Machine Learning in Python: From Zero to Hero in 10 Hours

Machine Learning in Python: From Zero to Hero in 10 Hours, available at $74.99, has an average rating of 4.4, with 78 lectures, 3 quizzes, based on 69 reviews, and has 429 subscribers.

You will learn about Hands-on explanation of every major Machine Learning techniques. Model Development, Deployment and Monitoring. Regression: Simple, Polynomial, and Multinomial Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes Ensemble Modeling: Voting Classifier, Bagging, Boosting, Stacking, Random Forest Implementation of every concepts explained in the course. Source codes are made available to you for your use. Data Visualization with MatPlotLib and Seaborn Use train, test and Cross Validation to choose and tune data Feature Engineering (Reduce Noise, Outliers) and Data Preprocessing Practical examples of How to trade-off between Bias, Variance, Irreducible errors using Ensemble Learning model and Bagging, Boosting Understand how to implement Machine Learning at massive scale Understand math and statistics behind Machine Learning models This course is ideal for individuals who are Students and professionals who want to become Machine Learning Expert or Data Scientist. or IT Professionals, Mathematicians, Statisticians. or Machine learning enthusiasts. or Project Managers, Data Analytics, and Business Intelligence Professionals. or Python developers. It is particularly useful for Students and professionals who want to become Machine Learning Expert or Data Scientist. or IT Professionals, Mathematicians, Statisticians. or Machine learning enthusiasts. or Project Managers, Data Analytics, and Business Intelligence Professionals. or Python developers.

Enroll now: Machine Learning in Python: From Zero to Hero in 10 Hours

Summary

Title: Machine Learning in Python: From Zero to Hero in 10 Hours

Price: $74.99

Average Rating: 4.4

Number of Lectures: 78

Number of Quizzes: 3

Number of Published Lectures: 75

Number of Published Quizzes: 3

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 78

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Hands-on explanation of every major Machine Learning techniques.
Model Development, Deployment and Monitoring.
Regression: Simple, Polynomial, and Multinomial
Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes
Ensemble Modeling: Voting Classifier, Bagging, Boosting, Stacking, Random Forest
Implementation of every concepts explained in the course. Source codes are made available to you for your use.
Data Visualization with MatPlotLib and Seaborn
Use train, test and Cross Validation to choose and tune data
Feature Engineering (Reduce Noise, Outliers) and Data Preprocessing
Practical examples of How to trade-off between Bias, Variance, Irreducible errors using Ensemble Learning model and Bagging, Boosting
Understand how to implement Machine Learning at massive scale
Understand math and statistics behind Machine Learning models

Who Should Attend

Students and professionals who want to become Machine Learning Expert or Data Scientist.
IT Professionals, Mathematicians, Statisticians.
Machine learning enthusiasts.
Project Managers, Data Analytics, and Business Intelligence Professionals.
Python developers.

Target Audiences

Students and professionals who want to become Machine Learning Expert or Data Scientist.
IT Professionals, Mathematicians, Statisticians.
Machine learning enthusiasts.
Project Managers, Data Analytics, and Business Intelligence Professionals.
Python developers.

Join the most comprehensive Machine Learning Hands-on Course, because now is the time to get started!

From basic concepts about Python Programming, Supervised Machine Learning, Unsupervised Machine Learningto Reinforcement Machine Learning, Natural Language Processing (NLP),this course covers all you need to know to become a successful Machine Learning Professional!

But that’s not all! Along with covering all the steps of Machine Learning functions,this course also has quizzes and projects, which allow you to practice the things learned throughout the course!

You’ll not only learn about the concepts but also practice each of those concepts through hands-on and real-life Projects.

And if you do get stuck, you benefit from extremely fast and friendly support – both via direct messaging or discussion. You have my word!

With more than two decades of IT experience, I have designed this course for students and professionals who wish to master how to develop and support industry-standard Machine learning projects.

This course will be kept up-to-date to ensure you don’t miss out on any changes once Machine Learning is required in your project!

Why Machine Learning?

In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to available data, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.

If you are looking for a thriving career in Data Analytics, Artificial Intelligence, Robotics, this is the right time to learn Machine Learning.

Don’t be left out and prepare well for these opportunities.

So, what are you waiting for?

Pay once, benefit a lifetime! This is an evolving course! Machine Learningand future enhancements will be covered in this course. You won’t lose out on anything! Don’t lose any time, gain an edge, and start now!

