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Machine Learning with Python

Machine Learning with Python

Enhance your skills in Machine Learning with our comprehensive course on Python.

  • Category AI & ML
Machine Learning with Python

What you'll learn

  • Fundamentals of Python and Machine Learning
  • Real-World Applications & Case Studies
  • Data Preprocessing & Feature Engineering
  • Handling Imbalanced Data & Anomaly Detection
  • Model Building & Evaluation
  • Hyperparameter Tuning & Optimization

Course Syllabus

Introduction to AI, ML & Python
  • a:6:{i:0;s:16:"What is AI & ML?";i:1;s:23:"Applications of AI & ML";i:2;s:25:"Machine Learning Workflow";i:3;s:23:"Why Python for AI & ML?";i:4;s:29:"Setting up Python Environment";i:5;s:29:"Writing & Running Python Code";}
Introduction to Python Libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)
  • a:3:{i:0;s:31:"Installing & Importing Packages";i:1;s:19:"Python Fundamentals";i:2;s:23:"Variables & Identifiers";}
Operators in Python
  • Assignment
  • Comparison
  • Logical
  • Membership Operators";}
  • a:6:{i:0;s:29:"Assigning Values to Variables";i:1;s:24:"Dynamic Typing in Python";i:2;s:27:"Reserved Keywords in Python";i:3;s:27:"Writing Meaningful Comments";i:4;s:20:"Control Flow & Loops";i:5;s:65:"Arithmetic
Exploratory Data Analysis (EDA) using NumPy, Pandas, Matplotlib & Seaborn
  • a:0:{}
NumPy
  • a:1:{i:0;s:21:"Introduction to NumPy";}
Pandas
  • 2D
  • Accessing
  • Concat
  • Joining
  • Reshape
  • Reshape";}
  • Transpose
  • and 3D Arrays";i:32;s:54:"Special NumPy Functions: zeros()
  • and Broadcasting";i:40;s:22:"Introduction to Pandas";i:41;s:21:"Series and DataFrames";i:42;s:48:"Creating DataFrames using Lists and Dictionaries";i:43;s:28:"Insert and Delete Operations";i:44;s:21:"Arithmetic Operations";i:45;s:20:"Indexing and Slicing";i:46;s:34:"Reading Data from CSV & JSON Files";i:47;s:31:"Exploratory Data Analysis (EDA)";i:48;s:21:"Handling Missing Data";i:49;s:23:"Handling Duplicate Data";i:50;s:32:"Outliers Detection and Treatment";i:51;s:34:"Join
  • and Merge Operations";i:52;s:25:"Date-Time Functionalities";i:53;s:29:"groupby()
  • and Modifying Lists & Tuples";i:8;s:20:"List & Tuple Methods";i:9;s:19:"Dictionaries & Sets";i:10;s:31:"Key-Value Pairs in Dictionaries";i:11;s:14:"Set Operations";i:12;s:27:"Advanced Control Statements";i:13;s:47:"break
  • and Splitting";i:39;s:36:"Transpose
  • continue
  • continue
  • elif
  • else Statements";i:1;s:50:"Iterative Statements (Loops) - for and while Loops";i:2;s:12:"Nested Loops";i:3;s:23:"Loop Control Statements";i:4;s:21:"break
  • etc.";i:33;s:24:"Random Number Generation";i:34;s:20:"Data Type Conversion";i:35;s:26:"Memory Management in NumPy";i:36;s:21:"Arithmetic Operations";i:37;s:22:"Statistical Operations";i:38;s:31:"Sorting
  • etc.)";i:20;s:16:"Lambda Functions";i:21;s:40:"Anonymous Functions & Their Applications";i:22;s:24:"Advanced Python Concepts";i:23;s:32:"List & Dictionary Comprehensions";i:24;s:29:"Writing Efficient Python Code";i:25;s:20:"Examples & Use Cases";i:26;s:18:"Exception Handling";i:27;s:27:"try
  • except
  • finally Blocks";i:28;s:28:"Handling Multiple Exceptions";i:29;s:25:"Raising Custom Exceptions";i:30;s:26:"NumPy Array vs Python List";i:31;s:33:"Creation of 1D
  • full()
  • max()
  • ones()
  • pass (Detailed with Use Cases)";i:14;s:19:"Functions in Python";i:15;s:22:"User-defined Functions";i:16;s:29:"Function Definition & Calling";i:17;s:34:"Function Arguments & Return Values";i:18;s:18:"Built-in Functions";i:19;s:51:"Commonly Used Functions (len()
  • pass";i:5;s:25:"Data Structures in Python";i:6;s:14:"Lists & Tuples";i:7;s:49:"Creating
