Masterclass of Machine Learning with Python

Masterclass of Machine Learning with Python, available at $19.99, has an average rating of 4.6, with 74 lectures, based on 43 reviews, and has 390 subscribers.

You will learn about The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning Problem Solving Approach Impress interviewers by showing an understanding of the Machine Learning Algorithm concept Python Basic to Advance Concept with Numpy, Pandas, Matplotlib, Seaborn, Plotly Library Scikit Learn Library in Depth Machine Learning Algorithms such as Linear, Logistic, SVM, KNN, K Mean, Na?ve Bayes, Decision Tree and Random Forest Machine Learning Types Such as Supervise Learning, Unsupervised Learning, Reinforcement Learning Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation 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 Machine Learning Algorithms or People interested to learn Machine Learning Algorithms using Scikit Learning Library and 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 Machine Learning Algorithms or People interested to learn Machine Learning Algorithms using Scikit Learning Library and Python.

Enroll now: Masterclass of Machine Learning with Python

Summary

Title: Masterclass of Machine Learning with Python

Price: $19.99

Average Rating: 4.6

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: ?999

Quality Status: approved

Status: Live

What You Will Learn

The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning

Problem Solving Approach

Impress interviewers by showing an understanding of the Machine Learning Algorithm concept

Python Basic to Advance Concept with Numpy, Pandas, Matplotlib, Seaborn, Plotly Library

Scikit Learn Library in Depth

Machine Learning Algorithms such as Linear, Logistic, SVM, KNN, K Mean, Na?ve Bayes, Decision Tree and Random Forest

Machine Learning Types Such as Supervise Learning, Unsupervised Learning, Reinforcement Learning

Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation

Who Should Attend

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

People interested to learn Machine Learning Algorithms using Scikit Learning Library and Python

Target Audiences

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

People interested to learn Machine Learning Algorithms using Scikit Learning Library and Python

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 using Scikit Learn library. The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning. Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

A subfield of artificial intelligence (AI) and computer science called machine learning focuses on using data and algorithms to simulate how humans learn, gradually increasing the accuracy of the system.

With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programmes can predict outcomes more accurately without having to be explicitly instructed to do so. In order to forecast new output values, machine learning algorithms use historical data as input.

Machine learning is frequently used in recommendation engines. Business process automation (BPA), predictive maintenance, spam filtering, malware threat detection, and fraud detection are a few additional common uses.

Machine learning is significant because it aids in the development of new goods and provides businesses with a picture of trends in consumer behaviour and operational business patterns. A significant portion of the operations of many of today’s top businesses, like Facebook, Google, and Uber, revolve around machine learning. For many businesses, machine learning has emerged as a key competitive differentiation.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview in Animation

Lecture 2: Why to join this course?

Lecture 3: Meet Trainer for this Course

Lecture 4: Supervise Learning

Chapter 2: Python Tutorial with its Libraries

Lecture 1: Introduction of Python and Python Libraries

Lecture 2: Environment Set up

Lecture 3: Data Type, Variable and Keywords

Lecture 4: how to take input

Lecture 5: How to produce output _ Print Statement in Python

Lecture 6: List, Tuple, Set, Dictionary

Lecture 7: List Operations in details

Lecture 8: Tuple Operations in details

Lecture 9: Set Operations in details

Lecture 10: Dictionary Operations in details

Lecture 11: String Operation in Python

Lecture 12: Data Type Conversion

Lecture 13: Types of Operator

Lecture 14: Math library

Lecture 15: Generation of Random Number and Range Functions

Lecture 16: Importance of Indentation

Lecture 17: Sequential, Selection, Repetition

Lecture 18: Python CSV file Operations

Lecture 19: User Define Functions and inbuilt Function

Lecture 20: Python Crash Course

Lecture 21: Numpy Library Tutorial 1

Lecture 22: Numpy Library Tutorial 2

Lecture 23: Numpy Library Tutorial 3

Lecture 24: Numpy Library Tutorial 4

Lecture 25: Numpy Library Tutorial 5

Lecture 26: Numpy Library Tutorial 6

Lecture 27: Numpy Library Tutorial 7

Lecture 28: Numpy Library Official Site Visit

Lecture 29: Pandas Tutorial 1

Lecture 30: Pandas Tutorial 2

Lecture 31: Pandas Tutorial 3

Lecture 32: Pandas Tutorial 4

Lecture 33: Pandas Tutorial 5

Lecture 34: Pandas Tutorial 6

Lecture 35: Pandas Tutorial 7

Lecture 36: Pandas Tutorial 8

Lecture 37: How to choose the RIGHT Charts & Graph for your Data

Lecture 38: Matplotlib Library Tutorial 1

Lecture 39: Matplotlib Library Tutorial 2

Lecture 40: Matplotlib Library Tutorial 3

Lecture 41: Matplotlib Library Tutorial 4

Lecture 42: Matplotlib Library Official Site Visit

Lecture 43: Seaborn Library Tutorial 1

Lecture 44: Seaborn Library Tutorial 2

Lecture 45: Seaborn Library Official Site Visit

Lecture 46: Plotly Library Tutorial

Chapter 3: Different Sources for Dataset

Lecture 1: Different Sources for Dataset

Chapter 4: Advance Statistics Technique using Excel

Lecture 1: When to Use Which statistical Method

Lecture 2: T Test and Z Test

Lecture 3: Chi Square

Lecture 4: Anova

Chapter 5: Machine Learning Foundation

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

Lecture 2: Unsupervised Learning

Lecture 3: Reinforcement Learning

Lecture 4: Confusion Matrix

Lecture 5: Dimension Reduction is Curse in Machine Learning

Lecture 6: Reasons to Learn Probability for Machine Learning

Lecture 7: Data Science Introduction

Lecture 8: Steps to Start Project in Data Science with Machine Learning

Lecture 9: Case Study of Titanic Dataset

Lecture 10: Case Study of Suicides in India 2001-2012

Lecture 11: Case Study on Google Review using various different plot using Matplotlib

Chapter 6: 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: Complete Guide to Scikit Learn Library with Case Study on Diabetes Dataset

Lecture 1: Case Study

Chapter 8: Complete Guide to Scikit Learn Library with Case Study on Titanic Dataset

Lecture 1: Case Study

Instructors

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

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 6 votes

5 stars: 34 votes

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