Data Science with Machine Learning Algorithm using Python

Data Science with Machine Learning Algorithm using Python, available at $19.99, has an average rating of 3.9, with 88 lectures, based on 56 reviews, and has 716 subscribers.

You will learn about The course provides path to become a data scientist Problem Solving Approach Impress interviewers by showing an understanding of the data science concept with Machine Learning Python Basic to Advance Concept Python Libraries for Data Analysis such Numpy, Scipy, Pandas Python Libraries for Data Visualization such Matplotlib, Seaborn, Plotlypy Case Studies of Data Science with Coding Machine Learning With Linear Regression, Logistic Regression, SVM, NLP This course is ideal for individuals who are The course is ideal for beginners, as it starts from the fundamentals and gradually builds up your skills in Data Science with Machine Learning or People interested to learn data science with Machine Learning using Python It is particularly useful for The course is ideal for beginners, as it starts from the fundamentals and gradually builds up your skills in Data Science with Machine Learning or People interested to learn data science with Machine Learning using Python.

Enroll now: Data Science with Machine Learning Algorithm using Python

Summary

Title: Data Science with Machine Learning Algorithm using Python

Price: $19.99

Average Rating: 3.9

Number of Lectures: 88

Number of Published Lectures: 88

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: ?999

Quality Status: approved

Status: Live

What You Will Learn

The course provides path to become a data scientist

Problem Solving Approach

Impress interviewers by showing an understanding of the data science concept with Machine Learning

Python Basic to Advance Concept

Python Libraries for Data Analysis such Numpy, Scipy, Pandas

Python Libraries for Data Visualization such Matplotlib, Seaborn, Plotlypy

Case Studies of Data Science with Coding

Machine Learning With Linear Regression, Logistic Regression, SVM, NLP

Who Should Attend

The course is ideal for beginners, as it starts from the fundamentals and gradually builds up your skills in Data Science with Machine Learning

People interested to learn data science with Machine Learning using Python

Target Audiences

The course is ideal for beginners, as it starts from the fundamentals and gradually builds up your skills in Data Science with Machine Learning

People interested to learn data science with Machine Learning using Python

This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLP

This is best course for any one who wants to start career in data science. with machine Learning.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered.

Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Na?ve Bayes, Decision Tree and Random Forest are covered with case studies

Course Curriculum

Chapter 1: Everything about Python used for Data Science and Machine Learning

Lecture 1: Why to join this course?

Lecture 2: Introduction of Python and Python Libraries

Lecture 3: Meet Trainer for this Course

Lecture 4: Set Up – Python IDLE and Google Colab

Lecture 5: Data Type and Variable, Keywords

Lecture 6: How to take input?

Lecture 7: How to produce output?

Lecture 8: Introduction to List, Tuple, Dictionary, Set

Lecture 9: String Operations

Lecture 10: Operators in details

Lecture 11: List Operations in details

Lecture 12: Tuple Operations in details

Lecture 13: Set Operations in details

Lecture 14: Dictionary Operations in details

Lecture 15: Data Type Conversion

Lecture 16: Importance of Indentation

Lecture 17: Random Number, Range Function

Lecture 18: Sequential, Selection & Repetition -for, while, break, continue, if-elif else

Lecture 19: Math Library

Lecture 20: Datetime and Calendar Module

Lecture 21: Create, Edit, Write, Read Text File

Lecture 22: Exception Handling in Python

Lecture 23: Collection Module

Lecture 24: Python Queue

Lecture 25: User Define Functions and inbuilt Function

Lecture 26: Global and local Variables in Functions

Lecture 27: Lambda, Map, Filter and Reduce Function

Lecture 28: isinstance, Use of format, Timeit(), round(), Slice and abs()

Lecture 29: Iterator

Lecture 30: Generator and Decorators

Lecture 31: List Comprehension, Sets, Frozensets and Assertion

Lecture 32: Python CSV file Operations

Lecture 33: Zip Function

Lecture 34: eval(),exec(),repr() function

Lecture 35: Switch Case

Lecture 36: Ternary Operator

Lecture 37: Logging Module

Lecture 38: Python Crash Course

Lecture 39: Numpy Library Tutorial 1

Lecture 40: Numpy Library Tutorial 2

Lecture 41: Numpy Library Tutorial 3

Lecture 42: Numpy Library Tutorial 4

Lecture 43: Numpy Library Tutorial 5

Lecture 44: Numpy Library Tutorial 6

Lecture 45: Numpy Library Tutorial 7

Lecture 46: Numpy Library Office Site

Lecture 47: Scipy Tutorial 1

Lecture 48: Scipy Tutorial 2

Lecture 49: Pandas Library Tutorial 1

Lecture 50: Pandas Library Tutorial 2

Lecture 51: Pandas Library Tutorial 3

Lecture 52: Pandas Library Tutorial 4

Lecture 53: Pandas Library Tutorial 5

Lecture 54: Pandas Library Tutorial 6

Lecture 55: Pandas Library Tutorial 7

Lecture 56: Pandas Library Tutorial 8

Lecture 57: Pandas Library Tutorial 9

Lecture 58: Matplotlib Library Tutorial 1

Lecture 59: Matplotlib Library Tutorial 2

Lecture 60: Matplotlib Library Tutorial 3

Lecture 61: Matplotlib Library Tutorial 4

Lecture 62: Matplotlib Library Tutorial 5

Lecture 63: Seaborn Library Tutorial 1

Lecture 64: Seaborn Library Tutorial 2

Lecture 65: Seaborn Library Tutorial 3

Lecture 66: Plotly Library Tutorial

Lecture 67: How to choose the RIGHT Charts & Graph for your Data

Chapter 2: Data Science Introduction

Lecture 1: Data Science Introduction

Chapter 3: Data Science Example

Lecture 1: Case Study of Suicides in India 2001-2012

Lecture 2: Case Study of Titanic

Lecture 3: Case Study on Google Review using various different plot using Matplotlib

Chapter 4: Machine Learning Introduction

Lecture 1: Training, Testing and Model Evaluation in Machine Learning

Lecture 2: Supervise Learning made easy in Animation

Lecture 3: Unsupervised Machine Learning

Lecture 4: Reinforcement Learning

Lecture 5: Confusion Matrix

Lecture 6: Reasons to Learn Probability for Machine Learning

Lecture 7: Dimension Reduction is Curse in Machine Learning

Chapter 5: Steps to Start Project in Data Science

Lecture 1: Steps to Start Project in Data Science

Chapter 6: Data Science with Machine Learning Algorithms

Lecture 1: Linear Regression

Lecture 2: Logistic Regression

Lecture 3: Support Vector Machines (SVM)

Lecture 4: Support Vector Machines (SVM)

Lecture 5: K Mean Algorithm

Lecture 6: KNN Algorithm

Chapter 7: Scikit Learn Library Tutorial

Lecture 1: Complete Guide to Scikit Learn Library with Case Study on Diabetes Dataset

Lecture 2: Complete Guide to Scikit Learn Library with Case Study on Titanic Dataset

Chapter 8: Natural Language Processing

Lecture 1: NLP Tutorial

Instructors

Piyushh n Dave9
Professional Trainer of Python, Data Science, AI, ML, DL

Rating Distribution

1 stars: 0 votes

2 stars: 3 votes

3 stars: 5 votes

4 stars: 9 votes

5 stars: 39 votes

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!