Python & Machine Learning Mastery For Data Science Warriors

Master Data Science and Machine Learning with Python: From Fundamentals to Practical Projects

What you'll learn

Python Foundations for Data Science: Master Python basics, data structures, and manipulation using NumPy and Pandas.

Data Visualization and Preprocessing: Explore data visualization libraries, conduct exploratory data analysis, and implement preprocessing methods.

Machine Learning Fundamentals: Understand core machine learning concepts, build, optimize, and validate models, and manage performance metrics.

Dive deep into Support Vector Machines (SVM), Random Forests, and Logistic Regression, understanding their implementations and applications.

Engage in real-world projects such as predicting home prices, email classification, car price forecasting, customer segmentation, and employee retention.

Requirements

No Prior knowledge is required at all.

Description

Welcome to our comprehensive course on data science and machine learning with Python. This course is designed to provide you with a structured and in-depth learning experience, encompassing seven distinct modules to help you become proficient in this dynamic field.Holistic Learning: From mastering the fundamentals of Python to delving into advanced machine learning techniques, you'll gain a deep understanding of every stage in data science.Practical Application: Engage in hands-on projects that simulate real-world scenarios, equipping you with practical skills that can be immediately applied.Career Readiness: Develop highly sought-after competencies in Python, data manipulation, visualization, and various machine learning algorithms, making you job-ready in the data science field.Course Outline:Introduction: Dive into an immersive exploration of Python's capabilities in data science and machine learning. This course integrates seven modules into a cohesive learning experience, guiding you from Python basics to advanced machine learning algorithms. Through practical projects, you'll discover how Python transforms theoretical knowledge into essential skills for data analysis and prediction.Module 1: Python Foundations for Data ScienceMaster Python basics and syntaxData structures and manipulation with NumPy and PandasHands-on projects for practical learningModule 2: Data Visualization and Preprocessing in PythonExplore data visualization librariesConduct exploratory data analysis techniquesImplement data preprocessing methods for machine learningModule 3: Machine Learning FundamentalsUnderstand core machine learning conceptsBuild, optimize, and validate modelsManage overfitting, generalization, and performance metricsModule 4: Specialized Topics in Machine LearningDeep dive into Support Vector Machines (SVM)Understand Random Forests: Implementation and ApplicationsExplore Logistic Regression: Theory and Practical ImplementationModule 5: Hands-On Machine Learning ProjectsProject 1: Predicting Home Prices Using Linear RegressionProject 2: Email Filtering with Naive Bayes ClassifierProject 3: Predicting Car Prices Using Neural NetworksProject 4: Customer Segmentation with K-MeansProject 5: Employee Retention Prediction with Logistic RegressionConclusion:Summarize key learnings across modulesEncourage further exploration and practiceHighlight opportunities and applications in data science and machine learningThis course promises a comprehensive exploration of Python for data science and machine learning, aiming to equip you with a blend of theoretical knowledge and practical skills essential for success in this dynamic field.Why This Course: Are you eager to embark on an exciting journey into the world of data science and machine learning with Python? This course offers the perfect blend of theory and hands-on experience, equipping you with the skills and knowledge necessary for a successful career in this rapidly evolving field. Whether you're a beginner looking to start your data science adventure or an aspiring professional seeking to enhance your expertise, this course is your gateway to mastering Python for data science and machine learning.Who Will Take This Course:Aspiring Data Scientists: If you're passionate about data and want to kickstart a career in data science, this course provides the foundation you need.Python Enthusiasts: If you're already familiar with Python and want to leverage it for data analysis and machine learning, this course is the perfect next step.Professionals Seeking Advancement: If you're working in data-related roles and want to upskill to stay competitive or transition into data science, this course is tailored to your needs.Anyone Curious About Data: If you're simply curious about the world of data science and want to explore its possibilities, this course welcomes learners of all backgrounds.What You Will Learn: Throughout this course, you will acquire a diverse set of skills and knowledge, including:Mastery of Python fundamentals, syntax, and data structures.Proficiency in data manipulation and preprocessing using NumPy and Pandas.Expertise in data visualization techniques to gain insights from data.A deep understanding of core machine learning concepts and algorithms.Hands-on experience in building, optimizing, and validating machine learning models.Knowledge of specialized topics such as Support Vector Machines, Random Forests, and Logistic Regression.Practical implementation skills through a series of real-world projects.The ability to predict home prices, filter emails, segment customers, and more using machine learning.A comprehensive grasp of data science and machine learning, making you job-ready in these fields.This course not only equips you with essential skills but also provides you with the confidence to tackle complex data science challenges. Whether you aspire to become a data scientist, analyst, or simply want to harness the power of data, this course is your key to success. Join us on this exciting journey into the world of data science and machine learning with Python!Keywords: Machine Learning Training, Python for Data Analysis, Data Visualization Tutorial, Machine Learning Algorithms, Python Programming for ML, Data Science Projects, Hands-On ML with Python, Data Manipulation Techniques, Python Data Science Certification, Data Science Python Course

