Machine Learning & Data Science With Python, Kaggle & Pandas
Published 4/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 9.01 GB | Duration: 29h 39m
Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples
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
Determination to learn machine learning and patience.
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
Curiosity for machine learning python
Desire to learn Python
Desire to learn matplotlib
Desire to learn pandas and numpy
Desire to learn machine learning a-z, complete machine learning
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 " Machine Learning & Data Science with Python, Kaggle & Pandas " CourseMachine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examplesMachine 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.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 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 " Machine Learning & Data Science with Python, Kaggle & Pandas " CourseMachine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examplesSee 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() Function
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: First Contact with Machine Learning
Lecture 87 What is Machine Learning?
Lecture 88 Machine Learning Terminology
Lecture 89 Machine Learning: Project Files
Lecture 90 FAQ regarding Python
Lecture 91 FAQ regarding Machine Learning
Section 18: Evaluation Metrics in Machine Learning
Lecture 92 Classification vs Regression in Machine Learning
Lecture 93 Machine Learning Model Performance Evaluation: Classification Error Metrics
Lecture 94 Evaluating Performance: Regression Error Metrics in Python
Lecture 95 Machine Learning With Python
Section 19: Supervised Learning with Machine Learning
Lecture 96 What is Supervised Learning in Machine Learning?
Section 20: Linear Regression Algorithm in Machine Learning A-Z
Lecture 97 Linear Regression Algorithm Theory in Machine Learning A-Z
Lecture 98 Linear Regression Algorithm With Python Part 1
Lecture 99 Linear Regression Algorithm With Python Part 2
Lecture 100 Linear Regression Algorithm With Python Part 3
Lecture 101 Linear Regression Algorithm With Python Part 4
Section 21: Bias Variance Trade-Off in Machine Learning
Lecture 102 What is Bias Variance Trade-Off?
Section 22: Logistic Regression Algorithm in Machine Learning A-Z
Lecture 103 What is Logistic Regression Algorithm in Machine Learning?
Lecture 104 Logistic Regression Algorithm with Python Part 1
Lecture 105 Logistic Regression Algorithm with Python Part 2
Lecture 106 Logistic Regression Algorithm with Python Part 3
Lecture 107 Logistic Regression Algorithm with Python Part 4
Lecture 108 Logistic Regression Algorithm with Python Part 5
Section 23: K-fold Cross-Validation in Machine Learning A-Z
Lecture 109 K-Fold Cross-Validation Theory
Lecture 110 K-Fold Cross-Validation with Python
Section 24: K Nearest Neighbors Algorithm in Machine Learning A-Z
Lecture 111 K Nearest Neighbors Algorithm Theory
Lecture 112 K Nearest Neighbors Algorithm with Python Part 1
Lecture 113 K Nearest Neighbors Algorithm with Python Part 2
Lecture 114 K Nearest Neighbors Algorithm with Python Part 3
Section 25: Hyperparameter Optimization
Lecture 115 Hyperparameter Optimization Theory
Lecture 116 Hyperparameter Optimization with Python
Section 26: Decision Tree Algorithm in Machine Learning A-Z
Lecture 117 Decision Tree Algorithm Theory
Lecture 118 Decision Tree Algorithm with Python Part 1
Lecture 119 Decision Tree Algorithm with Python Part 2
Lecture 120 Decision Tree Algorithm with Python Part 3
Lecture 121 Decision Tree Algorithm with Python Part 4
Lecture 122 Decision Tree Algorithm with Python Part 5
Section 27: Random Forest Algorithm in Machine Learning A-Z
Lecture 123 Random Forest Algorithm Theory
Lecture 124 Random Forest Algorithm with Pyhon Part 1
Lecture 125 Random Forest Algorithm with Pyhon Part 2
Section 28: Support Vector Machine Algorithm in Machine Learning A-Z
Lecture 126 Support Vector Machine Algorithm Theory
Lecture 127 Support Vector Machine Algorithm with Python Part 1
Lecture 128 Support Vector Machine Algorithm with Python Part 2
Lecture 129 Support Vector Machine Algorithm with Python Part 3
Lecture 130 Support Vector Machine Algorithm with Python Part 4
Section 29: Unsupervised Learning with Machine Learning
Lecture 131 Unsupervised Learning Overview
Section 30: K Means Clustering Algorithm in Machine Learning A-Z
Lecture 132 K Means Clustering Algorithm Theory
Lecture 133 K Means Clustering Algorithm with Python Part 1
Lecture 134 K Means Clustering Algorithm with Python Part 2
Lecture 135 K Means Clustering Algorithm with Python Part 3
Lecture 136 K Means Clustering Algorithm with Python Part 4
Section 31: Hierarchical Clustering Algorithm in machine learning data science
Lecture 137 Hierarchical Clustering Algorithm Theory
Lecture 138 Hierarchical Clustering Algorithm with Python Part 1
Lecture 139 Hierarchical Clustering Algorithm with Python Part 2
Section 32: Principal Component Analysis (PCA) in Machine Learning A-Z
Lecture 140 Principal Component Analysis (PCA) Theory
Lecture 141 Principal Component Analysis (PCA) with Python Part 1
Lecture 142 Principal Component Analysis (PCA) with Python Part 2
Lecture 143 Principal Component Analysis (PCA) with Python Part 3
Section 33: Recommender System Algorithm in Machine Learning A-Z
Lecture 144 What is the Recommender System? Part 1
Lecture 145 What is the Recommender System? Part 2
Section 34: First Contact with Kaggle
Lecture 146 What is Kaggle?
