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Machine Learning A-Z: Become Kaggle Master

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Machine Learning A-Z: Become Kaggle Master, available at $79.99, has an average rating of 4.4, with 257 lectures, based on 518 reviews, and has 3179 subscribers.

You will learn about Master Machine Learning on Python Learn to use MatplotLib for Python Plotting Learn to use Numpy and Pandas for Data Analysis Learn to use Seaborn for Statistical Plots Learn All the Mathmatics Required to understand Machine Learning Algorithms Implement Machine Learning Algorithms along with Mathematic intutions Projects of Kaggle Level are included with Complete Solutions Learning End to End Data Science Solutions All Advanced Level Machine Learning Algorithms and Techniques like Regularisations , Boosting , Bagging and many more included Learn All Statistical concepts To Make You Ninza in Machine Learning Real World Case Studies Model Performance Metrics Deep Learning Model Selection This course is ideal for individuals who are This course is meant for anyone who wants to become a Data Scientist It is particularly useful for This course is meant for anyone who wants to become a Data Scientist.

Enroll now: Machine Learning A-Z: Become Kaggle Master

Summary

Title: Machine Learning A-Z: Become Kaggle Master

Price: $79.99

Average Rating: 4.4

Number of Lectures: 257

Number of Published Lectures: 257

Number of Curriculum Items: 257

Number of Published Curriculum Objects: 257

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master Machine Learning on Python
  • Learn to use MatplotLib for Python Plotting
  • Learn to use Numpy and Pandas for Data Analysis
  • Learn to use Seaborn for Statistical Plots
  • Learn All the Mathmatics Required to understand Machine Learning Algorithms
  • Implement Machine Learning Algorithms along with Mathematic intutions
  • Projects of Kaggle Level are included with Complete Solutions
  • Learning End to End Data Science Solutions
  • All Advanced Level Machine Learning Algorithms and Techniques like Regularisations , Boosting , Bagging and many more included
  • Learn All Statistical concepts To Make You Ninza in Machine Learning
  • Real World Case Studies
  • Model Performance Metrics
  • Deep Learning
  • Model Selection

Who Should Attend

  • This course is meant for anyone who wants to become a Data Scientist

Target Audiences

  • This course is meant for anyone who wants to become a Data Scientist

Want to become a good Data Scientist?  Then this is a right course for you.

This course has been designed by IIT professionals who have mastered in Mathematics and Data Science.  We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advance level.

We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.

We have covered following topics in detail in this course:

1. Python Fundamentals

2. Numpy

3. Pandas

4. Some Fun with Maths

5. Inferential Statistics

6. Hypothesis Testing

7. Data Visualisation

8. EDA

9. Simple Linear Regression

10. Multiple Linear regression

11. Hotstar/ Netflix: Case Study

12. Gradient Descent

13. KNN

14. Model Performance Metrics

15. Model Selection

16. Naive Bayes

17. Logistic Regression

18. SVM

19. Decision Tree

20. Ensembles – Bagging / Boosting

21. Unsupervised Learning

22. Dimension Reduction

23. Advance ML Algorithms

24. Deep Learning

Course Curriculum

Chapter 1: Python Fundamentals

Lecture 1: Introduction to the course

Lecture 2: Introduction to Kaggle

Lecture 3: Installation of Python and Anaconda

Lecture 4: Python Introduction

Lecture 5: Variables in Python

Lecture 6: Numeric Operations in Python

Lecture 7: Logical Operations

Lecture 8: If else Loop

Lecture 9: for while Loop

Lecture 10: Functions

Lecture 11: String part1

Lecture 12: String part2

Lecture 13: List Part1

Lecture 14: List Part2

Lecture 15: List Part3

Lecture 16: List Part4

Lecture 17: Tuples

Lecture 18: Sets

Lecture 19: Dictionaries

Lecture 20: Comprehentions

Chapter 2: Numpy

Lecture 1: Introduction

Lecture 2: Numpy Operations Part1

Lecture 3: Numpy Operations Part2

Chapter 3: Pandas

Lecture 1: Introduction

Lecture 2: Series

Lecture 3: DataFrame

Lecture 4: Operations Part1

Lecture 5: Operations Part2

Lecture 6: Indexes

Lecture 7: loc and iloc

Lecture 8: Reading CSV

Lecture 9: Merging Part1

Lecture 10: groupby

Lecture 11: Merging Part2

Lecture 12: Pivot Table

Chapter 4: Some Fun With Maths

Lecture 1: Linear Algebra : Vectors

Lecture 2: Linear Algebra : Matrix Part1

Lecture 3: Linear Algebra : Matrix Part2

Lecture 4: Linear Algebra : Going From 2D to nD Part1

Lecture 5: Linear Algebra : 2D to nD Part2

Chapter 5: Inferential Statistics

Lecture 1: Inferential Statistics

Lecture 2: Probability Theory

Lecture 3: Probability Distribution

Lecture 4: Expected Values Part1

Lecture 5: Expected Values Part2

Lecture 6: Without Experiment

Lecture 7: Binomial Distribution

Lecture 8: Commulative Distribution

Lecture 9: PDF

Lecture 10: Normal Distribution

Lecture 11: z Score

Lecture 12: Sampling

Lecture 13: Sampling Distribution

Lecture 14: Central Limit Theorem

Lecture 15: Confidence Interval Part1

Lecture 16: Confidence Interval Part2

Chapter 6: Hypothesis Testing

Lecture 1: Introduction

Lecture 2: NULL And Alternate Hypothesis

Lecture 3: Examples

Lecture 4: One/Two Tailed Tests

Lecture 5: Critical Value Method

Lecture 6: z Table

Lecture 7: Examples

Lecture 8: More Examples

Lecture 9: p Value

Lecture 10: Types of Error

Lecture 11: t- distribution Part1

Lecture 12: t- distribution Part2

Chapter 7: Data Visualisation

Lecture 1: Matplotlib

Lecture 2: Seaborn

Lecture 3: Case Study

Lecture 4: Seaborn On Time Series Data

Chapter 8: Exploratory Data Analysis

Lecture 1: Introduction

Lecture 2: Data Sourcing and Cleaning part1

Lecture 3: Data Sourcing and Cleaning part2

Lecture 4: Data Sourcing and Cleaning part3

Lecture 5: Data Sourcing and Cleaning part4

Lecture 6: Data Sourcing and Cleaning part5

Lecture 7: Data Sourcing and Cleaning part6

Lecture 8: Data Cleaning part1

Lecture 9: Data Cleaning part2

Lecture 10: Univariate Analysis Part1

Lecture 11: Univariate Analysis Part2

Lecture 12: Segmented Analysis

Lecture 13: Bivariate Analysis

Lecture 14: Derived Columns

Chapter 9: Simple Linear Regression

Lecture 1: Introduction to Machine Learning

Lecture 2: Types of Machine Learning

Lecture 3: Introduction to Linear Regression (LR)

Lecture 4: How LR Works?

Lecture 5: Some Fun With Maths Behind LR

Instructors

Rating Distribution

  • 1 stars: 20 votes
  • 2 stars: 19 votes
  • 3 stars: 59 votes
  • 4 stars: 193 votes
  • 5 stars: 227 votes

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