Overview

Section 1: Python for Data Science and Data Analysis

Lecture 1 Link to the Python codes for the projects and the data

Lecture 2 Introduction: About the Tutor and AI Sciences

Lecture 3 Introduction: Introduction To Instructor

Lecture 4 Introduction: Focus of the Course-Part 1

Lecture 5 Introduction: Focus of the Course- Part 2

Lecture 6 Basics of Programming: Understanding the Algorithm

Lecture 7 Basics of Programming: FlowCharts and Pseudocodes

Lecture 8 Basics of Programming: Example of Algorithms- Making Tea Problem

Lecture 9 Basics of Programming: Example of Algorithms-Searching Minimun

Lecture 10 Basics of Programming: Example of Algorithms-Sorting Problem

Lecture 11 Basics of Programming: Example of Algorithms-Searching Minimun Quiz

Lecture 12 Basics of Programming: Example of Algorithms-Searching Minimun Solution

Lecture 13 Basics of Programming: Sorting Problem in Python

Lecture 14 Why Python and Jupyter Notebook: Why Python

Lecture 15 Why Python and Jupyter Notebook: Why Jupyter Notebooks

Lecture 16 Installation of Anaconda and IPython Shell: Installing Python and Jupyter AnaconDA

Lecture 17 Installation of Anaconda and IPython Shell: Your First Python Code- Hello World

Lecture 18 Installation of Anaconda and IPython Shell: Coding in IPython Shell

Lecture 19 Variable and Operator: Variables

Lecture 20 Variable and Operator: Operators

Lecture 21 Variable and Operator: Variable Name Quiz

Lecture 22 Variable and Operator: Bool Data Type in Python

Lecture 23 Variable and Operator: Comparison in Python

Lecture 24 Variable and Operator: Combining Comparisons in Python

Lecture 25 Variable and Operator: Combining Comparisons Quiz

Lecture 26 Python Useful function: Python Function- Round

Lecture 27 Python Useful function: Python Function- Round Quiz

Lecture 28 Python Useful function: Python Function- Round Solution

Lecture 29 Python Useful function: Python Function- Divmod

Lecture 30 Python Useful function: Python Function- Is instance and PowFunctions

Lecture 31 Python Useful function: Python Function- Input

Lecture 32 Control Flow in Python: If Python Condition

Lecture 33 Control Flow in Python: if Elif Else Python Conditions

Lecture 34 Control Flow in Python: if Elif Else Python Conditions Quiz

Lecture 35 Control Flow in Python: if Elif Else Python Conditions Solution

Lecture 36 Control Flow in Python: More on if Elif Else Python Conditions

Lecture 37 Control Flow in Python: More on if Elif Else Python Conditions Quiz

Lecture 38 Control Flow in Python: More on if Elif Else Python Conditions Solution

Lecture 39 Control Flow in Python: Indentations

Lecture 40 Control Flow in Python: Indentations Quiz

Lecture 41 Control Flow in Python: Indentations Solution

Lecture 42 Control Flow in Python: Comments and Problem Solving Practice With If

Lecture 43 Control Flow in Python: While Loop

Lecture 44 Control Flow in Python: While Loop break Continue

Lecture 45 Control Flow in Python: While Loop break Continue Quiz

Lecture 46 Control Flow in Python: While Loop break Continue Solution

Lecture 47 Control Flow in Python: For Loop

Lecture 48 Control Flow in Python: For Loop Quiz

Lecture 49 Control Flow in Python: For Loop Solution

Lecture 50 Control Flow in Python: Else In For Loop

Lecture 51 Control Flow in Python: Loops Practice-Sorting Problem

Lecture 52 Function and Module in Python: Functions in Python

Lecture 53 Function and Module in Python: DocString

Lecture 54 Function and Module in Python: Input Arguments

Lecture 55 Function and Module in Python: Multiple Input Arguments

Lecture 56 Function and Module in Python: Multiple Input Arguments Quiz

Lecture 57 Function and Module in Python: Multiple Input Arguments Solution

Lecture 58 Function and Module in Python: Ordering Multiple Input Arguments

Lecture 59 Function and Module in Python: Output Arguments and Return Statement

Lecture 60 Function and Module in Python: Function Practice-Output Arguments and Return Statement

