Complete Data Science & Machine Learning A-Z With Python

Machine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, Kaggle

What you'll learn

Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries.

Learn Machine Learning with Hands-On Examples

What is Machine Learning?

Machine Learning Terminology

Evaluation Metrics

What are Classification vs Regression?

Evaluating Performance-Classification Error Metrics

Evaluating Performance-Regression Error Metrics

Supervised Learning

Cross Validation and Bias Variance Trade-Off

Use matplotlib and seaborn for data visualizations

Machine Learning with SciKit Learn

Linear Regression Algorithm

Logistic Regresion Algorithm

K Nearest Neighbors Algorithm

Decision Trees And Random Forest Algorithm

Support Vector Machine Algorithm

Unsupervised Learning

K Means Clustering Algorithm

Hierarchical Clustering Algorithm

Principal Component Analysis (PCA)

Recommender System Algorithm

Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.

Python is a general-purpose, object-oriented, high-level programming language.

Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles

Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language

Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks.

Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website.

Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar

Machine learning describes systems that make predictions using a model trained on real-world data.

Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.

It's possible to use machine learning without coding, but building new systems generally requires code.

Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together.

Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning.

Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.

Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine"

A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science.

Python machine learning, complete machine learning, machine learning a-z

Requirements

Basic knowledge of Python Programming Language

Be Able To Operate & Install Software On A Computer

Free software and tools used during the machine learning a-z course

Motivation to learn the the second largest number of job postings relative program language among all others

Data visualization libraries in python such as seaborn, matplotlib

Desire to learn Python and machine learning python

Desire to work on python machine learning

Desire to learn pandas

Desire to learn numpy

Any device you can watch the course, such as a mobile phone, computer or tablet.

Watching the lecture videos completely, to the end and in order.

Nothing else! It’s just you, your computer and your ambition to get started today.

LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device.

Description

Hello there,Welcome to the " Complete Data Science & Machine Learning A-Z with Python " CourseMachine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, KaggleMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data sciencePandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. Pandas is built on top of another package named Numpy, which provides support for multi-dimensional arrays.Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. data analysis, pandas, numpy, numpy stack, numpy python, python data analysis, python, Python numpy, data visualization, pandas python, python pandas, python for data analysis, python data, data visualization.Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack.Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Do you know data science needs will create 11.5 million job openings by 2026?Do you know the average salary is $100.000 for data science careers!Data Science Careers Are Shaping The FutureData science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.If you want to learn one of the employer’s most request skills?If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?If you are an experienced developer and looking for a landing in Data Science!In all cases, you are at the right place!We've designed for you “Machine Learning & Data Science with Python & Kaggle | A-Z” a straightforward course for Python Programming Language and Machine Learning.In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.Also you will get to know the Kaggle platform step by step with hearth attack prediction kaggle project.Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community-published data & code.Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detecting cancer cells. Kaggle has a massive community of data scientists who are always willing to help others with their data science problems.You will learn the Numpy and Pandas Python Programming Language libraries step by step.Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. You will learn data analysis and visualization in detail.Data visualization is the graphical representation of information and data. It is a storytelling tool that provides a way to communicate the meaning behind a data set. Simply put, data visualization helps users — the individuals or teams who generate the data, and in many cases, their audience — make sense of data and make the best data-driven decisionsStatistics alone can fall flat. That’s why data visualization is so important to communicating the meaning behind data sets. Good visualizations can magically transform complex data analysis into appealing and easily understood representations that in turn inform smarter, more calculated business moves.Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.This Machine Learning course is for everyone!If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).What is machine learning?Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.Why we use a Python programming language in Machine learning?Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.What is machine learning used for?Machine learning a-z is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.Does Machine learning require coding?It's possible to use machine learning data science without coding, but building new systems generally requires code. For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from itWhat is the best language for machine learning?Python is the most used language in machine learning using python. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a complete machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets.What is data visualization? Data visualization is the graphical representation of information and data. It is a storytelling tool that provides a way to communicate the meaning behind a data set. Simply put, data visualization helps users — the individuals or teams who generate the data, and in many cases, their audience — make sense of data and make the best data-driven decisions. Good visualizations can magically transform complex data analysis into appealing and easily understood representations that, in turn, inform smarter, more calculated business moves. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.What is a Kaggle?Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.How does Kaggle work?Every competition on Kaggle has a dataset associated with it and a goal you must reach (i.e., predict housing prices or detect cancer cells). You can access the data as often as possible and build your prediction model. Still, once you submit your solution, you cannot use it to make future submissions.This ensures that everyone is starting from the same point when competing against one another, so there are no advantages given to those with more computational power than others trying to solve the problem.Competitions are separated into different categories depending on their complexity level, how long they take, whether or not prize money is involved, etc., so users with varying experience levels can compete against each other in the same arena.What is a Pandas in Python?Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays.What is Pandas used for?Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.What is difference between NumPy and pandas?NumPy library provides objects for multi-dimensional arrays, whereas Pandas is capable of offering an in-memory 2d table object called DataFrame. NumPy consumes less memory as compared to Pandas. Indexing of the Series objects is quite slow as compared to NumPy arrays.What are the different types of machine learning?Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled 'spam' or 'not spam.' That trained model could then identify new spam emails even from data it's never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within.Is Machine learning a good career?Machine learning python is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience.What is the difference between machine learning and artifical intelligence?Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine" that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward "true artificial intelligence" and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly.What skills should a machine learning engineer know?A python machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field.What is data science?We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.Why would you want to take this course?Our answer is simple: The quality of teaching.OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading.When you enroll, you will feel the OAK Academy`s seasoned developers' expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.Video and Audio Production QualityAll our videos are created/produced as high-quality video and audio to provide you the best learning experience.You will be,Seeing clearlyHearing clearlyMoving through the course without distractionsYou'll also get:Lifetime Access to The CourseFast & Friendly Support in the Q&A sectionUdemy Certificate of Completion Ready for DownloadWe offer full support, answering any questions.If you are ready to learn Dive in now into the " Complete Data Science & Machine Learning A-Z with Python" CourseMachine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, KaggleSee you in the course!

