Python for Mastering Machine Learning and Data Science

Python for Mastering Machine Learning and Data Science, available at $69.99, has an average rating of 4.75, with 93 lectures, 24 quizzes, based on 71 reviews, and has 2606 subscribers.

You will learn about Understand Python programming concepts: Variables, lists, tuples, sets and Dictionaries. Comfortably deal with Python programming concepts: If statements, loops, custom functions, built-in functions, comprehensions, lambda functions and more.. Comfortably create, evaluate and improve the performance of famous machine learning models with the help of Python Identify the most suitable machine learning algorithm to practically deal with the problem you are solving. Be comfortable with the theoretical elements of each machine learning model. Broad understanding of each machine learning concepts and their practice implementation with Python programming language. Be comfortable with Exploratory data analysis. Distinguish the different algorithms and capable of selecting the best. Parameter tuning and model improvements. Be comfortable dealing with Outliers, Missing Values, Feature Scaling, Imbalanced data and feature selection. Understand the idea behind the boosting techniques and how to implement them effectively. Be a pro who can deal with machine learning algorithms by your own. This course is ideal for individuals who are Anyone who is curious about data science. or Anyone who wants to properly understand and learn both theoretical and practice aspects of Machine learning. or Those who expect quizzes and practices to improve their skills while learning machine learning. or If you are someone who expects the real world challenges in the journey of machine learning. or You know machine learning but you prefer to improve both theoretical and practical aspect of it. It is particularly useful for Anyone who is curious about data science. or Anyone who wants to properly understand and learn both theoretical and practice aspects of Machine learning. or Those who expect quizzes and practices to improve their skills while learning machine learning. or If you are someone who expects the real world challenges in the journey of machine learning. or You know machine learning but you prefer to improve both theoretical and practical aspect of it.

Enroll now: Python for Mastering Machine Learning and Data Science

Summary

Title: Python for Mastering Machine Learning and Data Science

Price: $69.99

Average Rating: 4.75

Number of Lectures: 93

Number of Quizzes: 24

Number of Published Lectures: 93

Number of Published Quizzes: 23

Number of Curriculum Items: 117

Number of Published Curriculum Objects: 116

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Understand Python programming concepts: Variables, lists, tuples, sets and Dictionaries.
Comfortably deal with Python programming concepts: If statements, loops, custom functions, built-in functions, comprehensions, lambda functions and more..
Comfortably create, evaluate and improve the performance of famous machine learning models with the help of Python
Identify the most suitable machine learning algorithm to practically deal with the problem you are solving.
Be comfortable with the theoretical elements of each machine learning model.
Broad understanding of each machine learning concepts and their practice implementation with Python programming language.
Be comfortable with Exploratory data analysis.
Distinguish the different algorithms and capable of selecting the best.
Parameter tuning and model improvements.
Be comfortable dealing with Outliers, Missing Values, Feature Scaling, Imbalanced data and feature selection.
Understand the idea behind the boosting techniques and how to implement them effectively.
Be a pro who can deal with machine learning algorithms by your own.

Who Should Attend

Anyone who is curious about data science.
Anyone who wants to properly understand and learn both theoretical and practice aspects of Machine learning.
Those who expect quizzes and practices to improve their skills while learning machine learning.
If you are someone who expects the real world challenges in the journey of machine learning.
You know machine learning but you prefer to improve both theoretical and practical aspect of it.

Target Audiences

Anyone who is curious about data science.
Anyone who wants to properly understand and learn both theoretical and practice aspects of Machine learning.
Those who expect quizzes and practices to improve their skills while learning machine learning.
If you are someone who expects the real world challenges in the journey of machine learning.
You know machine learning but you prefer to improve both theoretical and practical aspect of it.

Welcome to the best Machine Learning and Data Science with Python course in the planet. Are you ready to start your journey to becoming a Data Scientist?

