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Python for Mastering Machine Learning and Data Science

Name: Python for Mastering Machine Learning and Data Science
Rating: 4.6 (88 reviews)

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

Created byJifry Issadeen

Last updated 1/2023

English

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.

Course content

16 sections • 93 lectures • 19h 45m total length

Welcome Message and Important Instructions2:59
Join this beginner-friendly introduction to Python for machine learning, download course resources, install Anaconda and Python, explore Jupyter Notebook basics, and use Q&A for support.
Download Resources1:51
Python Installation11:15
Access Notebook Files with Jupyter Notebook6:07
Jupyter Notebook Walkthrough Tutorial16:42

Getting started with Python3:03
Variables - Types10:37
Variables - Strings13:05
Explore Python string basics with zero-based and negative indexing, and slicing using examples like a name and color. Apply string methods such as lower, upper, and title to transform text.
Variables - Usage6:46
Learn how to replace hardcoded names with variables using formatted strings (f-strings) and curly braces, so changing one variable updates all appearances; variables store strings and enable reuse across code.
Variables - Integers, Floats and Booleans6:35
Lists15:35
Tuples11:59
Explore tuples in Python, compare them with lists, and learn to create, access, slice, and convert between tuples and lists while understanding their immutability.
Dictionaries and Sets16:23
If Statements18:11
for loop16:54
while loop9:53
Custom Functions12:43
Learn how to define and use custom Python functions to perform tasks, pass arguments, handle input, and return results, with examples like len and summing lists.
List Comprehensions10:50
Built-in Functions21:19
Lambda Function14:14
External Libraries6:30
Python Exercise Overview10:20
Explore Python exercises spanning two parts, from user input and string manipulation to list, dictionary, and function tasks, emphasizing basic operations and introductory problem solving.
Python Exercise Solution - Part 120:06
Python Exercise Solution - Part 218:50

Introduction to Machine Learning13:48
This gives a general broad idea of the machine learning and the different type of them.
Introduction to Machine Learning
Machine Learning Life-Cycle6:12
After learning the basic idea of what machine learning is, the best thing to learn next is to understand how the overall process happens in general. This is basically the life-cycle of a data science project presented as the machine learning life-cycle.
Machine Learning Life-Cycle
Introduction to Performance Evaluation - Classification5:27
Since we are focusing on building models, we should be able to evaluate the performance of the model. This lecture helps to understand why the performance should be evaluated, how the data should be handle for evaluating the performance and how the performance of the classification problems are evaluated.
Introduction to Performance Evaluation - Classification Metrics
Confusion Matrix8:41
This lecture explains the confusion matrix and it's related terminologies. It also explain the different types of errors and why we should focus on them individually.
Confusion Matrix
Main Classification Metrics9:08
The Accuracy, Precision, Recall and F1 are the main classification metrics and they are briefly discussed here.
Main Classification Metrics
Performance Evaluation - Regression8:59
This topic is about the performance evaluation of regression problems which includes the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error.
Performance Evaluation - Regression
Introduction to Sklearn3:37
This gives a basic understanding of what scikit-learn libraries are and how we use them.
One Hot encoding7:27
This gives a basic idea about the one hot encoding, what happens underneath when we perform the one hot encoding.
Split the Data5:32
We know that we should split the data because we need to evaluate the model's performance. But what happens when we split the data using the scikit-learn train test split library?
What is Fit?3:32
Whether building a model or performing a pre-processing activity, we'll be fitting the data all the time and I think we should know what exactly happens when we fit the data before actually do the fitting. This explains it well.

Linear Regression Theory9:53
Explore linear regression theory, from simple to multiple regression, and learn to fit a regression line with the least squares method to predict the dependent variable from the independent variable.
Linear Regression - Theory
Linear Regression - Salary Prediction - Practical - Part 119:17
Linear Regression - Salary Prediction - Practical - Part 218:28
Linear Regression - House Price Prediction - Practical - Part 120:06
Linear Regression - House Price Prediction - Practical - Part 214:45
Define X as all features except property value and y as value, then perform a train-test split. Scale after splitting, train linear regression model, and evaluate with MAE, MSE, RMSE.
Linear Regression - Practical

Logistic Regression - Theory8:35
Logistic Regression - Theory
Logistic Regression - Iris Flower - Practical14:09
Logistic Regression - Gender Classification - Exercise Overview3:31
Study logistic regression for gender classification, covering data import, EDA with heat map, missing values, and feature engineering, then train the model and evaluate with confusion matrix and classification report.
Logistic Regression - Exercise Solution - Gender Classification - Part 115:02
Logistic Regression - Exercise Solution - Gender Classification - Part 28:33

Lasso and Ridge Regression - Theory14:45
Lasso and Ridge Regression - Theory
Lasso and Ridge Regression - Melbourne Housing - Practice - Part 128:28
Master L1 and L2 regularization with lasso and ridge regression on the Melbourne housing dataset, focusing on data cleaning, handling missing values, and creating dummy variables.
Lasso and Ridge Regression - Melbourne Housing - Practice - Part 216:21
Lasso and Ridge Regression - Melbourne Housing - Practice - Part 312:17
Lasso and Ridge - Insurance - Exercise overview4:17
Lasso and Ridge - Insurance - Solution to the Exercise17:50

Gaussian Naïve Bayes Classifier - Theory15:32
Gaussian Naïve Bayes Classifier
Gaussian Naïve Bayes Classifier - Titanic - Practical - Part 115:44
Apply Gaussian naive bayes to Titanic data, perform exploratory data analysis, drop cabins with many missing values, and impute age by passenger class means before building a survivor predictor.
Gaussian Naïve Bayes Classifier - Titanic - Practical - Part 216:21

Random Forest - Theory11:31
Random Forest - Theory
Random Forest - Practical - Bike Sharing - Part 120:44
Random Forest - Practical - Bike Sharing - Part 221:42
Random Forest - WeatherAUS - Exercise Overview2:26
Random Forest - weatherAUS - Solution Part 120:02
Random Forest - weatherAUS - Solution Part 217:57
Clean weatherAUS data, visualize correlations with a heat map, create dummy variables for categorical features, and train a random forest classifier while addressing imbalance and tuning with grid search.
Extra Tree - Theory2:49

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 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 going to answer the most important questions. 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.

Python for Mastering Machine Learning and Data Science

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 39min

Python Basics - Starter Kit19 lectures • 4hr 4min

Introduction to Machine Learning10 lectures • 1hr 12min

Linear Regression5 lectures • 1hr 22min

Logistic Regression5 lectures • 50min

Lasso and Ridge Regression / Regularizations6 lectures • 1hr 34min

Dealing with Practical Issues7 lectures • 1hr 37min

Naïve Bayes Classifier (Gaussian)3 lectures • 48min

Decision Trees4 lectures • 42min

Random Forest7 lectures • 1hr 37min

Requirements

Description

Who this course is for: