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Machine Learning

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About Course

Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.

What You Get:
Certificate:
  • Receive a signed certificate with the institution’s logo to verify your achievement and increase your job prospects.
  • Easily Shareable certificate
  • Add the certificate to your CV or resume, or post it directly on LinkedIn
Trainings:
  • Get access of 48 lessons training on Machine Learning
  • Assignments on Machine Learning

You need to complete all lessons to download certificate. If you already completed lesson then mark complete lesson from upper right corner.

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Course Content

Installation

  • How to install Python on Windows 7/8.1/10
    04:56
  • How to install Anaconda in Windows 7/8.1/10
    02:53
  • How to Launch Jupyter Notebook Using Anaconda
    01:34

Linear Regression

  • Topic Files
    00:08
  • How To Import Libraries and Dataset for Linear Regression
    12:59
  • Distribution Plot For Linear Regression
    08:46
  • How To Use Label Encoder and Heatmap
    08:30
  • How To Use Train Test and Split For Linear Regression
    08:06
  • Linear Regression Model Creation and Model Training
    06:23
  • Linear Regression Model Evaluation
    06:00

Logistic Regression

  • Topic Files
    00:08
  • How To Import libraries and dataset for Logistic Regression
    08:52
  • Imputing Null Values
    09:03
  • Plotting For Logistic Regression
    05:23
  • How To Do The Pre-Processing For Logistic Regression
    08:07
  • How To Use Train, Test and Split For Logistic Regression
    06:49
  • How To Create Logistic Model and Model Evaluation
    07:44

Decision Tree and Random Forest

  • Topic Files
    00:08
  • How To Import Libraries and Dataset
    07:19
  • EDA (Exploratory Data Analysis) and Count Plot
    10:10
  • How To Do The Pre-Processing
    06:28
  • How To Use Train, Test and Split
    05:43
  • How to Create Logistic Model and Logistic Model Evaluation
    07:38
  • Decision Tree Entropy Model
    06:35
  • Decision Tree Gini Model
    03:49
  • Random Forest Model
    09:15

Naive Bayes

  • Topic Files
    00:08
  • How To Import Libraries and Dataset
    07:04
  • Distribution Plot and EDA for Naive Bayes
    07:02
  • How To Use Train, Test, Split and Logistic Model
    07:34
  • Naive Bayes Model
    04:02

K-Means Cluster

  • Topic Files
    00:08
  • How To Import Libraries and Dataset
    05:17
  • Plotting and EDA For K-Means Cluster
    03:49
  • Finding K and Elbow method for K-means Cluster
    12:55
  • How Get Cluster Centers and Plotting
    07:13

K-Nearest Neighbor(KNN)

  • Topic Files
    00:08
  • How To Import Libraries and Dataset
    12:04
  • Distribution Plot and EDA For KNN
    06:23
  • How To Use Train, Test and Split
    04:18
  • How To Create KNN Model and Model Evaluation
    07:07
  • How To Find K Optimum for KNN
    11:55

Support Vector Machine (SVM)

  • Topic Files
    00:08
  • How To Import Libraries, Dataset and Pre-Processing
    07:08
  • Distribution Plot and EDA For SVM
    05:20
  • How To Use Train, Test and Split the dataset
    03:58
  • How to Create Logistic Model and Logistic Model Evaluation
    04:42
  • Creating a Support Vector Machine and Model Evaluation
    07:46

Assignments

  • Assignments

Quiz

  • Machine Learning Quiz
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  • Certificate of completion
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