Lecture 61 Function and Module in Python: Variable Number of Input Arguments

Lecture 62 Function and Module in Python: Variable Number of Input Arguments Quiz

Lecture 63 Function and Module in Python: Variable Number of Input Arguments Solution

Lecture 64 Function and Module in Python: Variable Number of Input Arguments as Dictionary

Lecture 65 Function and Module in Python: Variable Number of Input Arguments as Dictionary Quiz

Lecture 66 Function and Module in Python: Variable Number of Input Arguments as Dictionary Solution

Lecture 67 Function and Module in Python: Default Values in Python

Lecture 68 Function and Module in Python: Modules in Python

Lecture 69 Function and Module in Python: Making Modules in Python

Lecture 70 Function and Module in Python: Function Practice-Sorting List in Python

Lecture 71 String in Python: Strings

Lecture 72 String in Python: Multi Line Strings

Lecture 73 String in Python: Indexing Strings

Lecture 74 String in Python: Indexing Strings Quiz

Lecture 75 String in Python: Indexing Strings Solution

Lecture 76 String in Python: String Methods

Lecture 77 String in Python: String Methods Quiz

Lecture 78 String in Python: String Methods Solution

Lecture 79 String in Python: String Escape Sequences

Lecture 80 String in Python: String Escape Sequences Quiz

Lecture 81 String in Python: String Escape Sequences Solution

Lecture 82 Data Structure: Introduction to Data Structure

Lecture 83 Data Structure: Defining and Indexing

Lecture 84 Data Structure: Insertion and Deletion

Lecture 85 Data Structure: Insertion and Deletion Quiz

Lecture 86 Data Structure: Insertion and Deletion Solution

Lecture 87 Data Structure: Python Practice-Insertion and Deletion

Lecture 88 Data Structure: Python Practice-Insertion and Deletion Quiz

Lecture 89 Data Structure: Python Practice-Insertion and Deletion Solution

Lecture 90 Data Structure: Deep Copy or Reference Slicing

Lecture 91 Data Structure: Deep Copy or Reference Slicing Quiz

Lecture 92 Data Structure: Deep Copy or Reference Slicing Solution

Lecture 93 Data Structure: Exploring Methods Using TAB Completion

Lecture 94 Data Structure: Data Structure Abstract Ways

Lecture 95 Data Structure: Data Structure Practice

Lecture 96 Data Structure: Data Structure Practice Quiz

Lecture 97 Data Structure: Data Structure Practice Solution

Section 2: Python- Python for Data Preprocessing and Data Visualization

Lecture 98 Link to oneDrive and Github to get the Python Notebooks

Lecture 99 Introduction to the Course: About the Tutor and AI Sciences

Lecture 100 Introduction to the Course: Introduction To Instructor

Lecture 101 Introduction to the Course: Focus of the Course

Lecture 102 Introduction to the Course: Content of the Course

Lecture 103 Strings in Python: Introduction to Strings

Lecture 104 Strings in Python: MultiLine Strings

Lecture 105 Strings in Python: Indexing Strings

Lecture 106 Strings in Python: Indexing Strings Quiz

Lecture 107 Strings in Python: Indexing Strings Solution

Lecture 108 Strings in Python: String Methods

Lecture 109 Strings in Python: String Methods Quiz

Lecture 110 Strings in Python: String Methods Solution

Lecture 111 Strings in Python: String Escape and Sequences

Lecture 112 Strings in Python: String Escape and Sequences Quiz

Lecture 113 Strings in Python: String Escape and Sequences Solution

Lecture 114 Python Data Structure: Introduction to Data Structure

Lecture 115 Python Data Structure: Data Structures-Defining and Indexing

Lecture 116 Python Data Structure: Data Structures-Insertion and Deletion

Lecture 117 Python Data Structure: Data Structures-Insertion and Deletion Quiz

Lecture 118 Python Data Structure: Data Structures-Insertion and Deletion Solution

Lecture 119 Python Data Structure: Data Structures-Insertion and Deletion Python Practice

Lecture 120 Python Data Structure: Data Structures-Insertion and Deletion Python Practice Quiz

