MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!)

In-depth approach to ML easing you into the basics of ML and making you a pro out of it in no time. Grab this course now

4.70 (144 reviews)
Udemy
platform
English
language
Data Science
category
3,096
students
47 hours
content
Oct 2023
last update
$74.99
regular price

What you will learn

The most effective method to dodge issues with Machine Learning, to effectively execute it without losing your brain!

To realise what issues Machine Learning can illuminate, and how the Machine Learning Process functions

Use Python for Machine Learning

Percentiles, moment and Quantiles

Learn to utilise Matplotlib for Python Plotting

Learn to utilise Seaborn for measurable plots

Understand matrix multiplication, Matrix operations and scalar operations

Use Pair plot and limitations

Implement Identity matrix, matrix inverse properties, transpose of matrix, Vector multiplication

Implement Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression

AdaBoost and XGBoost regressor, SVM (regression) Background, SVR under Python

ML Concept-k-Fold validation, GridSearch

Classification-k-nearest neighbours’ algorithm (KNN)

Gaussian Naive Bayes under python & visualization of models

Learn evaluation techniques using curves (ROC, AUC, PR, CAP)

Implement machine learning algorithms

More topics coming soon

Description

Welcome to the MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!)

In this course, we will take you on a journey from a beginner to a proficient practitioner in the exciting field of Machine Learning. Whether you are a beginner or have prior programming experience, this course is designed to equip you with the knowledge and skills needed to excel in machine learning and data science. Whether you're interested in data science, or statistics, or simply want to kick-start your Machine Learning journey, this course covers all the essential theory and practical techniques you need to succeed. With step-by-step tutorials and real-life examples, you'll not only gain knowledge but also get hands-on practice building your own models.

Here's a breakdown of what you'll learn in each section of the course:

Course Overview:

Section 1 - Python Basics and Advanced Concepts:

  • Learn the fundamentals of Python programming, including decorators and generators.

  • Explore essential libraries such as NumPy and Pandas for efficient data manipulation and analysis.

Section 2 - Machine Learning Concepts:

  • Understand the core concepts of Unsupervised and Supervised learning.

  • Dive into statistical measures like standard deviation, percentiles, and quantiles.

  • Master descriptive statistics such as mean, mode, and median.

Section 3 - Data Preprocessing:

  • Learn how to split data into test and train sets for model evaluation.

  • Handle missing data and explore techniques like under and oversampling.

Section 4 - Regression:

  • Build a strong foundation in regression analysis, including simple linear regression, multiple linear regression, SVR, decision tree regression, random forest regression, and polynomial regression.

Section 5 - Classification:

  • Gain expertise in classification algorithms, including logistic regression, K-nearest neighbors (K-NN), support vector machines (SVM), naive Bayes, decision tree classification, and random forest classification.

Section 6 - Clustering:

  • Master the art of clustering with K-means clustering and learn to determine the optimal number of clusters.

Section 7 - Reinforcement Learning:

  • Explore reinforcement learning algorithms, focusing on the Upper Confidence Bound (UCB) approach.

Section 8 - Natural Language Processing (NLP):

  • Gain an introduction to NLP and its applications in text classification using machine learning.

  • Build your own text classifier using the techniques learned.

Section 9 - Deep Learning:

  • Delve into the fascinating world of deep learning, including neural networks, backpropagation, data representation using numbers, and activation functions.

Section 10 - Model Selection & Boosting:

  • Discover techniques for model selection and optimization, such as k-fold cross-validation, parameter tuning, and grid search.

  • Learn about the powerful XGBoost algorithm for boosting performance.

Section 11 - Web Application using Flask and Model Deployment:

  • Get hands-on experience in building a basic web application using Flask.

  • Learn how to deploy your machine learning models in a web application.

You'll also cover essential topics like feature selection, visualization, evaluation techniques, and many more.

Moreover, the course is packed with practical exercises that are based on real-life examples to reinforce your learning and enable you to build your own models confidently. So not only you will learn the theory, but you will also get some hands-on practice building your models.

Are you aware of the current high demand for skills in Data Science and Machine Learning? These fields are undoubtedly challenging to master. Have you ever found yourself wishing for a comprehensive course that covers aspects of Data Science and Machine Learning, including Math for Machine Learning, Data Processing, Machine Learning A-Z, Deep Learning, and much more?

Well, you have come to the right place.

Why Choose This Course?

  • Comprehensive Coverage: Our course covers everything from Python basics to advanced machine learning techniques, ensuring you have a solid foundation in the subject.

  • Practical Approach: We provide hands-on practice and real-life examples to help you apply the concepts you learn.

  • Experienced Instructor: With eight years of teaching experience to over 140,000+ students and industry expertise, I will guide you through the course with clarity and simplicity.