Overview

Section 1: Installations

Lecture 1 Installing Anaconda Distribution for Windows

Lecture 2 Notebook Project Files Link regarding NumPy Python Programming Language Library

Lecture 3 Installing Anaconda Distribution for MacOs

Lecture 4 6 Article Advice And Links about Numpy, Numpy Pyhon

Lecture 5 Installing Anaconda Distribution for Linux

Section 2: NumPy Library Introduction

Lecture 6 Introduction to NumPy Library

Lecture 7 The Power of NumPy

Section 3: Creating NumPy Array in Python

Lecture 8 Creating NumPy Array with The Array() Function

Lecture 9 Creating NumPy Array with Zeros() Function

Lecture 10 Creating NumPy Array with Ones() Function

Lecture 11 Creating NumPy Array with Full() Function

Lecture 12 Creating NumPy Array with Arange() Function

Lecture 13 Creating NumPy Array with Eye() Function

Lecture 14 Creating NumPy Array with Linspace() Function

Lecture 15 Creating NumPy Array with Random() Function

Lecture 16 Properties of NumPy Array

Section 4: Functions in the NumPy Library

Lecture 17 Reshaping a NumPy Array: Reshape() Function

Lecture 18 Identifying the Largest Element of a Numpy Array

Lecture 19 Detecting Least Element of Numpy Array: Min(), Ar

Lecture 20 Concatenating Numpy Arrays: Concatenate() Functio

Lecture 21 Splitting One-Dimensional Numpy Arrays: The Split

Lecture 22 Splitting Two-Dimensional Numpy Arrays: Split(),

Lecture 23 Sorting Numpy Arrays: Sort() Function

Section 5: Indexing, Slicing, and Assigning NumPy Arrays

Lecture 24 Indexing Numpy Arrays,

Lecture 25 Slicing One-Dimensional Numpy Arrays

Lecture 26 Slicing Two-Dimensional Numpy Arrays

Lecture 27 Assigning Value to One-Dimensional Arrays

Lecture 28 Assigning Value to Two-Dimensional Array

Lecture 29 Fancy Indexing of One-Dimensional Arrrays

Lecture 30 Fancy Indexing of Two-Dimensional Arrrays

Lecture 31 Combining Fancy Index with Normal Indexing

Lecture 32 Combining Fancy Index with Normal Slicing

Section 6: Operations in Numpy Library

Lecture 33 Operations with Comparison Operators

Lecture 34 Arithmetic Operations in Numpy

Lecture 35 Statistical Operations in Numpy

Lecture 36 Solving Second-Degree Equations with NumPy

Section 7: Pandas Library Introduction

Lecture 37 Introduction to Pandas Library

Lecture 38 Pandas Project Files Link

Section 8: Series Structures in the Pandas Library

Lecture 39 Creating a Pandas Series with a List

Lecture 40 Creating a Pandas Series with a Dictionary

Lecture 41 Creating Pandas Series with NumPy Array

Lecture 42 Object Types in Series

Lecture 43 Examining the Primary Features of the Pandas Seri

Lecture 44 Most Applied Methods on Pandas Series

Lecture 45 Indexing and Slicing Pandas Series

Section 9: DataFrame Structures in Pandas Library

Lecture 46 Creating Pandas DataFrame with List

Lecture 47 Creating Pandas DataFrame with NumPy Array

Lecture 48 Creating Pandas DataFrame with Dictionary

Lecture 49 Examining the Properties of Pandas DataFrames

Section 10: Element Selection Operations in DataFrame Structures

Lecture 50 Element Selection Operations in Pandas DataFrames: Lesson 1

Lecture 51 Element Selection Operations in Pandas DataFrames: Lesson 2

Lecture 52 Top Level Element Selection in Pandas DataFrames:Lesson 1

Lecture 53 Top Level Element Selection in Pandas DataFrames:Lesson 2

Lecture 54 Top Level Element Selection in Pandas DataFrames:Lesson 3

Lecture 55 Element Selection with Conditional Operations in

Section 11: Structural Operations on Pandas DataFrame

Lecture 56 Adding Columns to Pandas Data Frames

Lecture 57 Removing Rows and Columns from Pandas Data frames

Lecture 58 Null Values in Pandas Dataframes

Lecture 59 Dropping Null Values: Dropna() Function

Lecture 60 Filling Null Values: Fillna() Function

Lecture 61 Setting Index in Pandas DataFrames

Section 12: Multi-Indexed DataFrame Structures

Lecture 62 Multi-Index and Index Hierarchy in Pandas DataFrames

Lecture 63 Element Selection in Multi-Indexed DataFrames

Lecture 64 Selecting Elements Using the xs() Function in Multi-Indexed DataFrames

