Master Machine Learning: Basics, Jobs and Interview Bootcamp

Learn to create Machine Learning Algorithms in Python | Interview Questions | Fundamental Machine Learning Concepts

4.75 (2 reviews)
Udemy
platform
English
language
Data Science
category
instructor
15
students
6.5 hours
content
Oct 2020
last update
$39.99
regular price

What you will learn

Master Machine Learning on Python , Make Machine Learning models, Build powerful Machine Learning models and know how to combine them to solve any problem

Master Machine Learning on Python

Make accurate predictions using Machine Learning.

Make powerful analysis

Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Description

This course is designed by Manik Soni, professional Data Scientists so that I can share my knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own machine learning models.

  1. Master Machine Learning on Python

  2. Have a great intuition of many Machine Learning models

  3. Make accurate predictions

  4. Make a powerful analysis

  5. Make robust Machine Learning models

  6. Create strong added value to your business

  7. Use Machine Learning for personal purpose

  8. Handle advanced techniques like Dimensionality Reduction

  9. Know which Machine Learning model to choose for each type of problem

  10. Build an army of powerful Machine Learning models and know-how to combine them to solve any problem

  11. Questions for Job Interview


    Who this course is for:

    • Anyone interested in Machine Learning.

    • Students who have at least high school knowledge in math and who want to start learning Machine Learning.

    • Any intermediate-level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

    • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

    • Any students in college who want to start a career in Data Science.

    • Any data analysts who want to level up in Machine Learning.

    • Any people who are not satisfied with their job and who want to become a Data Scientist.

    • Any people who want to create added value to their business by using powerful Machine Learning tools.

Content

Introduction

Introduction
Importance of Machine Learning

Data PreProcessing

Importing Basic Libraries
Importing DataSet
Matrix of Features and Dependent Variable
Processing of Missing Values
Processing of Categorical Data
Splitting the DataSet into Train and Test Set
Feature Scaling on DataSet

Simple Linear Regression

Introduction to Simple Linear Regression
Ordinary Least Squares
CODE : Simple Regression(Part 1)
CODE : Simple Regression(Part 2)
CODE : Simple Regression(Part 3)
visualisation of simple Linear Regression Model

Multiple Linear Regression

Introduction to Multiple Linear Regression
Dummy Variable and Dummy Variable Trap
Introduction to Build a Model ?
Backward Elimination
CODE : Backward Elimination(PART1)
CODE : Backward Elimination(PART2)
CODE : Backward Elimination(PART3)

Polynomial Regression

Introduction to Polynomial Regression ?
CODE : Polynomial Regression

Decision Tree Regression

Introduction to Decision Tree Regression ?
CODE : Decision Tree Regression

Random Forest Regression

Introduction to Random Forest Regression?
CODE : Random Forest Regression

Logistic Regression

Introduction to Logistic Regression?
CODE : Logistic Regression(PART1)
CODE : Logistic Regression(PART2)
Confusion Matrix
Logistic Regression Visualization

K-Nearest Neighbor

Introduction to K-Nearest Neighbor?
CODE : K-Nearest Neighbor

Support Vector Machine(SVM)

Introduction to Support Vector Machine(SVM)?
CODE: Support Vector Machine(SVM)

Kernel - Support Vector Machine(SVM)

Linearly Separable Vs Non Linearly Separable
Mapping to higher dimensions
Deep Knowledge of Kernel Function
Types of Kernel Function
CODE : Kernel Support Vector Machine(SVM)

Naive Bayes

Introduction to Bayes Theorem?
Introduction to Project Naive Bayes with example
CODE : Naive Bayes

Decision Tree Classification

Introduction to Decision Tree Classification
CODE : Decision Tree Classification

Random Forest Classification

Introduction to Random Forest Classification
CODE : Random Forest Classification

K- Means Clustering

Introduction to K- Means Clustering ?
What is Random Initialization Trap ?
How to choose right number of clusters?
CODE: K-Means Clustering?

Hierarchical Clustering

Introduction to Hierarchical Clustering
What is Dendogram and How it works?
CODE : Hierarchical Clustering

Capstone Project and Interview Questions

Capstone Project
Interview Questions

Screenshots

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3463994
udemy ID
8/31/2020
course created date
11/7/2020
course indexed date
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