  • Clear Doubt Resolution: If you find any course content confusing, our instructor is readily available to answer your questions and clarify doubts.

  • High-Quality Teaching: Our unique teaching style focuses on simplicity and step-by-step learning, making complex concepts easy to understand.

  • Valuable Skill Set: Machine learning is in high demand across various industries, and mastering it will enhance your career prospects as a data scientist, machine learning engineer, or computer vision specialist.

This course stands out due to its unique teaching style, breaking down complex topics into easy-to-understand explanations and following a step-by-step approach. If you ever find the content confusing or need clarification, our experienced instructor will be available to address your doubts promptly.

Topics You’ll Learn:

  • Effective and efficient machine learning methods which are executed devoid of any issues

  • Issues that can be solved through Machine Learning

  • How Machine Learning can be used to process functions

  • Use Python for Machine Learning

  • Percentiles, moment and quantiles

  • Learn to utilize Matplotlib for Python plotting

  • Learn to utilize Seaborn for measurable plots

  • Learn Advance Mathematics for Machine Learning

  • Understand matrix multiplication, Matrix operations, and scalar operations

  • Use Pair plot and limitations

  • Implement Identity matrix, matrix inverse properties, transpose of a matrix, and Vector multiplication

  • Implement Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression

  • AdaBoost and XGBoost regressor, SVM (regression) Background, SVR under Python

  • ML Concept-k-Fold validation, GridSearch

  • Classification-k-nearest neighbours algorithm(KNN)

  • Gaussian Naive Bayes under Python & visualization of models

  • Learn evaluation techniques using curves (ROC, AUC, PR, CAP)

  • Implement machine learning algorithms

  • Model Deployment on Flask WebApplication

  • Natural Language Processing(NLP)

  • Deep Learning

  • And many more interesting topics.


Why is Machine Learning Important?

Machine learning has become crucial in today's data-driven world. With the availability of vast amounts of data, combined with advancements in computational power and affordable storage, machine learning algorithms play a vital role in extracting valuable insights and making data-driven decisions. Machine learning enables businesses to identify opportunities and risks quickly, gain a competitive edge, and drive innovation in various industries such as retail, healthcare, transportation, and more.

Learning Made Accessible:

This course provides a unique opportunity to learn machine learning from the comfort of your home. We understand that practical application is essential to master machine learning, so we offer hands-on exercises and real-world examples to enhance your skills. By completing this course, you will gain valuable experience and become a sought-after professional in the field of machine learning.


If you’re optimistic about reaping the benefits of having Machine Learning skills under your belt, then this course is for you!


No Question Asked – Money Back Guarantee!

The main barrier to people paying for a course to learn a daunting, challenging skill is whether it is suitable for them or whether they would be able to benefit from it. However, you can be at peace with the fact that you can opt out of this Machine Learning tutorial whenever you want to within 30 days. Basically, there is minimal risk involved with purchasing this course as it comes with a 30-day money-back guarantee. Once you purchase the course and later find that for any reason you are not satisfied with the course, you are entitled to a full refund, no questions asked.

Now that you know that you’ve got nothing to lose, so what are you waiting for? Purchase this course now and get access to a Machine Learning master class that gives you a step-by-step approach to Machine Learning.

Join Us Today:

Don't miss the chance to acquire powerful Machine Learning skills that are in high demand. Enroll now and embark on your journey to becoming a Machine Learning expert. Whether you are a beginner or an experienced programmer, this course will equip you with the knowledge and practical skills necessary to excel in the field of machine learning. By the end of this course, you would have Machine Learning at the tip of your fingers, along with the skills necessary to enter the high-paying and in-demand field of Data Science.

Learning enthusiasts will find this course appealing and would furnish their skill sets as well as provide weightage to their resumes.

Enroll now and unlock the power of machine learning from the comfort of your home!

                                          Join me on this adventure today! See you on the course.

Content

Python: Setting up

Python setting up
Jupyter notebook
Pycharm python IDE
Update: Anaconda website updated

Python: Basics

Data types
Python numbers
Variables and assignment
String basics
String Start Stop and Step
String slicing
String formatting
Lists in Python
List shorting, reversing, removing, clear, list of list
Sets
Tuples
Dictionary in python
None and Bool
Comparison operators
Logical operators

Python: Statements

If ElIf & else
While loop
For loop
Tuple unpacking
Break, continue and pass
Range, enumerate and zip
In
Input and import

Python: Method and Functions

User-defined functions
Help function
Scopes
args and kwargs
Maps, Filters and Lambdas
Lambda once again