Lecture 121 Python Data Structure: Data Structures-Insertion and Deletion Python Practice Solution

Lecture 122 Python Data Structure: Data Structures-Deep Copy or Reference and Slicing

Lecture 123 Python Data Structure: Data Structures-Deep Copy or Reference and Slicing Quiz

Lecture 124 Python Data Structure: Data Structures-Deep Copy or Reference and Slicing Solution

Lecture 125 Python Data Structure: Data Structures-Exploring Methods Using TAB Completion

Lecture 126 Python Data Structure: Data Structures-Abstract Ways

Lecture 127 Python Data Structure: Data Structures-Problem Solving Practice

Lecture 128 Python Data Structure: Data Structures-Problem Solving Practice Quiz

Lecture 129 Python Data Structure: Data Structures-Problem Solving Practice Solution

Lecture 130 NumPy for Numerical Data Processing: Introduction to NumPy

Lecture 131 NumPy for Numerical Data Processing: Numpy Dimensions

Lecture 132 NumPy for Numerical Data Processing: NumPy Shape, Size and Bytes

Lecture 133 NumPy for Numerical Data Processing: NumPy Arange and Random Package

Lecture 134 NumPy for Numerical Data Processing: NumPy Arange and Random Package Quiz

Lecture 135 NumPy for Numerical Data Processing: NumPy Arange and Random Package Solution

Lecture 136 NumPy for Numerical Data Processing: NumPy Random Reshape

Lecture 137 NumPy for Numerical Data Processing: NumPy Slicing Combined

Lecture 138 NumPy for Numerical Data Processing: NumPy Slicing Combined Quiz

Lecture 139 NumPy for Numerical Data Processing: NumPy Slicing Combined Solution

Lecture 140 NumPy for Numerical Data Processing: NumPy Masking

Lecture 141 NumPy for Numerical Data Processing: NumPy Masking Quiz

Lecture 142 NumPy for Numerical Data Processing: NumPy Masking Solution

Lecture 143 NumPy for Numerical Data Processing: NumPy BroadCasting and Concatination

Lecture 144 NumPy for Numerical Data Processing: NumPy Ufuncs and SpeedTest

Lecture 145 NumPy for Numerical Data Processing: Ufuncs Add, Sum and Plus Operators

Lecture 146 NumPy for Numerical Data Processing: Ufuncs Subtract Power Mod

Lecture 147 NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators

Lecture 148 NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Quiz

Lecture 149 NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators Solution

Lecture 150 NumPy for Numerical Data Processing: Ufuncs Output Argument

Lecture 151 NumPy for Numerical Data Processing: NumPy Playing with Images

Lecture 152 NumPy for Numerical Data Processing: NumPy Playing with Images Quiz

Lecture 153 NumPy for Numerical Data Processing: NumPy Playing with Images Solution

Lecture 154 NumPy for Numerical Data Processing: NumPy KNN Classifier fromScratch

Lecture 155 NumPy for Numerical Data Processing: NumPy Structured Arrays

Lecture 156 NumPy for Numerical Data Processing: NumPy Structured Arrays Quiz

Lecture 157 NumPy for Numerical Data Processing: NumPy Structured Arrays Solution

Lecture 158 Pandas for Data Manipulation and Understanding: Introduction to Pandas

Lecture 159 Pandas for Data Manipulation and Understanding: Pandas Series

Lecture 160 Pandas for Data Manipulation and Understanding: Pandas DataFrame

Lecture 161 Pandas for Data Manipulation and Understanding: Pandas DataFrame Quiz

Lecture 162 Pandas for Data Manipulation and Understanding: Pandas DataFrame Solution

Lecture 163 Pandas for Data Manipulation and Understanding: Pandas Missing Values

Lecture 164 Pandas for Data Manipulation and Understanding: Pandas Loc Iloc

Lecture 165 Pandas for Data Manipulation and Understanding: Pandas in Practice

Lecture 166 Pandas for Data Manipulation and Understanding: Pandas Group by

Lecture 167 Pandas for Data Manipulation and Understanding: Pandas Group by Quiz

Lecture 168 Pandas for Data Manipulation and Understanding: Pandas Group by Solution

Lecture 169 Pandas for Data Manipulation and Understanding: Hierarchical Indexing

