# Master Machine Learning and Data Science with Python

Learn Pandas, Scikit-Learn, Seaborn, Matplotlib, Machine Learning, NLP, Dealing with practical problems and more!

**Language**: english

**Note**: 4.8/5 (7 notes) 1,571 students ** New course **

**Instructor(s)**: Jifry Issadeen

**Last update**: 2022-08-14

## What you’ll 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.

## Requirements

- We have included a Python training kit for beginners, so, NO programming knowledge is required.
- There is NO prerequisite knowledge of Machine Learning or Data Science. Everything will be taught to you from the ground up.
- You should have a computer/tablet and time to learn.. That’s all.

## Description

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 **over 19 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

## Who this course is for

- 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.

## Course content

- Introduction
- Welcome Message and Important Instructions
- Download Resources
- Python Installation
- Access Notebook Files with Jupyter Notebook
- Jupyter Notebook Walkthrough Tutorial

- Python Basics – Starter Kit
- Getting started with Python
- Variables – Types
- Variables – Usage
- Variables – Strings
- Variables – Integers, Floats and Booleans
- Lists
- Tuples
- Dictionaries and Sets
- If Statements
- for loop
- while loop
- Custom Functions
- List Comprehensions
- Lambda Function
- Built-in Functions
- External Libraries
- Python Exercise Overview
- Python Exercise Solution – Part 1
- Python Exercise Solution – Part 2

- Introduction to Machine Learning
- Introduction to Machine Learning
- Introduction to Machine Learning
- Machine Learning Life-Cycle
- Machine Learning Life-Cycle
- Introduction to Performance Evaluation – Classification
- Introduction to Performance Evaluation – Classification Metrics
- Confusion Matrix
- Confusion Matrix
- Main Classification Metrics
- Main Classification Metrics
- Performance Evaluation – Regression
- Performance Evaluation – Regression
- Introduction to Sklearn
- One Hot encoding
- Split the Data
- What is Fit?

- Linear Regression
- Linear Regression Theory
- Linear Regression – Theory
- Linear Regression – Salary Prediction – Practical – Part 1
- Linear Regression – Salary Prediction – Practical – Part 2
- Linear Regression – House Price Prediction – Practical – Part 1
- Linear Regression – House Price Prediction – Practical – Part 2
- Linear Regression – Practical

- Logistic Regression
- Logistic Regression – Theory
- Logistic Regression – Theory
- Logistic Regression – Iris Flower – Practical
- Logistic Regression – Gender Classification – Exercise Overview
- Logistic Regression – Exercise Solution – Gender Classification – Part 1
- Logistic Regression – Exercise Solution – Gender Classification – Part 2

- Lasso and Ridge Regression / Regularizations
- Lasso and Ridge Regression – Theory
- Lasso and Ridge Regression – Theory
- Lasso and Ridge Regression – Melbourne Housing – Practice – Part 1
- Lasso and Ridge Regression – Melbourne Housing – Practice – Part 2
- Lasso and Ridge Regression – Melbourne Housing – Practice – Part 3
- Lasso and Ridge – Insurance – Exercise overview
- Lasso and Ridge – Insurance – Solution to the Exercise

- Dealing with Practical Issues
- Bias Variance Trade-off
- Bias Variance Trade-off
- Dealing with Imbalanced Data
- Dealing with Imbalanced Data
- Dealing with Missing Values
- Dealing with Missing Values
- Dealing with Outliers – Theory
- Dealing with Outliers – Practical
- Dealing with Outliers
- Feature Scaling of Data – Theory
- Feature Scaling – Practical
- Feature Scaling of Data

- Naïve Bayes Classifier (Gaussian)
- Gaussian Naïve Bayes Classifier – Theory
- Gaussian Naïve Bayes Classifier
- Gaussian Naïve Bayes Classifier – Titanic – Practical – Part 1
- Gaussian Naïve Bayes Classifier – Titanic – Practical – Part 2

- Decision Trees
- Decision Tree – Theory
- Decision Tree – Penguin – Practical
- Decision Tree – Wine Quality – Exercise – Overview
- Decision Tree – Wine Quality – Exercise Solution

- Random Forest
- Random Forest – Theory
- Random Forest – Theory
- Random Forest – Practical – Bike Sharing – Part 1
- Random Forest – Practical – Bike Sharing – Part 2
- Random Forest – WeatherAUS – Exercise Overview
- Random Forest – weatherAUS – Solution Part 1
- Random Forest – weatherAUS – Solution Part 2
- Extra Tree – Theory

- Boosting Techniques
- Introduction to Boosting Techniques
- Boosting Techniques Theory – Adaboost
- Boosting Techniques Theory – Gradient Boosting
- Boosting Techniques – Adult – Practical Implementation
- Boosting Techniques

- Support Vector Machines
- SVM Theory
- SVM – Practical – Heart Disease Classification
- SVM – Water Potability – Exercise Overview
- SVM – Water Potability – Exercise Solution

- K-Nearest Neighbors
- KNN Theory
- KNN – Practical – Classified Data
- K-Nearest Neighbor

- Unsupervised Machine Learning Algorithms
- K-Means Clustering Theory
- K-Means Clustering – Practice – Iris
- K-Means Clustering
- DBSCAN Clustering – Theory
- DBSCAN Clustering – Practical
- DBSCAN Clustering

- PCA – Principal Component Analysis
- Principal Component Analysis – Theory
- PCA – Practical – Airline Passenger – Part 1
- PCA – Practical – Airline Passenger – Part 2
- PCA – Principal Component Analysis

- Natural Language Processing
- NLP – Natural Language Processing – Introduction – Theory
- NLP – Naïve Bayes Multinomial Classification – Theory
- NLP – Practical – Amazon Reviewer Classification – Part 1
- NLP – Practical – Amazon Reviewer Classification – Part 2
- NLP – Practical – Amazon Reviewer Classification – Part 3
- NLP – Natural Language Processing

**Time remaining or 429 enrolls left**

Don’t miss any coupons by joining our Telegram group |