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is Machine Learning

Lecture 2: Machine Learning Terms and Keywords

Lecture 3: Types of ML Algorithm

Chapter 2: Basic Math and Statistics

Lecture 1: Different Types of Data

Chapter 3: Prerequisite Tools

Lecture 1: IDE tools

Lecture 2: Anaconda & Jupyter Notebook Installation

Lecture 3: Google Collab

Lecture 4: Kaggle

Chapter 4: Python Programming

Lecture 1: Python Basics

Lecture 2: Numpy- Single Dimensional Array (Vector)

Lecture 3: Numpy-Multidimensional Array (Matrix)

Lecture 4: Numpy-Statistical Functions

Lecture 5: Pandas

Lecture 6: Pandas- Time Series

Lecture 7: Matplot

Lecture 8: Seaborn

Chapter 5: Data Pre-processing

Lecture 1: Introduction

Lecture 2: Data Preprocessing

Chapter 6: Part 1: Supervised Learning -> Regression

Lecture 1: Supervised Learning : Regression

Chapter 7: Simple Linear Regression

Lecture 1: Simple Linear Regression

Lecture 2: Hans-on: Simple Linear Regression Algorithm

Lecture 3: Hands-on: Simple Linear Regression -1

Lecture 4: Hands-on: Simple Linear Regression -2

Chapter 8: Multiple Linear Regression (MLR)

Lecture 1: Multiple Linear Regression Introduction

Lecture 2: Multiple Linear Regression Data Set

Lecture 3: Hands-on: Multiple Linear Regression – 1

Lecture 4: Hands-on: Multiple Linear Regression -2

Lecture 5: Hands-on: Multiple Linear Regression -3

Chapter 9: Polynomial Linear Regression (PLR)

Lecture 1: Polynomial Regression Introduction

Lecture 2: Hands-on: Polynomial Linear Regression

Chapter 10: K-Nearest Neighbors (KNN)

Lecture 1: KNN Introduction

Lecture 2: Hand-on: KNN Regression- Step1

Lecture 3: Hands-on: KNN Regression -Step2

Chapter 11: Advanced Regression Techniques

Lecture 1: LASSO and Ridge

Chapter 12: Part 2: Supervised Learning -> Classification

Lecture 1: Classification Models

Chapter 13: KNN Classifier

Lecture 1: KNN Classifier – Data Source

Lecture 2: Hands-on: KNN Classifier -Step1

Lecture 3: Hands-on: KNN Classifier – Step2

Chapter 14: Logistic Regression

Lecture 1: Logistic Regression Introduction

Lecture 2: Logistic Regression Data Source

Lecture 3: Hands-on:Logistic Regression- Step1

Lecture 4: Hands-on: Logistic Regression – Step2

Lecture 5: Hands-on: Logistic Regression – Step3

Chapter 15: Support Vector Machine (SVM)

Lecture 1: Vector Dot Product

Lecture 2: Support Vector Machine (SVM) Intuition

Lecture 3: Hands-on: SVM -Linear Kernel

Lecture 4: Hands-on: SVM -RBF Kernel

Chapter 16: Naive Bayes

Lecture 1: Bayes Theorem

Lecture 2: Naive Bayes Intuition

Lecture 3: Hands-on: Gaussian Naive Bayes

Lecture 4: Multinomial Naive Bayes Intuition

Lecture 5: NLP and Naive Bayes Projec t-Prerequisite

Lecture 6: Countervectorizers

Lecture 7: Term Frequency (TF) – Inverse Document Frequency (IDF)

Lecture 8: Hands-on: Multinomial Naive Bayes- 1

Lecture 9: Hands-on: Multinomrial Naive Bayes -2

Chapter 17: Decision Tree

Lecture 1: Decision Tree

Lecture 2: Decision Tree Classifier – Data Source

Lecture 3: Hands-on Decision Tree Classifier

Lecture 4: Decision Tree Classifier Intuition

Lecture 5: Decision Tree- GINI, Depth, Sample, Entropy

Chapter 18: Ensemble Learning

Lecture 1: Bias, Variance & Irreducible Errors

Lecture 2: Ensemble Learning -Data Source

Lecture 3: Voting Classifier

Lecture 4: Voting Classifier-2

Lecture 5: Bagging

Lecture 6: Random Forest

Lecture 7: Boosting

Lecture 8: Adaboost

Lecture 9: Gradient Boosting – Intuition

Lecture 10: Gradient Boosting

Chapter 19: K-Fold Validation

Lecture 1: Cross Validation Introduction

Chapter 20: Model Deployment

Lecture 1: Model Deployment

Lecture 2: Model Deployment as REST API

Chapter 21: Bonus Lectures

Lecture 1: Further Learning

Instructors

Sanjay Singh
Data and Machine Learning Professional

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 7 votes
4 stars: 20 votes
5 stars: 40 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!

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