Lecture 170 Pandas for Data Manipulation and Understanding: Pandas Rolling

Lecture 171 Pandas for Data Manipulation and Understanding: Pandas Rolling Quiz

Lecture 172 Pandas for Data Manipulation and Understanding: Pandas Rolling Solution

Lecture 173 Pandas for Data Manipulation and Understanding: Pandas Where

Lecture 174 Pandas for Data Manipulation and Understanding: Pandas Clip

Lecture 175 Pandas for Data Manipulation and Understanding: Pandas Clip Quiz

Lecture 176 Pandas for Data Manipulation and Understanding: Pandas Clip Solution

Lecture 177 Pandas for Data Manipulation and Understanding: Pandas Merge

Lecture 178 Pandas for Data Manipulation and Understanding: Pandas Merge Quiz

Lecture 179 Pandas for Data Manipulation and Understanding: Pandas Merge Solution

Lecture 180 Pandas for Data Manipulation and Understanding: Pandas Pivot Table

Lecture 181 Pandas for Data Manipulation and Understanding: Pandas Strings

Lecture 182 Pandas for Data Manipulation and Understanding: Pandas DateTime

Lecture 183 Pandas for Data Manipulation and Understanding: Pandas Hands On COVID19 Data

Lecture 184 Pandas for Data Manipulation and Understanding: Pandas Hands On COVID19 Data Bug

Lecture 185 Matplotlib for Data Visualization: Introduction to Matplotlib

Lecture 186 Matplotlib for Data Visualization: Matplotlib Multiple Plots

Lecture 187 Matplotlib for Data Visualization: Matplotlib Colors and Styles

Lecture 188 Matplotlib for Data Visualization: Matplotlib Colors and Styles Quiz

Lecture 189 Matplotlib for Data Visualization: Matplotlib Colors and Styles Solution

Lecture 190 Matplotlib for Data Visualization: Matplotlib Colors and Styles Shortcuts

Lecture 191 Matplotlib for Data Visualization: Matplotlib Axis Limits

Lecture 192 Matplotlib for Data Visualization: Matplotlib Axis Limits Quiz

Lecture 193 Matplotlib for Data Visualization: Matplotlib Axis Limits Solution

Lecture 194 Matplotlib for Data Visualization: Matplotlib Legends Labels

Lecture 195 Matplotlib for Data Visualization: Matplotlib Set Function

Lecture 196 Matplotlib for Data Visualization: Matplotlib Set Function Quiz

Lecture 197 Matplotlib for Data Visualization: Matplotlib Set Function Solution

Lecture 198 Matplotlib for Data Visualization: Matplotlib Markers

Lecture 199 Matplotlib for Data Visualization: Matplotlib Markers Randomplots

Lecture 200 Matplotlib for Data Visualization: Matplotlib Scatter Plot

Lecture 201 Matplotlib for Data Visualization: Matplotlib Contour Plot

Lecture 202 Matplotlib for Data Visualization: Matplotlib Contour Plot Quiz

Lecture 203 Matplotlib for Data Visualization: Matplotlib Contour Plot Solution

Lecture 204 Matplotlib for Data Visualization: Matplotlib Histograms

Lecture 205 Matplotlib for Data Visualization: Matplotlib Subplots

Lecture 206 Matplotlib for Data Visualization: Matplotlib Subplots Quiz

Lecture 207 Matplotlib for Data Visualization: Matplotlib Subplots Solution

Lecture 208 Matplotlib for Data Visualization: Matplotlib 3D Introduction

Lecture 209 Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots

Lecture 210 Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Quiz

Lecture 211 Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots Solution

Lecture 212 Matplotlib for Data Visualization: Matplotlib 3D Surface Plots

Lecture 213 Seaborn for Data Visualization: Introduction to Seaborn

Lecture 214 Seaborn for Data Visualization: Seaborn Relplot

Lecture 215 Seaborn for Data Visualization: Seaborn Relplot Quiz

Lecture 216 Seaborn for Data Visualization: Seaborn Relplot Solution

Lecture 217 Seaborn for Data Visualization: Seaborn Relplot Kind Line

Lecture 218 Seaborn for Data Visualization: Seaborn Relplot Facets

Lecture 219 Seaborn for Data Visualization: Seaborn Relplot Facets Quiz

Lecture 220 Seaborn for Data Visualization: Seaborn Relplot Facets Solution

Lecture 221 Seaborn for Data Visualization: Seaborn Catplot

Lecture 222 Seaborn for Data Visualization: Seaborn Heatmaps

Lecture 223 Bokeh for Interactive Plotting: Introduction to Bokeh

Lecture 224 Bokeh for Interactive Plotting: Bokeh Multiplots Markers

Lecture 225 Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot

Lecture 226 Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Quiz

Lecture 227 Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot Solution

Lecture 228 Plotly for 3D Interactive Plotting; Plotly 3D Interactive Scatter Plot