Section 13: Structural Concatenation Operations in Pandas DataFrame

Lecture 65 Concatenating Pandas Dataframes: Concat Function

Lecture 66 Merge Pandas Dataframes: Merge() Function: Lesson 1

Lecture 67 Merge Pandas Dataframes: Merge() Function: Lesson 2

Lecture 68 Merge Pandas Dataframes: Merge() Function: Lesson 3

Lecture 69 Merge Pandas Dataframes: Merge() Function: Lesson 4

Lecture 70 Joining Pandas Dataframes: Join() Function

Section 14: Functions That Can Be Applied on a DataFrame

Lecture 71 Loading a Dataset from the Seaborn Library

Lecture 72 Examining the Data Set 1

Lecture 73 Aggregation Functions in Pandas DataFrames

Lecture 74 Examining the Data Set 2

Lecture 75 Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes

Lecture 76 Advanced Aggregation Functions: Aggregate() Function

Lecture 77 Advanced Aggregation Functions: Filter() Function

Lecture 78 Advanced Aggregation Functions: Transform() Function

Lecture 79 Advanced Aggregation Functions: Apply() Function

Section 15: Pivot Tables in Pandas Library

Lecture 80 Examining the Data Set 3

Lecture 81 Pivot Tables in Pandas Library

Section 16: File Operations in Pandas Library

Lecture 82 Accessing and Making Files Available

Lecture 83 Data Entry with Csv and Txt Files

Lecture 84 Data Entry with Excel Files

Lecture 85 Outputting as an CSV Extension

Lecture 86 Outputting as an Excel File

Section 17: Code Files And Resources: Python data analysis and visualization

Lecture 87 Data Visualisation - Matplotlib Files

Lecture 88 Data Visualisation - Seaborn Files

Lecture 89 Data Visualisation - Geoplotlib

Section 18: Introduction to Data Visualization with Python

Lecture 90 Introduction to Data Visualization with Python

Lecture 91 FAQ regarding Data Visualization, Python

Section 19: Fundamentals of Python 3

Lecture 92 Data Types in Python

Lecture 93 Operators in Python

Lecture 94 Conditionals in Python

Lecture 95 Loops in Python

Lecture 96 Lists, Tuples, Dictionaries and Sets in pyhton

Lecture 97 Data Type Operators and Methods in Python

Lecture 98 Modules in Python

Lecture 99 Functions in Python

Lecture 100 Exercise - Analyse in Python

Lecture 101 Exercise - Solution in Python

Section 20: Object Oriented Programming (OOP)

Lecture 102 Logic of Object Oriented Programming

Lecture 103 Constructor in Object Oriented Programming (OOP)

Lecture 104 Methods in Object Oriented Programming (OOP)

Lecture 105 Inheritance in Object Oriented Programming (OOP)

Lecture 106 Overriding and Overloading in Object Oriented Programming (OOP)

Section 21: Matplotlib

Lecture 107 What is Matplotlib

Lecture 108 Using Pyplot

Lecture 109 Pyplot – Pylab - Matplotlib

Lecture 110 Figure, Subplot and Axex

Lecture 111 Figure Customization

Lecture 112 Plot Customization

Lecture 113 Grid, Spines, Ticks

Lecture 114 Basic Plots in Matplotlib I

Lecture 115 Basic Plots in Matplotlib II

Section 22: Seaborn

Lecture 116 What is Seaborn?

Lecture 117 Controlling Figure Aesthetics in Seaborn

Lecture 118 Example in Seaborn

Lecture 119 Color Palettes in Seaborn

Lecture 120 Basic Plots in Seaborn

Lecture 121 Multi-Plots in Seaborn

Lecture 122 Regression Plots and Squarify in Seaborn

Section 23: Geoplotlib

Lecture 123 What is Geoplotlib?

Lecture 124 Example - 1

Lecture 125 Example - 2

Lecture 126 Example - 3

Section 24: First Contact with Machine Learning

Lecture 127 What is Machine Learning?

Lecture 128 Machine Learning Terminology

Lecture 129 Machine Learning: Project Files

Lecture 130 FAQ regarding Python

Lecture 131 FAQ regarding Machine Learning

Section 25: Evaluation Metrics in Machine Learning

Lecture 132 Classification vs Regression in Machine Learning

Lecture 133 Machine Learning Model Performance Evaluation: Classification Error Metrics

Lecture 134 Evaluating Performance: Regression Error Metrics in Python

Lecture 135 Machine Learning With Python

Section 26: Supervised Learning with Machine Learning

Lecture 136 What is Supervised Learning in Machine Learning?

Section 27: Linear Regression Algorithm in Machine Learning A-Z

Lecture 137 Linear Regression Algorithm Theory in Machine Learning A-Z

Lecture 138 Linear Regression Algorithm With Python Part 1

Lecture 139 Linear Regression Algorithm With Python Part 2

Lecture 140 Linear Regression Algorithm With Python Part 3

Lecture 141 Linear Regression Algorithm With Python Part 4

Section 28: Bias Variance Trade-Off in Machine Learning

Lecture 142 What is Bias Variance Trade-Off?

Section 29: Logistic Regression Algorithm in Machine Learning A-Z

Lecture 143 What is Logistic Regression Algorithm in Machine Learning?