In this comprehensive course, you’ll begin your journey with installation and learning the basics of Python. Once you are ready, the introduction to Machine Learning section will give you an overview of what Machine Learning is all about, covering all the nitty gritty details before landing on your very first algorithm. You’ll learn a variety of supervised and unsupervised machine learning algorithms, ranging from linear regression to the famous boosting algorithms. You’ll also learn text classification using Natural Language processing where you’ll deal with an interesting problem.

Data science has been recognized as one of the best jobs in the world and it’s on fire right now. Not only it has a very good earning potential, but also it facilitates the freedom to work with top companies globally. Data scientists also gets the opportunity to deal with interesting problems, while being invaluable to the organization and enjoy the satisfaction of transforming the way how businesses make decisions. Machine learning and data science is one of the fastest growing and most in demand skills globally and the demand is growing rapidly. Parallel to that, Python is the easiest and most used programming language right now and that’s the first language choice when it comes to the machine learning. So, there is no better time to learn machine learning using python than today.

I designed this course keeping the beginners and those who with some programming experience in mind. You may be coming from the Finance, Marketing, Engineering, Medical or even a fresher, as long as you have the passion to learn, this course will be your first step to become a Data Scientist.

I have 20 hours of best quality video contents. There are over 90 HD video lectures each ranging from 5 to 20 minutes on average. I’ve included Quizzes to test your knowledge after each topic to ensure you only leave the chapter after gaining the full knowledge. Not only that, I’ve given you many exercises to practice what you learn and solution to the exercise videos to compare the results. I’ve included all the exercise notebooks, solution notebooks, data files and any other information in the resource folder.

Now, I’m gonna answer the most important question. Why should you choose this course over the other courses?

I cover all the important machine learning concepts in this course and beyond.
When it comes to machine learning, learning theory is the key to understanding the concepts well. We’ve given the equal importance to the theory section which most of the other courses don’t.
We’ve used the graphical tools and the best possible animations to explain the concepts which we believe to be a key factor that would make you enjoy the course.
Most importantly, I’ve a dedicated section covering all the practical issues you’d face when solving machine learning problems. This is something that other courses tend to ignore.
I’ve set the course price to the lowest possible amount so that anyone can afford the course.

Here a just a few of the topics we will be learning:

Install Python and setup the virtual environment
Learn the basics of Python programming including variables, lists, tuples, sets, dictionaries, if statements, for loop, while loop, construct a custom function, Python comprehensions, Python built-in functions, Lambda functions and dealing with external libraries.
Use Python for Data Science and Machine Learning
Learn in-dept theoretical aspects of all the machine learning models
Open the data, perform pre-processing activities, build and evaluate the performance of the machine learning models Implement Machine Learning Algorithms
Learn, Visualization techniques like Matplotlib and Seaborn
Use SciKit-Learn for Machine Learning Tasks
K-Means Clustering
DBSCAN Clustering
K-Nearest Neighbors
Logistic Regression
Linear Regression
Lasso and Ridge – Regularization techniques
Random Forest and Decision Trees and Extra Tree
Naïve Bayes Classifier
Support Vector Machines
PCA – Principal Component Analysis
Boosting Techniques – Adaboost, Gradient boost, XGBoost, Catboost and LightGBM
Natural Language Processing
How to deal with the practical problems when dealing with Machine learning

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome Message and Important Instructions

Lecture 2: Download Resources

Lecture 3: Python Installation

Lecture 4: Access Notebook Files with Jupyter Notebook

Lecture 5: Jupyter Notebook Walkthrough Tutorial

Chapter 2: Python Basics – Starter Kit

Lecture 1: Getting started with Python

Lecture 2: Variables – Types

Lecture 3: Variables – Usage

Lecture 4: Variables – Strings

Lecture 5: Variables – Integers, Floats and Booleans

Lecture 6: Lists

Lecture 7: Tuples

Lecture 8: Dictionaries and Sets

Lecture 9: If Statements

Lecture 10: for loop

Lecture 11: while loop

Lecture 12: Custom Functions

Lecture 13: List Comprehensions

Lecture 14: Lambda Function

Lecture 15: Built-in Functions

Lecture 16: External Libraries

Lecture 17: Python Exercise Overview

Lecture 18: Python Exercise Solution – Part 1

Lecture 19: Python Exercise Solution – Part 2

Chapter 3: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Machine Learning Life-Cycle