Lecture 229 Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot Quiz

Lecture 230 Plotly for 3D Interactive Plotting; Plotly 3D Interactive Scatter Plot Solution

Lecture 231 Plotly for 3D Interactive Plotting; Plotly 3D Interactive Surface Plot

Lecture 232 Plotly for 3D Interactive Plotting; Plotly 3D Interactive Surface Plot Quiz

Lecture 233 Plotly for 3D Interactive Plotting; Plotly 3D Interactive Surface Plot Solution

Lecture 234 Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data

Lecture 235 Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Quiz

Lecture 236 Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data Solution

Lecture 237 Pandas for Plotting: Pandas for Plotting

Section 3: Python Machine Learning Crash Course for Beginners

Lecture 238 Link to the Python codes for the projects and the data

Lecture 239 Introduction to the Course: Introduction to the Course

Lecture 240 Introduction to the Course: Introduction To Instructor

Lecture 241 Introduction to the Course: Focus of the Course

Lecture 242 Introduction to the Course: Python Practical of the Course

Lecture 243 Why Machine Learning: Machine Learning Applications-Part 1

Lecture 244 Why Machine Learning: Machine Learning Applications-Part 2

Lecture 245 Why Machine Learning: Why Machine Learning is Trending Now

Lecture 246 Process of Learning from Data: Supervised Learning

Lecture 247 Process of Learning from Data: UnSupervised Learning and Reinforcement Learning

Lecture 248 Machine Learning Methods: Features

Lecture 249 Machine Learning Methods: Features Practice with Python

Lecture 250 Machine Learning Methods: Regression

Lecture 251 Machine Learning Methods: Regression Practice with Python

Lecture 252 Machine Learning Methods: Classsification

Lecture 253 Machine Learning Methods: Classification Practice with Python

Lecture 254 Machine Learning Methods: Clustering

Lecture 255 Machine Learning Methods: Clustering Practice with Python

Lecture 256 Data Preparation and Preprocessing: Handling Image Data

Lecture 257 Data Preparation and Preprocessing: Handling Video and Audio Data

Lecture 258 Data Preparation and Preprocessing: Handling Text Data

Lecture 259 Data Preparation and Preprocessing: One Hot Encoding

Lecture 260 Data Preparation and Preprocessing: Data Standardization

Lecture 261 Machine Learning Models and Optimization: Machine Learning Model 1

Lecture 262 Machine Learning Models and Optimization: Machine Learning Model 2

Lecture 263 Machine Learning Models and Optimization: Machine Learning Model 3

Lecture 264 Machine Learning Models and Optimization: Training Process, Error, Cost and Loss

Lecture 265 Machine Learning Models and Optimization: Optimization

Lecture 266 Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 1

Lecture 267 Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2

Lecture 268 Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 1

Lecture 269 Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 2

Lecture 270 Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 1

Lecture 271 Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 2

Lecture 272 Overfitting, Underfitting and Generalization: Overfitting Introduction

Lecture 273 Overfitting, Underfitting and Generalization: Overfitting example on Python

Lecture 274 Overfitting, Underfitting and Generalization: Regularization

Lecture 275 Overfitting, Underfitting and Generalization: Generalization

Lecture 276 Overfitting, Underfitting and Generalization: Data Snooping and the Test Set

Lecture 277 Overfitting, Underfitting and Generalization: Cross-validation

Lecture 278 Machine Learning Model Performance Metrics: The Accuracy

Lecture 279 Machine Learning Model Performance Metrics: The Confusion Matrix

Section 4: Support Vector Machine A-Z: Support Vector Machine Python

Lecture 280 Link to the Python codes for the projects and the data

Lecture 281 Support Vector Machine: Introduction SVM

Lecture 282 Support Vector Machine: Linear Discriminants

Lecture 283 Support Vector Machine: Linear Discriminants higher spaces

Lecture 284 Support Vector Machine: Linear Discriminants Decision Boundary

Lecture 285 Support Vector Machine: Generalized Linear Model

Lecture 286 Support Vector Machine: Feature Transformation

Lecture 287 Support Vector Machine: Max Margin Linear Discriminant

Lecture 288 Support Vector Machine: Hard Margin Vs Soft Margin

Lecture 289 Support Vector Machine: Confidence

Lecture 290 Support Vector Machine: Multiclass Extension

Lecture 291 Support Vector Machine: SVM Vs Logistic Regression Sparsity

Lecture 292 Support Vector Machine: SVM Optimization

Lecture 293 Support Vector Machine: SVM Langrangian Dual

Lecture 294 Support Vector Machine: Kernels

Lecture 295 Support Vector Machine: Python Packages & Titanic DataSet

Lecture 296 Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 1)