Lecture 144 Logistic Regression Algorithm with Python Part 1

Lecture 145 Logistic Regression Algorithm with Python Part 2

Lecture 146 Logistic Regression Algorithm with Python Part 3

Lecture 147 Logistic Regression Algorithm with Python Part 4

Lecture 148 Logistic Regression Algorithm with Python Part 5

Section 30: K-fold Cross-Validation in Machine Learning A-Z

Lecture 149 K-Fold Cross-Validation Theory

Lecture 150 K-Fold Cross-Validation with Python

Section 31: K Nearest Neighbors Algorithm in Machine Learning A-Z

Lecture 151 K Nearest Neighbors Algorithm Theory

Lecture 152 K Nearest Neighbors Algorithm with Python Part 1

Lecture 153 K Nearest Neighbors Algorithm with Python Part 2

Lecture 154 K Nearest Neighbors Algorithm with Python Part 3

Section 32: Hyperparameter Optimization

Lecture 155 Hyperparameter Optimization Theory

Lecture 156 Hyperparameter Optimization with Python

Section 33: Decision Tree Algorithm in Machine Learning A-Z

Lecture 157 Decision Tree Algorithm Theory

Lecture 158 Decision Tree Algorithm with Python Part 1

Lecture 159 Decision Tree Algorithm with Python Part 2

Lecture 160 Decision Tree Algorithm with Python Part 3

Lecture 161 Decision Tree Algorithm with Python Part 4

Lecture 162 Decision Tree Algorithm with Python Part 5

Section 34: Random Forest Algorithm in Machine Learning A-Z

Lecture 163 Random Forest Algorithm Theory

Lecture 164 Random Forest Algorithm with Pyhon Part 1

Lecture 165 Random Forest Algorithm with Pyhon Part 2

Section 35: Support Vector Machine Algorithm in Machine Learning A-Z

Lecture 166 Support Vector Machine Algorithm Theory

Lecture 167 Support Vector Machine Algorithm with Python Part 1

Lecture 168 Support Vector Machine Algorithm with Python Part 2

Lecture 169 Support Vector Machine Algorithm with Python Part 3

Lecture 170 Support Vector Machine Algorithm with Python Part 4

Section 36: Unsupervised Learning with Machine Learning

Lecture 171 Unsupervised Learning Overview

Section 37: K Means Clustering Algorithm in Machine Learning A-Z

Lecture 172 K Means Clustering Algorithm Theory

Lecture 173 K Means Clustering Algorithm with Python Part 1

Lecture 174 K Means Clustering Algorithm with Python Part 2

Lecture 175 K Means Clustering Algorithm with Python Part 3

Lecture 176 K Means Clustering Algorithm with Python Part 4

Section 38: Hierarchical Clustering Algorithm in machine learning data science

Lecture 177 Hierarchical Clustering Algorithm Theory

Lecture 178 Hierarchical Clustering Algorithm with Python Part 2

Lecture 179 Hierarchical Clustering Algorithm with Python Part 2

Section 39: Principal Component Analysis (PCA) in Machine Learning A-Z

Lecture 180 Principal Component Analysis (PCA) Theory

Lecture 181 Principal Component Analysis (PCA) with Python Part 1

Lecture 182 Principal Component Analysis (PCA) with Python Part 2

Lecture 183 Principal Component Analysis (PCA) with Python Part 3

Section 40: Recommender System Algorithm in Machine Learning A-Z

Lecture 184 What is the Recommender System? Part 1

Lecture 185 What is the Recommender System? Part 2

Section 41: First Contact with Kaggle

Lecture 186 What is Kaggle?

Lecture 187 FAQ about Kaggle

Lecture 188 Registering on Kaggle and Member Login Procedures

Lecture 189 Project Link File - Hearth Attack Prediction Project, Machine Learning

Lecture 190 Getting to Know the Kaggle Homepage

Section 42: Competition Section on Kaggle

Lecture 191 Competitions on Kaggle: Lesson 1

Lecture 192 Competitions on Kaggle: Lesson 2

Section 43: Dataset Section on Kaggle

Lecture 193 Datasets on Kaggle

Section 44: Code Section on Kaggle

Lecture 194 Examining the Code Section in Kaggle: Lesson 1

Lecture 195 Examining the Code Section in Kaggle: Lesson 2

Lecture 196 Examining the Code Section in Kaggle: Lesson 3

Section 45: Discussion Section on Kaggle

Lecture 197 What is Discussion on Kaggle?

