Complete Machine Learning & Data Science with Python| ML A-Z

Complete Machine Learning & Data Science with Python| ML A-Z, available at $59.99, has an average rating of 4.15, with 78 lectures, based on 470 reviews, and has 25191 subscribers.

You will learn about Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more Machine learning Concept and Different types of Machine Learning Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc.. Feature engineering Python Basics This course is ideal for individuals who are Anyone interested in Machine Learning. or Any students in college who want to start a career in Data Science. It is particularly useful for Anyone interested in Machine Learning. or Any students in college who want to start a career in Data Science.

Enroll now: Complete Machine Learning & Data Science with Python| ML A-Z

Summary

Title: Complete Machine Learning & Data Science with Python| ML A-Z

Price: $59.99

Average Rating: 4.15

Number of Lectures: 78

Number of Published Lectures: 78

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more
Machine learning Concept and Different types of Machine Learning
Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..
Feature engineering
Python Basics

Who Should Attend

Anyone interested in Machine Learning.
Any students in college who want to start a career in Data Science.

Target Audiences

Anyone interested in Machine Learning.
Any students in college who want to start a career in Data Science.

Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.

This Data Science & Machine Learning (ML) course is not only ‘Hands-On’ practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI.

In this course several Machine Learning (ML) projects are included.

1) Project – Customer Segmentation Using K Means Clustering

2) Project – Fake News Detection using Machine Learning (Python)

3) Project COVID-19: Coronavirus Infection Probability using Machine Learning

4) Project – Image compression using K-means clustering | Color Quantization using K-Means

This course include topics —

What is Data Science
Describe Artificial Intelligence and Machine Learning and Deep Learning
Concept of Machine Learning – Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement Learning
Python for Data Analysis- Numpy
Working envirnment-
Google Colab
Anaconda Installation
Jupyter Notebook
Data analysis-Pandas
Matplotlib
What is Supervised Machine Learning
Regression
Classification
Multilinear Regression Use Case- Boston Housing Price Prediction
Save Model
Logistic Regression on Iris Flower Dataset
Naive Bayes Classifier on Wine Dataset
Naive Bayes Classifier for Text Classification
Decision Tree
K-Nearest Neighbor(KNN) Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm I
What is UnSupervised Machine Learning
Types of Unsupervised Learning
Advantages and Disadvantages of Unsupervised Learning
What is clustering?
K-means Clustering
Image compression using K-means clustering | Color Quantization using K-Means
Underfitting, Over-fitting and best fitting in Machine Learning
How to avoid Overfitting in Machine Learning
Feature Engineering
Teachable Machine
Python Basics

In the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories. Everyday a new app, product or service unveils that it is using machine learning to get smarter and better.

NOTE :- In description reference notes also provided , open reference notes , there is Download link. You can download datasets there.

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Data Science

Lecture 2: Describe Artificial Intelligence and Machine Learning and Deep Learning

Lecture 3: Concept of Machine Learning

Chapter 2: Installation and Colab

Lecture 1: Google Colab Introduction

Lecture 2: Anaconda Installation

Lecture 3: Jupyter Notebook

Chapter 3: Python for Data Analysis- Numpy

Lecture 1: Python for Data Analysis- Numpy

Lecture 2: linear algebra for data science

Chapter 4: Python for Data Analysis- Pandas

Lecture 1: Pandas Series

Lecture 2: Pandas DataFrames

Lecture 3: Grouping and Filtering

Lecture 4: Slicing and Sorting

Lecture 5: Pandas Missing Values

Lecture 6: Pandas Aggregation Functions

Chapter 5: Python for Data Visualization – Matplotlib

Lecture 1: Matplotlib Introduction

Lecture 2: Matplotlib Bar Graphs

Lecture 3: Matplotlib Histogram

Lecture 4: Matplotlib Scatter Plot

Lecture 5: Matplotlib Area Plot

Lecture 6: Matplotlib Pie Chart

Lecture 7: Matplotlib Subplots

Chapter 6: Supervised Machine Learning

Lecture 1: What is Supervised Machine Learning and Linear Regression Algorithm

Lecture 2: Implementation of Linear Regression

Lecture 3: Plot Linear Regression

Lecture 4: Implementation of Multi Linear Regression

Lecture 5: Example 2- Multilinear Regression

Lecture 6: Use Case- Boston Housing Price Prediction

Lecture 7: Save Model

Lecture 8: how to implement Logistic Regression

Lecture 9: Logistic Regression on Iris Flower Dataset

Lecture 10: Logistic Regression on Digits Dataset

Lecture 11: Naive Bayes Classifier

Lecture 12: Implementation of Naive Bayes classifier on Wine Dataset

Lecture 13: Introduction to Naive Bayes Classifier for Text Classification

Lecture 14: Implementating Naive Bayes Classifier for Text Classification

Lecture 15: Introduction to Decision Tree

Lecture 16: Implementation of Decision Tree

Lecture 17: K-Nearest Neighbor(KNN) Algorithm

Lecture 18: Implementation of K-Nearest Neighbor(KNN) Algorithm

Lecture 19: Support Vector Machine Algorithm

Lecture 20: Implementation of Support Vector Machine Algorithm

Lecture 21: Random Forest Algorithm

Lecture 22: Implementation of Random Forest Algorithm

Chapter 7: UnSupervised Machine Learning

Lecture 1: What is UnSupervised Machine Learning

Lecture 2: Types of Unsupervised Learning

Lecture 3: Advantages and Disadvantages of Unsupervised Learning

Lecture 4: What is clustering?

Lecture 5: K-means Clustering

Lecture 6: Implementing K-means Clustering

Lecture 7: Image compression using K-means clustering | Color Quantization using K-Means

Chapter 8: Reinforcement Learning

Lecture 1: Introduction to Reinforcement Learning

Lecture 2: Q-learning algorithm in machine learning

Chapter 9: Feature Engineering

Lecture 1: What is Feature Engineering

Lecture 2: What is Outlier

Lecture 3: outlier detection and removal using std deviation

Lecture 4: Z score

Lecture 5: IQR

Chapter 10: Project – Customer Segmentation using Clustering

Lecture 1: Customer Segmentation Part 1

Lecture 2: Customer Segmentation Part 2

Lecture 3: Customer segmentation 3

Lecture 4: Customer Segmentation Part 4

Chapter 11: Project – Fake News Detection using machine learning (Python)

Lecture 1: Fake News Detection using machine learning (Python)

Chapter 12: Project COVID-19: Coronavirus Infection Probability using machine learning

Lecture 1: Project COVID-19: Coronavirus Infection Probability using machine learning

Chapter 13: Miscellaneous

Lecture 1: Underfitting, Overfitting and best fitting in Machine Learning

Lecture 2: How to avoid Overfitting in Machine Learning

Lecture 3: Applications of Artificial Intelligence

Lecture 4: Teachable Machine

Chapter 14: Python Crash Course

Lecture 1: Python Basic Introduction

Lecture 2: Getting Started with Python

Lecture 3: Python Data Types-Numbers

Lecture 4: Python Data Types-Strings

Lecture 5: Python Data Types-Lists

Lecture 6: Python Data Types- Dictionary

Lecture 7: Python Data Types- Sets

Lecture 8: Python Loops

Lecture 9: Decision Making

Lecture 10: Control Statements

Lecture 11: Python Functions

Instructors

Goeduhub Technologies
Technical Training Provider Company.

Rating Distribution

1 stars: 7 votes
2 stars: 17 votes
3 stars: 71 votes
4 stars: 162 votes
5 stars: 213 votes

Frequently Asked Questions

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