Lecture 147 FAQ about Kaggle
Lecture 148 Registering on Kaggle and Member Login Procedures
Lecture 149 Project Link File – Hearth Attack Prediction Project, Machine Learning
Lecture 150 Getting to Know the Kaggle Homepage
Section 35: Competition Section on Kaggle
Lecture 151 Competitions on Kaggle: Lesson 1
Lecture 152 Competitions on Kaggle: Lesson 2
Section 36: Dataset Section on Kaggle
Lecture 153 Datasets on Kaggle
Section 37: Code Section on Kaggle
Lecture 154 Examining the Code Section in Kaggle: Lesson 1
Lecture 155 Examining the Code Section in Kaggle Lesson 2
Lecture 156 Examining the Code Section in Kaggle Lesson 3
Section 38: Discussion Section on Kaggle
Lecture 157 What is Discussion on Kaggle?
Section 39: Other Most Used Options on Kaggle
Lecture 158 Courses in Kaggle
Lecture 159 Ranking Among Users on Kaggle
Lecture 160 Blog and Documentation Sections
Section 40: Details on Kaggle
Lecture 161 User Page Review on Kaggle
Lecture 162 Treasure in The Kaggle
Lecture 163 Publishing Notebooks on Kaggle
Lecture 164 What Should Be Done to Achieve Success in Kaggle?
Section 41: Introduction to Machine Learning with Real Hearth Attack Prediction Project
Lecture 165 First Step to the Project
Lecture 166 FAQ about Machine Learning, Data Science
Lecture 167 Notebook Design to be Used in the Project
Lecture 168 Project Link File – Hearth Attack Prediction Project, Machine Learning
Lecture 169 Examining the Project Topic
Lecture 170 Recognizing Variables In Dataset
Section 42: First Organization
Lecture 171 Required Python Libraries
Lecture 172 Loading the Dataset
Lecture 173 Initial analysis on the dataset
Section 43: Preparation For Exploratory Data Analysis (EDA)
Lecture 174 Examining Missing Values
Lecture 175 Examining Unique Values
Lecture 176 Separating variables (Numeric or Categorical)
Lecture 177 Examining Statistics of Variables
Section 44: Exploratory Data Analysis (EDA) – Uni-variate Analysis
Lecture 178 Numeric Variables (Analysis with Distplot): Lesson 1
Lecture 179 Numeric Variables (Analysis with Distplot): Lesson 2
Lecture 180 Categoric Variables (Analysis with Pie Chart): Lesson 1
Lecture 181 Categoric Variables (Analysis with Pie Chart): Lesson 2
Lecture 182 Examining the Missing Data According to the Analysis Result
Section 45: Exploratory Data Analysis (EDA) – Bi-variate Analysis
Lecture 183 Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1
Lecture 184 Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2
Lecture 185 Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1
Lecture 186 Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2
Lecture 187 Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1
Lecture 188 Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2
Lecture 189 Feature Scaling with the Robust Scaler Method
Lecture 190 Creating a New DataFrame with the Melt() Function
Lecture 191 Numerical – Categorical Variables (Analysis with Swarm Plot): Lesson 1
Lecture 192 Numerical – Categorical Variables (Analysis with Swarm Plot): Lesson 2
Lecture 193 Numerical – Categorical Variables (Analysis with Box Plot): Lesson 1
Lecture 194 Numerical – Categorical Variables (Analysis with Box Plot): Lesson 2
Lecture 195 Relationships between variables (Analysis with Heatmap): Lesson 1
Lecture 196 Relationships between variables (Analysis with Heatmap): Lesson 2
Section 46: Preparation for Modelling in Machine Learning
Lecture 197 Dropping Columns with Low Correlation
Lecture 198 Visualizing Outliers
Lecture 199 Dealing with Outliers – Trtbps Variable: Lesson 1
Lecture 200 Dealing with Outliers – Trtbps Variable: Lesson 2
Lecture 201 Dealing with Outliers – Thalach Variable
Lecture 202 Dealing with Outliers – Oldpeak Variable
Lecture 203 Determining Distributions of Numeric Variables
Lecture 204 Transformation Operations on Unsymmetrical Data
Lecture 205 Applying One Hot Encoding Method to Categorical Variables
Lecture 206 Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms
Lecture 207 Separating Data into Test and Training Set
Section 47: Modelling for machine learning
Lecture 208 Logistic Regression
Lecture 209 Cross Validation
Lecture 210 Roc Curve and Area Under Curve (AUC)
Lecture 211 Hyperparameter Optimization (with GridSearchCV)
Lecture 212 Decision Tree Algorithm
Lecture 213 Support Vector Machine Algorithm
Lecture 214 Random Forest Algorithm
Lecture 215 Hyperparameter Optimization (with GridSearchCV)
Section 48: Conclusion
Lecture 216 Project Conclusion and Sharing
Section 49: Extra
Lecture 217 Machine Learning & Data Science with Kaggle, Pandas , Numpy
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Machine Learning A-Z AI, Python & R + ChatGPT Prize (8.09 GB)