Section 46: Other Most Used Options on Kaggle

Lecture 198 Courses in Kaggle

Lecture 199 Ranking Among Users on Kaggle

Lecture 200 Blog and Documentation Sections

Section 47: Details on Kaggle

Lecture 201 User Page Review on Kaggle

Lecture 202 Treasure in The Kaggle

Lecture 203 Publishing Notebooks on Kaggle

Lecture 204 What Should Be Done to Achieve Success in Kaggle?

Section 48: Introduction to Machine Learning with Real Hearth Attack Prediction Project

Lecture 205 First Step to the Hearth Attack Prediction Project

Lecture 206 FAQ about Machine Learning, Data Science

Lecture 207 Notebook Design to be Used in the Project

Lecture 208 Project Link File - Hearth Attack Prediction Project, Machine Learning

Lecture 209 Examining the Project Topic

Lecture 210 Recognizing Variables In Dataset

Section 49: First Organization

Lecture 211 Required Python Libraries

Lecture 212 Loading the Statistics Dataset in Data Science

Lecture 213 Initial analysis on the dataset

Section 50: Preparation For Exploratory Data Analysis (EDA) in Data Science

Lecture 214 Examining Missing Values

Lecture 215 Examining Unique Values

Lecture 216 Separating variables (Numeric or Categorical)

Lecture 217 Examining Statistics of Variables

Section 51: Exploratory Data Analysis (EDA) - Uni-variate Analysis

Lecture 218 Numeric Variables (Analysis with Distplot): Lesson 1

Lecture 219 Numeric Variables (Analysis with Distplot): Lesson 2

Lecture 220 Categoric Variables (Analysis with Pie Chart): Lesson 1

Lecture 221 Categoric Variables (Analysis with Pie Chart): Lesson 2

Lecture 222 Examining the Missing Data According to the Analysis Result

Section 52: Exploratory Data Analysis (EDA) - Bi-variate Analysis

Lecture 223 Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1

Lecture 224 Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2

Lecture 225 Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1

Lecture 226 Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2

Lecture 227 Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1

Lecture 228 Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2

Lecture 229 Feature Scaling with the Robust Scaler Method

Lecture 230 Creating a New DataFrame with the Melt() Function

Lecture 231 Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 1

Lecture 232 Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 2

Lecture 233 Numerical - Categorical Variables (Analysis with Box Plot): Lesson 1

Lecture 234 Numerical - Categorical Variables (Analysis with Box Plot): Lesson 2

Lecture 235 Relationships between variables (Analysis with Heatmap): Lesson 1

Lecture 236 Relationships between variables (Analysis with Heatmap): Lesson 2

Section 53: Preparation for Modelling in Machine Learning

Lecture 237 Dropping Columns with Low Correlation

Lecture 238 Visualizing Outliers

Lecture 239 Dealing with Outliers – Trtbps Variable: Lesson 1

Lecture 240 Dealing with Outliers – Trtbps Variable: Lesson 2

Lecture 241 Dealing with Outliers – Thalach Variable

Lecture 242 Dealing with Outliers – Oldpeak Variable

Lecture 243 Determining Distributions of Numeric Variables

Lecture 244 Transformation Operations on Unsymmetrical Data

Lecture 245 Applying One Hot Encoding Method to Categorical Variables

Lecture 246 Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms

Lecture 247 Separating Data into Test and Training Set

Section 54: Modelling for Machine Learning

Lecture 248 Logistic Regression

Lecture 249 Cross Validation

Lecture 250 Roc Curve and Area Under Curve (AUC)

Lecture 251 Hyperparameter Optimization (with GridSearchCV)

Lecture 252 Decision Tree Algorithm

Lecture 253 Support Vector Machine Algorithm

Lecture 254 Random Forest Algorithm

Lecture 255 Hyperparameter Optimization (with GridSearchCV)

Section 55: Conclusion

Lecture 256 Project Conclusion and Sharing

Section 56: Extra

Lecture 257 Complete Data Science & Machine Learning A-Z with Python

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