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Complete Machine Learning course
Rating: 4.5 out of 5(74 ratings)
171 students

Complete Machine Learning course

Basics of machine learning,Linear Regression,Logistic Regression, Naïve Bayes ,KNN alogrthim , K-means, PCA, Custering,
Created bySatyendra Singh (NCFM and NSIM certified ) Technical analyst, portfolio manager,ML, blockchain expert
Last updated 10/2024
English

What you'll learn

  • Basics of machine learning
  • Linear Regression
  • Logistic Regression
  • KNN alogrithm
  • Clustering
  • K-Means Clustering
  • Principal component analysis
  • Data preprocsseing
  • EDA
  • The Machine Learning Process
  • Naive Bayes Classifier
  • Supervised learning and unsupervised learning
  • Confusion Matrix
  • The Elbow Method
  • Feature Scaling
  • Feature Scaling
  • Make Predictions
  • Splitting your data into a Training set and a Test set
  • Classification
  • Machine Learning preparation
  • Ordinary Least Squares
  • Accuracy
  • Decision Tree algorithm
  • Random forest algorithm
  • Quiz (MCQ on machine learning course)

Course content

10 sections • 20 lectures • 6h 22m total length

  • Introduction to Machine learning course6:13

    This video tells about topics to covered for compete machine learning course.

  • Basics of machine learning, data in machine learning13:57

    This video covers Basics of machine learning, data in machine learning.


  • Supervised learning, Unsupervised learning , advantages and disadvantages of ML16:01

    This video will cover Supervised learning, Unsupervised learning , advantages and disadvantages of ML.

  • ML life cycle, Exploratory data analysis , ML Challenges and libraries36:23

    This video will cover ML life cycle,  Exploratory data analysis , ML Challenges and libraries.

Requirements

  • Learner should be aware of basic python

Description

This course will cover following topics

1. Basics of machine learning

2. Supervised and unsupervised learning

3. Linear regression

4. Logistic regression

5. KNN Algorithm

6. Naive Bayes Classifier

7.  Random forest  Algorithm

8. Decision Tree Algorithm

7. Principal component analysis

8. K means clustering

9. Agglomerative clustering

10. There will practical exercise based on Linear regression, Logistic regression ,Naive Bayes, KNN algorithm, Random forest, Decision tree, K Means, PCA .

11.  Quiz (MCQ on machine learning course)


We will look first in to linear  Regression, where we will learn to predict continuous variables and this will details of  Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R Squared and Adjusted R Squared.

We will get  full details of  Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios and you will build your very first Logistic Regression

We will look in to Naive bias classifier which will give full details of Bayes Theorem, implementation of Naive bias in machine learning. This can be used in Spam Filtering, Text analysis, Recommendation Systems.


Random forest algorithm can be used in regression and classification problems. This gives good accuracy even if

data is incomplete.


Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems.


We will look in to KNN algorithm which will working way of KNN algorithm, compute KNN distance matrix, Makowski distance, live examples of implementation of KNN in industry.

We will look in to PCA, K means clustering, Agglomerative clustering which will be part of unsupervised learning.

Along all part of machine supervised and unsupervised learning , we will be following data reading , data prerprocessing, EDA, data scaling, preparation of training and testing data along machine learning model selection , implemention and prediction of models.

Who this course is for:

  • Anyone interested in Data Science
  • Data Science professionals
  • Machine learning engineer
  • Learner who want to use Machine Learning to their CV or career toolkit

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