Complete Machine Learning 2024 A-Z™- 10 Real World Projects

Complete Machine Learning 2024 A-Z&: 10 Real World Projects, available at $69.99, has an average rating of 4.83, with 139 lectures, based on 470 reviews, and has 5725 subscribers.

You will learn about Python Machine Learning Statistics and Math Data Science Natural Language Processing Data Analysis Data Visualization This course is ideal for individuals who are Beginner or Intermediate or Advanced It is particularly useful for Beginner or Intermediate or Advanced.

Enroll now: Complete Machine Learning 2024 A-Z&: 10 Real World Projects

Summary

Title: Complete Machine Learning 2024 A-Z&: 10 Real World Projects

Price: $69.99

Average Rating: 4.83

Number of Lectures: 139

Number of Published Lectures: 137

Number of Curriculum Items: 139

Number of Published Curriculum Objects: 137

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Python

Machine Learning

Statistics and Math

Data Science

Natural Language Processing

Data Analysis

Data Visualization

Who Should Attend

Beginner

Intermediate

Advanced

Target Audiences

Beginner

Intermediate

Advanced

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.

It gives detailed guide on the Data science process involved and Machine Learning algorithms. All the algorithms are covered in detail so that the learner gains good understanding of the concepts. Although Machine Learning involves use of pre-developed algorithms one needs to have a clear understanding of what goes behind the scene to actually convert a good model to a great model.

Our exotic journey will include the concepts of:

1. Comparison between Artificial intelligence, Machine Learning, Deep Learning and Neural Network.

2. What is data science and its need.

3. The need for machine Learning and introduction to NLP (Natural Language Processing).

4. The different types of Machine Learning – Supervised and Unsupervised Learning.

5. Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease.

6. All the important libraries you would need to work on Machine learning lifecycle.

7. Full-fledged course on Statistics so that you don’t have to take another course for statistics, we cover it all.

8. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.

9. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future.

10. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.

11. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go.

12. Total 10 beginner to Advance level projects so that you can test your skills.

Course Curriculum

Chapter 1: Introduction to Data Science and Machine Learning

Lecture 1: Introduction to Data Science and ML

Lecture 2: ML Process and Types

Chapter 2: Python Basics, Decision Making and Loops

Lecture 1: Python_Installation

Lecture 2: Python Practice Guidelines

Lecture 3: Python_Numbers

Lecture 4: Practice Numbers

Lecture 5: String Operations

Lecture 6: String Slicing

Lecture 7: Practice Strings

Lecture 8: Practice String Functions

Lecture 9: Lists

Lecture 10: Boolean Operations

Lecture 11: If Else Conditions

Lecture 12: For and While Loops

Lecture 13: Functions

Chapter 3: Python Data Structures

Lecture 1: List comprehension

Lecture 2: Dictionaries

Lecture 3: Sets

Lecture 4: Tuples

Lecture 5: Dynamic Function Arguments

Lecture 6: Lambda functions, Map, Reduce, Filter

Chapter 4: Python Practice Questions

Lecture 1: Practice Sets and Dictionary

Lecture 2: List Comprehension Practice

Lecture 3: Functions Practice

Lecture 4: Functions Practice 2

Lecture 5: Practice String and List Comprehension

Lecture 6: Practice Functions

Chapter 5: OOPS

Lecture 1: Intro to OOPS

Lecture 2: OOPS : Without vs with OOPS

Lecture 3: OOPS: classes objects attributes

Lecture 4: OOPS: Methods

Lecture 5: OOPS: Inheritance

Lecture 6: OOPS: Polymorphism

Lecture 7: OOPS: Encapsulation

Lecture 8: Practice OOPS

Lecture 9: Python Assignment

Chapter 6: Descriptive Statistics

Lecture 1: Introduction to Statistics_Population & Sampling

Lecture 2: Measure Of Central Tendencies Mean Median Mode

Lecture 3: Measure Of Variability – Variance Standard Deviation IQR

Lecture 4: Data Diatributions Correlation & Covariance

Lecture 5: Descriptive statistics Practice questions

Chapter 7: Inferential Statistics: Intro, Central Limit Theorem,Z-Score,CI

Lecture 1: Intro to Inferential Statistics

Lecture 2: Variable Types

Lecture 3: Central_Limit_Theorem

Lecture 4: Z-Score

Lecture 5: Confidence Interval

Lecture 6: CI examples

Chapter 8: Hypothesis Testing

Lecture 1: Hypothesis Testing Introduction

Lecture 2: Hypothesis Testing Theory Explained

Lecture 3: Type of Errors and Significant Difference

Chapter 9: T-Test, chi-Square , AnOVa Test and more

Lecture 1: T-Tests

Lecture 2: Chi Square test of Goodness of Fit

Lecture 3: Chi Square test of Independance

Lecture 4: Anova

Lecture 5: Which test to pick

Lecture 6: Statistics Using Graphpad

Chapter 10: Case Study: Statistics on House Pricing Data Set

Lecture 1: Inferential Statistics Case Study

Chapter 11: Data Preparation : Numpy, Pandas, working with DataFrames

Lecture 1: Data Preparation Guidelines

Lecture 2: Data Preparation

Lecture 3: Numpy

Lecture 4: Reading and Writing to files

Lecture 5: Pandas introduction

Lecture 6: Pandas on Dataframe

Lecture 7: Pandas Sorting Merging

Lecture 8: Pandas: Stack unstack melt

Chapter 12: Numerical Analysis, and Data Visualization

Lecture 1: Data Preparation using Pandas

Lecture 2: Data Visualization using Matplotlib and Seaborn

Chapter 13: Case Study: Data Preparation Loans DataSet

Lecture 1: Numerical Summary

Chapter 14: Feature selection and Data Preparation (Structured and Text Data)

Lecture 1: Feature Selection

Lecture 2: Feature Selection Code

Lecture 3: NLP_Text Data preparation

Lecture 4: NLP_hands On

Lecture 5: Data Preparation Assignments and Solutions

Chapter 15: Modelling : Supervised Learning

Lecture 1: Supervised Learning

Lecture 2: Linear Regression Introduction

Lecture 3: Linear_Regression_Cost_Gradient_CV

Lecture 4: Linear Regression_Implementation

Lecture 5: Linear Regression_Regularization

Lecture 6: Logistic Regression: Introduction

Lecture 7: Logistic Regression: Mathematics

Lecture 8: Logistic Regression: Metrics

Lecture 9: Logistic Regression: Code

Lecture 10: Sklearn Metrics: Explained

Lecture 11: Decision Trees: Introduction and Rule generation

Lecture 12: Decision_Tree: Splitting

Instructors

MG Analytics
Data Scientist and Professional Trainer

Rating Distribution

1 stars: 15 votes

2 stars: 13 votes

3 stars: 47 votes

4 stars: 127 votes

5 stars: 268 votes

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