Python: Module and packages

Python packages
User defined packages
User defined packages continues

Python: OOPS in python

Naming conventions and introduction
Class attributes and Methods
Inheritance
Multiple, multi level inheritance and MRO
Polymorphism
Special class methods

Python: Errors handling

Try except finally
Error types, else and finally

Python decorators and Generators

Python decorators
Class method decorator
Python generators

Python: Regular expression

Regular expression introduction
Regular expression, grouping and pipe
Repetition and range
Greedy, non-greedy matches and findall
BeginsWith endsWith and dot character
BeginsWith endsWith and dot character continues
Sets
Literal matching, Sub and verbose

Python: Files

Files introduction
Paths
Read mode, write mode and methods

Python: Numpy

Setting up
NumPy array functions - Array generate
Random array based methods
Slicing and broadcast
Matrices selection and conditional selection
Numpy operations

Python: Pandas

Panda series
DataFrame introduction
DataFrame Selections
GroupBy
Concatenation
Operations

More useful modules

Python random class
Random under numpy and Arange
Python collections
Python counter from collections
Math Matrix multiplication

Python: Matplotlib

Matplotlib simple plot, line graphs
Matplotlib Bar-graph and multiple plotting
Matplotlib Subplot and histogram
Matplotlib Scatter plots and Pie charts
Matplotlib 3D scatter and simple plot
Matpotlib Wireframe surface plotting

ML: Before we start

Introduction to ML & Supervised learning
Unsupervised learning
Type of data
Mean Mode median
Standard deviation
Most common data distributions, PDF and PMF
Percentiles, moment and Quantiles

Visualisation ( Exploratory Data Analysis) with Seaborn

Autocomplete on jupyter notebook
Scatter plot on Iris dataset
Pair plot and limitations
Tips dataset
Seaborn plots
Facetgrid plots
Univariate Analysis using PDF
Boxplot and Violin Plot
HeatMap

Linear Algebra basics for ML

Matrices
Matrix operations and scalar operations
Matrix multiplication
Identity matrix, matrix inverse properties, transpose of matrix

Pre-processing

Data import
handling missing data
Feature selection and Encoding categorical data
Test and train data split and Feature scaling
Under and over sampling
Assignment and tips
Assignment solution and OneHotEncoding - Part 01
Assignment solution and OneHotEncoding - Part 02

Linear Regression

Linear regression working and Cost function
Linear regression implementation in python - Part 1
Linear regression implementation in python - Part 2

Multiple linear regression

Multiple linear regression in Python
Multiple linear regression behind the scene - Part 1
Multiple linear regression behind the scene - Part 2

Polynomial regression

Polynomial regression
Polynomial regression on multiple feature dataset

Before we move forward

Bias, Variance and overfitting
Gradient decent - Background
Gradient decent in 2D and 3D space

Decision Tree regression

Measuring Entropy & Gini impurity

Coming soon

Coming soon

Screenshots

MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!) - Screenshot_01MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!) - Screenshot_02MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!) - Screenshot_03MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!) - Screenshot_04

Reviews

Jahan
October 31, 2023
this is my first machine learning course on internet . i would say this was a quite good journey . everything was fine but the only thing that sucks is at the end of this course like during reinforcement learning , deep learning mentor just explains you how it should be done , instead of how it works in background
Akshar
June 26, 2023
It was a really good match for me and I have understood everything whatever I have laernt, best course for starters
Randy
May 27, 2023
As a fellow instructor, I completely agree. Do not be quick to judge the value of the course until you see the results in the end. The real key with this course is that it seems that the entire course has great value.
Mikhail
April 12, 2022
I needed a course where the author explains how to connect a web app with our trained model. This course is covering this part, not a huge section but still I'm happy with how author explained the process =)
Eliot
January 26, 2022
It helps me a lot to find new technique for my task, I applied all of this and its really worked so good,,thanks a loy
David
January 26, 2022
This is course is absolutely fabulous.After complete this course i learned so many important new things and applied successfully,thank you
Roberts
January 26, 2022
This is really an amazing course to learn machine learning language properly, i am really happy after complete that
Pooja
November 5, 2021
It's a very good course for beginners to become a master in ML, go for it don't think too much about the quality of this course it is one of the best
Pooja
October 18, 2021
Firstly , Thank you so much sir , for great course , I learned from Beginning to Advanced Of Python , THank you.
Juned
August 20, 2021
The cource is really good.When i first started this cource at that time i have a just basic understanding of Machine learning but after completing this cource I can say that i have achieved something in Machine Learning. So thank you chand sir.
Amit
February 24, 2021
I, really loved the way Chaand sir explains the concepts, He explains most of the complex concepts in most simplest way possible.

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3329214
udemy ID
7/13/2020
course created date
9/16/2020
course indexed date
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