ART 1: Univariate & Multivariate ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningPART 1: Univariate & Multivariate ProfilingIn Part 1 we’ll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We’ll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:Section 1: Machine Learning Intro & LandscapeMachine learning process, definition, and landscapeSection 2: Preliminary Data QAVariable types, empty values, range & count calculations, left/right censoring, etc.Section 3: Univariate ProfilingHistograms, frequency tables, mean, median, mode, variance, skewness, etc.Section 4: Multivariate ProfilingViolin & box plots, kernel densities, heat maps, correlation, etc.Throughout the course, we’ll introduce real-world scenarios to solidify key concepts and simulate actual data science and business intelligence cases. You’ll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and more.PART 2: Classification ModelingIn Part 2 we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting. From there we’ll review common classification models like K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization:Section 1: Intro to ClassificationSupervised learning & classification workflow, feature engineering, splitting, overfitting & underfittingSection 2: Classification ModelsK-nearest neighbors, naïve bayes, decision trees, random forests, logistic regression, sentiment analysisSection 3: Model Selection & TuningHyperparameter tuning, imbalanced classes, confusion matrices, accuracy, precision & recall, model driftYou’ll help build a simple recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for an online travel company, extract sentiment from a sample of book reviews, and more.PART 3: Regression & ForecastingIn Part 3 we’ll introduce core building blocks like linear relationships and least squared error, and practice applying them to univariate, multivariate, and non-linear regression models. We’ll review diagnostic metrics like R-squared, mean error, F-significance, and P-Values, then use time-series forecasting techniques to identify seasonality, predict nonlinear trends, and measure the impact of key business decisions using intervention analysis:Section 1: Intro to RegressionSupervised learning landscape, regression vs. classification, prediction vs. root-cause analysisSection 2: Regression Modeling 101Linear relationships, least squared error, univariate & multivariate regression, nonlinear transformationSection 3: Model DiagnosticsR-squared, mean error, null hypothesis, F-significance, T & P-values, homoskedasticity, multicollinearitySection 4: Time-Series ForecastingSeasonality, auto correlation, linear trending, non-linear models, intervention analysisYou’ll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.PART 4: Unsupervised LearningIn Part 4 we’ll explore the differences between supervised and unsupervised machine learning and introduce several common unsupervised techniques, including cluster analysis, association mining, outlier detection and dimensionality reduction. We’ll break down each model in simple terms and help you build an intuition for how they work, from K-means and apriori to outlier detection, principal component analysis, and more:Section 1: Intro to Unsupervised Machine LearningUnsupervised learning landscape & workflow, common unsupervised techniques, feature engineeringSection 2: Clustering & SegmentationClustering basics, K-means, elbow plots, hierarchical clustering, dendogramsSection 3: Association MiningAssociation mining basics, apriori, basket analysis, minimum support thresholds, markov chainsSection 4: Outlier DetectionOutlier detection basics, cross-sectional outliers, nearest neighbors, time-series outliers, residual distributionSection 5: Dimensionality ReductionDimensionality reduction basics, principle component analysis (PCA), scree plots, advanced techniquesYou’ll see how K-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:9+ hours of on-demand videoML Foundations ebook (350+ pages)Downloadable Excel project filesExpert Q&A forum30-day money-back guaranteeIf you’re an analyst or aspiring data professional looking to build the foundation for a successful career in machine learning or data science, you’ve come to the right place.Happy learning!-Josh & Chris__________Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!See why our courses are among the TOP-RATED on :"Some of the BEST courses I’ve ever taken. I’ve studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I’ve seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I’m running out of things to say. I wish Maven was in my life earlier!" 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