4 KB | ||
| 9. Decision Tree Hands-on With Python - Lecture 4.mp4 | 36.4 MB | ||
| 9. Decision Tree Hands-on With Python - Lecture 4.srt | 3.8 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 138 total files | |||
Complete Supervised Machine Learning With Python
https://CourseBoat.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 68 lectures (6h 58m) | Size: 2.3 GB
This course by industry and academic leaders is for people who want to build rewarding careers in data science
What you'll learn:
The principle of supervised and unsupervised learning and their difference.
Linear and Logistic Regression, Decision Tree, Regression Tree, Random Forest, Discriminant Analysis, Support Vector Machines, Naïve Bayes Classifier, KNN
How to choose the right set of algorithms and applying them in real-life projects in Python.
Lots of real life problem solving using Python programming language.
Requirements
You will need to have a computer or a mobile handset with an internet connection.
Basic knowledge of Python will be a plus.
Basic understanding of Statistics will be a plus.
Description
Data science, machine learning and Python have become key industry drivers in the global job and opportunity market. This course, designed and delivered by the industry experts and Ivy League academic leaders, will help you learn supervised machine learning from scratch. You will learn the subject with lots of applications and coding using Python programming language in real life business scenarios.
In this course you will learn:
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