Lecture 297 Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 2)

Lecture 298 Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 3)

Lecture 299 Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 4)

Lecture 300 Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 5)

Lecture 301 Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 6)

Lecture 302 Support Vector Machine: DataSet Preprocessing

Lecture 303 Support Vector Machine: SVM with Sklearn

Lecture 304 Support Vector Machine: SVM without Sklearn (Part 1)

Lecture 305 Support Vector Machine: SVM without Sklearn (Part 2)

Lecture 306 Optional SVM Section: Optional SVM Optimization (Part 1)

Lecture 307 Optional SVM Section: Optional SVM Optimization (Part 2)

Lecture 308 Optional SVM Section: Optional SVM Optimization (Part 3)

Lecture 309 Optional SVM Section: Optional SVM Optimization (Part 4)

Lecture 310 Optional SVM Section: Optional SVM Optimization (Part 5)

Lecture 311 Optional SVM Section: Optional SVM Optimization (Part 6)

Section 5: Machine Learning : Random Forest with Python

Lecture 312 Link to the Python codes for the projects and the data

Lecture 313 Random Forest Step-by-step: Introduction and Motivation

Lecture 314 Random Forest Step-by-step: How Decision Trees and Random Forest Work

Lecture 315 Random Forest Step-by-step: Pros and Cons of Random Forest

Lecture 316 Random Forest Step-by-step: Introduction to the final Project

Lecture 317 Random Forest Step-by-step: Using NumPy for Random Forest

Lecture 318 Random Forest Step-by-step: Using Pandas for Random Forest (1)

Lecture 319 Random Forest Step-by-step: Using Pandas for Random Forest (2)

Lecture 320 Random Forest Step-by-step: Reading and Manipulating Dataset

Lecture 321 Random Forest Step-by-step: Using Matplotlib for Data Visualization (1)

Lecture 322 Random Forest Step-by-step: Using Matplotlib for Data Visualization (2)

Lecture 323 Random Forest Step-by-step: Dealing with Missing Values

Lecture 324 Random Forest Step-by-step: Outliers Removal

Lecture 325 Random Forest Step-by-step: Categorical to Numeric Conversion

Lecture 326 Random Forest Step-by-step: Quick Implementation of Random Forest Model

Lecture 327 Random Forest Step-by-step: Feature Importance

Lecture 328 Random Forest Step-by-step: Recursion

Lecture 329 Random Forest Step-by-step: Structure

Lecture 330 Random Forest Step-by-step: Importing Data, Helper Functions

Lecture 331 Random Forest Step-by-step: Question and Partition

Lecture 332 Random Forest Step-by-step: Impurity

Lecture 333 Random Forest Step-by-step: Information Gain

Lecture 334 Random Forest Step-by-step: Best Slip

Lecture 335 Random Forest Step-by-step: Leaf and Decision Node

Lecture 336 Random Forest Step-by-step: How to Build Tree

Lecture 337 Random Forest Step-by-step: Classify

Lecture 338 Random Forest Step-by-step: Accuracy and Error

Section 6: Machine Learning A-Z: Logistic Regression with Python

Lecture 339 Link to the Python codes for the projects and the data

Lecture 340 Logistic Regression Step-by-Step: Introduction to Logistic Regression and Motivation

Lecture 341 Logistic Regression Step-by-Step: Pros and Cons

Lecture 342 Logistic Regression Step-by-Step: Introduction to the final Project

Lecture 343 Logistic Regression Step-by-Step: Numpy

Lecture 344 Logistic Regression Step-by-Step: Pandas (1)

Lecture 345 Logistic Regression Step-by-Step: Pandas (2)

Lecture 346 Logistic Regression Step-by-Step: Reading and Manipulating Dataset

Lecture 347 Logistic Regression Step-by-Step: Matplotlib (1)

Lecture 348 Logistic Regression Step-by-Step: Matplotlib (2)

