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Master Machine Learning: Basics, Jobs and Interview Bootcamp

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Master Machine Learning: Basics, Jobs and Interview Bootcamp, available at $39.99, has an average rating of 4.75, with 58 lectures, based on 2 reviews, and has 16 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone interested in Machine Learning , Any people who want to create added value to their business by using powerful Machine Learning tools , Any people who are not satisfied with their job and who want to become a Data Scientist or Those who are not able to find jobs in machine learning field. or Those who want to make a better future using machine learning. or Those who want to brush up there basics in machine learning field. It is particularly useful for Anyone interested in Machine Learning , Any people who want to create added value to their business by using powerful Machine Learning tools , Any people who are not satisfied with their job and who want to become a Data Scientist or Those who are not able to find jobs in machine learning field. or Those who want to make a better future using machine learning. or Those who want to brush up there basics in machine learning field.

Enroll now: Master Machine Learning: Basics, Jobs and Interview Bootcamp

Summary

Title: Master Machine Learning: Basics, Jobs and Interview Bootcamp

Price: $39.99

Average Rating: 4.75

Number of Lectures: 58

Number of Published Lectures: 58

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 58

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

  • Anyone interested in Machine Learning , Any people who want to create added value to their business by using powerful Machine Learning tools , Any people who are not satisfied with their job and who want to become a Data Scientist
  • Those who are not able to find jobs in machine learning field.
  • Those who want to make a better future using machine learning.
  • Those who want to brush up there basics in machine learning field.

Target Audiences

  • Anyone interested in Machine Learning , Any people who want to create added value to their business by using powerful Machine Learning tools , Any people who are not satisfied with their job and who want to become a Data Scientist
  • Those who are not able to find jobs in machine learning field.
  • Those who want to make a better future using machine learning.
  • Those who want to brush up there basics in machine learning field.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Importance of Machine Learning

Chapter 2: Data PreProcessing

Lecture 1: Importing Basic Libraries

Lecture 2: Importing DataSet

Lecture 3: Matrix of Features and Dependent Variable

Lecture 4: Processing of Missing Values

Lecture 5: Processing of Categorical Data

Lecture 6: Splitting the DataSet into Train and Test Set

Lecture 7: Feature Scaling on DataSet

Chapter 3: Simple Linear Regression

Lecture 1: Introduction to Simple Linear Regression

Lecture 2: Ordinary Least Squares

Lecture 3: CODE : Simple Regression(Part 1)

Lecture 4: CODE : Simple Regression(Part 2)

Lecture 5: CODE : Simple Regression(Part 3)

Lecture 6: visualisation of simple Linear Regression Model

Chapter 4: Multiple Linear Regression

Lecture 1: Introduction to Multiple Linear Regression

Lecture 2: Dummy Variable and Dummy Variable Trap

Lecture 3: Introduction to Build a Model ?

Lecture 4: Backward Elimination

Lecture 5: CODE : Backward Elimination(PART1)

Lecture 6: CODE : Backward Elimination(PART2)

Lecture 7: CODE : Backward Elimination(PART3)

Chapter 5: Polynomial Regression

Lecture 1: Introduction to Polynomial Regression ?

Lecture 2: CODE : Polynomial Regression

Chapter 6: Decision Tree Regression

Lecture 1: Introduction to Decision Tree Regression ?

Lecture 2: CODE : Decision Tree Regression

Chapter 7: Random Forest Regression

Lecture 1: Introduction to Random Forest Regression?

Lecture 2: CODE : Random Forest Regression

Chapter 8: Logistic Regression

Lecture 1: Introduction to Logistic Regression?

Lecture 2: CODE : Logistic Regression(PART1)

Lecture 3: CODE : Logistic Regression(PART2)

Lecture 4: Confusion Matrix

Lecture 5: Logistic Regression Visualization

Chapter 9: K-Nearest Neighbor

Lecture 1: Introduction to K-Nearest Neighbor?

Lecture 2: CODE : K-Nearest Neighbor

Chapter 10: Support Vector Machine(SVM)

Lecture 1: Introduction to Support Vector Machine(SVM)?

Lecture 2: CODE: Support Vector Machine(SVM)

Chapter 11: Kernel – Support Vector Machine(SVM)

Lecture 1: Linearly Separable Vs Non Linearly Separable

Lecture 2: Mapping to higher dimensions

Lecture 3: Deep Knowledge of Kernel Function

Lecture 4: Types of Kernel Function

Lecture 5: CODE : Kernel Support Vector Machine(SVM)

Chapter 12: Naive Bayes

Lecture 1: Introduction to Bayes Theorem?

Lecture 2: Introduction to Project Naive Bayes with example

Lecture 3: CODE : Naive Bayes

Chapter 13: Decision Tree Classification

Lecture 1: Introduction to Decision Tree Classification

Lecture 2: CODE : Decision Tree Classification

Chapter 14: Random Forest Classification

Lecture 1: Introduction to Random Forest Classification

Lecture 2: CODE : Random Forest Classification

Chapter 15: K- Means Clustering

Lecture 1: Introduction to K- Means Clustering ?

Lecture 2: What is Random Initialization Trap ?

Lecture 3: How to choose right number of clusters?

Lecture 4: CODE: K-Means Clustering?

Chapter 16: Hierarchical Clustering

Lecture 1: Introduction to Hierarchical Clustering

Lecture 2: What is Dendogram and How it works?

Lecture 3: CODE : Hierarchical Clustering

Chapter 17: Capstone Project and Interview Questions

Lecture 1: Capstone Project

Lecture 2: Interview Questions

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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