Tutorials > Introduction To Machine Learning For Begineers[Data–Science] 2023

Introduction To Machine Learning For Begineers[Data–Science] 2023

Date: 28 July 2023 | Posted by: voska89

Last updated 7/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.05 GB | Duration: 2h 7m
Learn about Data Science and Machine Learning with Python including Pandas, matplotlib with projects,quizes
Free Download What you'll learn

Basics of machine learning on python
Fundamental of machine learning
Learn about different types of machine learning agorithms
Make powerful analysis
Make accurate prediction on different datasets by using machine learning.
Know which Machine learning model to choose for each type of problem
Create complex visualization with matplotlib
Linear regression,Logistic Regression,Knn,Decision Tree,Naive Bayes,Random Forest
Requirements
A working computer with windows OS
Bbasics of python programming
Just some high school mathematics level
Anaconda software
Description
HERE IS WHY UOU SHOULD TAKE THIS COURSE:This course complete guides you to both supervised and unsupervised learning using python.This means ,this course covers all the main aspects of practical Data science and if you take this course you can done with taking other courses or buying books on Python based Data Science.In this age of big data companies across the globe use python to shift through the avalanche of information at their disposal .By becoming proficient in supervised and unsupervised learning in python you can give your company a competitive edge and boost your careeer to the next level.'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''LEARN FROM AN EXPERT DATA SCIENTIST WITH 3+ YEARS OF EXPERIENCEMy Name is Aakash Singh and I had also recently Published my Research Paper in INTERNATIONAL JOURNAL IJSR on Machine Learning Dataset.This course will help you to roboust grounding in the main machine learning clustering and classifiation....................................................................................................................................................................................................................................NO PRIOR PYTHON OR STATICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:You will start by absorbing the most valuable python data science basics and techniques.I use easy to understand hands on methods to simplify and address even the most difficult concepts in python.My course will help you to implement the methods using real data obtained from different sources.after taking this course you will easily use package like Numpy,Pandas and Mathplotlib to work with real data in python.We will go through Lab section on JUPYTER NOTEBOOK terminal ,we will go through lots of real like examples for increasing practical side knowledge of programming and we should not neglect theory section also ,Which is essential for this course for this course by the end of this course you will be able to code in python language and feel confident with Machine Learning and you will be able to create your own program and implement where you want.Most importantly uou will learn to implement these techniques pracitically using python ,you will have access to all the data and scripts used in this course remember i am always around you to support my student. SIGN UP NOW!......
Overview
Section 1: Complete machine learning series
Lecture 1 Introduction
Section 2: How Machine Learns?
Lecture 2 how machine learns?
Section 3: Installation of lab
Lecture 3 jupyter notebook installation
Section 4: Introduction To Pandas
Lecture 4 pandas
Section 5: DATA VISUALIZATION
Lecture 5 data visualization
Section 6: DATA PRE-PROCESSING
Lecture 6 Data Preprocessing theory
Lecture 7 Data Preprocessing code
Section 7: LINEAR REGRESSION
Lecture 8 Linear Regression theory
Lecture 9 Linear Regression code
Section 8: LOGISTIC REGRESSION
Lecture 10 Logistic Regression theory
Lecture 11 Logistic Regression code
Section 9: KNN Algorithm
Lecture 12 KNN theory
Lecture 13 KNN code
Section 10: DECISION TREE
Lecture 14 Decision Tree Theory
Lecture 15 Decision Tree code
Section 11: NAIVE BAYES
Lecture 16 Naive Bayes Theory
Lecture 17 Naive Bayes code
Section 12: RANDOM FOREST
Lecture 18 Random Forest Theory
Lecture 19 Random Forest code
Section 13: PROJECT
Lecture 20 Project
Anyone who wants to learn concept of machine learning,Any student in college who want to start career in data science,Any data analysts who want to level up in machine learning

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