Lecture 349 Logistic Regression Step-by-Step: Dealing with Missing Values

Lecture 350 Logistic Regression Step-by-Step: Outliers Removal

Lecture 351 Logistic Regression Step-by-Step: Categorical to Numeric

Lecture 352 Logistic Regression Step-by-Step: ScikitLearn - Quick Implementation of Logistic Regression

Lecture 353 Logistic Regression Step-by-Step: Sigmoid Function

Lecture 354 Logistic Regression Step-by-Step: Decision Boundary

Lecture 355 Logistic Regression Step-by-Step: Cost Function

Lecture 356 Logistic Regression Step-by-Step: Gradient Decent

Lecture 357 Logistic Regression Step-by-Step: Logistic Regression from Scratch (1)

Lecture 358 Logistic Regression Step-by-Step: Logistic Regression from Scratch (2)

Lecture 359 Logistic Regression Step-by-Step: Logistic Regression from Scratch (3)

Lecture 360 Logistic Regression Step-by-Step: Logistic Regression from Scratch (4)

Lecture 361 Logistic Regression Step-by-Step: Logistic Regression from Scratch (5)

Lecture 362 Logistic Regression Step-by-Step: Logistic Regression from Scratch (6)

Lecture 363 Logistic Regression Step-by-Step: Binary to Multiclass

Section 7: Machine Learning: 5 Beginner-Friendly Hands-On ML Projects

Lecture 364 Links for the Course's Materials and Codes

Lecture 365 Introduction: Introduction To Instructor

Lecture 366 Introduction: Introduction to Course

Lecture 367 House Price Prediction: Module Introduction

Lecture 368 House Price Prediction: Installing-Importing Libraries

Lecture 369 House Price Prediction: Importing & Visualizing Dataset

Lecture 370 House Price Prediction: Dataset

Lecture 371 House Price Prediction: Feature Label Split

Lecture 372 House Price Prediction: Train Test Split

Lecture 373 House Price Prediction: ML Model

Lecture 374 House Price Prediction: Liner Regression

Lecture 375 House Price Prediction: Model Evaluation

Lecture 376 House Price Prediction: Making Predictions

Lecture 377 House Price Prediction: Quiz

Lecture 378 Email Filtration: Module Introduction

Lecture 379 Email Filtration: Data Import Split

Lecture 380 Email Filtration: Removing Stopwords

Lecture 381 Email Filtration: Making Word Cloud

Lecture 382 Email Filtration: Count Vectorizer Explained

Lecture 383 Email Filtration: Vectorizing Text Feature

Lecture 384 Email Filtration: Model Implementation & Evaluation

Lecture 385 Email Filtration: NB Details and Types

Lecture 386 Email Filtration: Making Predictions

Lecture 387 Email Filtration: Quiz

Lecture 388 Car Price Predication: Module Introduction

Lecture 389 Car Price Predication: Getting Started with Colab

Lecture 390 Car Price Predication: Loading Data in Colab

Lecture 391 Car Price Predication: Data Visualization

Lecture 392 Car Price Predication: One Hot Encoding

Lecture 393 Car Price Predication: Data Standardization

Lecture 394 Car Price Predication: Deep Learning Explained

Lecture 395 Car Price Predication: Model Architecture

Lecture 396 Car Price Predication: Model Training

Lecture 397 Car Price Predication: Model Evaluation

Lecture 398 Car Price Predication: Making Predictions

Lecture 399 Car Price Predication: Quiz

Lecture 400 Customer Segmentation Kmean: Module Introduction

Lecture 401 Customer Segmentation Kmean: Imports and Data Intro

Lecture 402 Customer Segmentation Kmean: Data Visualization & Analysis

Lecture 403 Customer Segmentation Kmean: K Means

Lecture 404 Customer Segmentation Kmean: Model Implementation

Lecture 405 Customer Segmentation Kmean: Ploting the Centroids

Lecture 406 Customer Segmentation Kmean: Finding the Optimal Value

Lecture 407 Customer Segmentation Kmean: Cluster Map

Lecture 408 Customer Segmentation Kmean: Quiz

Lecture 409 Employee Retention Classification for HR: Module Introduction

Lecture 410 Employee Retention Classification for HR: Libraries & Dataset

Lecture 411 Employee Retention Classification for HR: Data Analysis

Lecture 412 Employee Retention Classification for HR: Model Training & Evaluation

Lecture 413 Employee Retention Classification for HR: Heatmap of Predictions

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