Lecture 3: Introduction to Performance Evaluation – Classification

Lecture 4: Confusion Matrix

Lecture 5: Main Classification Metrics

Lecture 6: Performance Evaluation – Regression

Lecture 7: Introduction to Sklearn

Lecture 8: One Hot encoding

Lecture 9: Split the Data

Lecture 10: What is Fit?

Chapter 4: Linear Regression

Lecture 1: Linear Regression Theory

Lecture 2: Linear Regression – Salary Prediction – Practical – Part 1

Lecture 3: Linear Regression – Salary Prediction – Practical – Part 2

Lecture 4: Linear Regression – House Price Prediction – Practical – Part 1

Lecture 5: Linear Regression – House Price Prediction – Practical – Part 2

Chapter 5: Logistic Regression

Lecture 1: Logistic Regression – Theory

Lecture 2: Logistic Regression – Iris Flower – Practical

Lecture 3: Logistic Regression – Gender Classification – Exercise Overview

Lecture 4: Logistic Regression – Exercise Solution – Gender Classification – Part 1

Lecture 5: Logistic Regression – Exercise Solution – Gender Classification – Part 2

Chapter 6: Lasso and Ridge Regression / Regularizations

Lecture 1: Lasso and Ridge Regression – Theory

Lecture 2: Lasso and Ridge Regression – Melbourne Housing – Practice – Part 1

Lecture 3: Lasso and Ridge Regression – Melbourne Housing – Practice – Part 2

Lecture 4: Lasso and Ridge Regression – Melbourne Housing – Practice – Part 3

Lecture 5: Lasso and Ridge – Insurance – Exercise overview

Lecture 6: Lasso and Ridge – Insurance – Solution to the Exercise

Chapter 7: Dealing with Practical Issues

Lecture 1: Bias Variance Trade-off

Lecture 2: Dealing with Imbalanced Data

Lecture 3: Dealing with Missing Values

Lecture 4: Dealing with Outliers – Theory

Lecture 5: Dealing with Outliers – Practical

Lecture 6: Feature Scaling of Data – Theory

Lecture 7: Feature Scaling – Practical

Chapter 8: Naïve Bayes Classifier (Gaussian)

Lecture 1: Gaussian Naïve Bayes Classifier – Theory

Lecture 2: Gaussian Naïve Bayes Classifier – Titanic – Practical – Part 1

Lecture 3: Gaussian Naïve Bayes Classifier – Titanic – Practical – Part 2

Chapter 9: Decision Trees

Lecture 1: Decision Tree – Theory

Lecture 2: Decision Tree – Penguin – Practical

Lecture 3: Decision Tree – Wine Quality – Exercise – Overview

Lecture 4: Decision Tree – Wine Quality – Exercise Solution

Chapter 10: Random Forest

Lecture 1: Random Forest – Theory

Lecture 2: Random Forest – Practical – Bike Sharing – Part 1

Lecture 3: Random Forest – Practical – Bike Sharing – Part 2

Lecture 4: Random Forest – WeatherAUS – Exercise Overview

Lecture 5: Random Forest – weatherAUS – Solution Part 1

Lecture 6: Random Forest – weatherAUS – Solution Part 2

Lecture 7: Extra Tree – Theory

Chapter 11: Boosting Techniques

Lecture 1: Introduction to Boosting Techniques

Instructors

Jifry Issadeen
CEO @ Data Kottu

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 4 votes
4 stars: 16 votes
5 stars: 50 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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