  • sum()
  • a:54:{i:0;s:49:"Conditional Statements- if
Matplotlib & Seaborn
  • a:11:{i:0;s:26:"Introduction to Matplotlib";i:1;s:9:"Line Plot";i:2;s:8:"Bar Plot";i:3;s:12:"Scatter Plot";i:4;s:9:"Histogram";i:5;s:9:"Pie Chart";i:6;s:8:"3D Plots";i:7;s:23:"Introduction to Seaborn";i:8;s:7:"Boxplot";i:9;s:8:"Distplot";i:10;s:7:"Heatmap";}
Statistics for Machine Learning
  • ANOVA Test
  • Chi-Square Test";i:34;s:38:"Null Hypothesis & Alternate Hypothesis";i:35;s:28:"P-Value & Significance Level";}
  • Decile";i:20;s:19:"Five Number Summary";i:21;s:25:"Interquartile Range (IQR)";i:22;s:34:"Effect of Outliers and Its Removal";i:23;s:31:"Outlier Detection using Boxplot";i:24;s:39:"Probability Distributions & Correlation";i:25;s:30:"Normal (Gaussian) Distribution";i:26;s:33:"Properties of Normal Distribution";i:27;s:21:"Central Limit Theorem";i:28;s:44:"Covariance & Pearson Correlation Coefficient";i:29;s:29:"Inferential Statistical Tests";i:30;s:19:"Confidence Interval";i:31;s:19:"Regression Analysis";i:32;s:18:"Hypothesis Testing";i:33;s:51:"T-Test
  • F-Test
  • Pentile
  • Percentile
  • Quantile
  • Z-Test
  • a:36:{i:0;s:26:"Fundamentals of Statistics";i:1;s:26:"Introduction to Statistics";i:2;s:19:"Types of Statistics";i:3;s:48:"Descriptive Statistics vs Inferential Statistics";i:4;s:26:"Population and Sample Data";i:5;s:20:"Sampling Techniques:";i:6;s:22:"Simple Random Sampling";i:7;s:19:"Systematic Sampling";i:8;s:19:"Stratified Sampling";i:9;s:16:"Cluster Sampling";i:10;s:30:"Variables & Data Distributions";i:11;s:18:"Types of Variables";i:12;s:37:"Quantitative vs Qualitative Variables";i:13;s:34:"Frequency and Cumulative Frequency";i:14;s:21:"Measures of Frequency";i:15;s:28:"Measures of Central Tendency";i:16;s:35:"Measures of Dispersion and Variance";i:17;s:30:"Z-Score and Standard Deviation";i:18;s:41:"Measures of Position or Data Distribution";i:19;s:47:"Quartile
Machine Learning
  • ML and DL";i:2;s:32:"Applications of Machine Learning";i:3;s:25:"Types of Machine Learning";i:4;s:41:"Supervised / Unsupervised / Reinforcement";i:5;s:28:"Parametric vs Non Parametric";i:6;s:28:"Introduction to Scikit-Learn";i:7;s:30:"Sample Dataset in Scikit-Learn";i:8;s:15:"Goodness of Fit";i:9;s:28:"Overfitting and Underfitting";i:10;s:22:"Bias-Variance Tradeoff";i:11;s:24:"L1 and L2 Regularization";i:12;s:15:"Imbalanced Data";}
  • a:13:{i:0;s:32:"Introduction to Machine Learning";i:1;s:32:"Difference Between AI
Cost and Loss Functions
  • a:5:{i:0;s:17:"Optimizers for ML";i:1;s:16:"Gradient Descent";i:2;s:18:"Evaluation Metrics";i:3;s:18:"Regression Metrics";i:4;s:22:"Classification Metrics";}
EDA and Data Wrangling
  • Label Encoding
  • Target Encoding";i:5;s:10:"Curriculum";}
  • a:6:{i:0;s:22:"Null Values Imputation";i:1;s:17:"Outlier Detection";i:2;s:44:"Univariate/ Bivariate/ Multivariate Analysis";i:3;s:18:"Encoding Technique";i:4;s:36:"OHE
Feature Scaling
  • a:2:{i:0;s:13:"Normalization";i:1;s:15:"Standardization";}
Feature Selection
  • a:3:{i:0;s:14:"Filter Methods";i:1;s:15:"Wrapper Methods";i:2;s:17:"Embedding Methods";}
Cross-Validation Techniques
  • a:8:{i:0;s:19:"Feature Engineering";i:1;s:15:"Model Selection";i:2;s:18:"Holdout Validation";i:3;s:23:"K-fold Cross Validation";i:4;s:17:"Stratified K-fold";i:5;s:21:"Hyperparameter Tuning";i:6;s:12:"GridSearchCV";i:7;s:18:"RandomizedSearchCV";}
Machine Learning Algorithms
  • a:5:{i:0;s:20:"Regression Algorithm";i:1;s:17:"Linear Regression";i:2;s:31:"Assumption of Linear Regression";i:3;s:32:"Limitations of Linear Regression";i:4;s:24:"Practical Implementation";}
Polynomial Regression
  • a:3:{i:0;s:35:"Assumption of Polynomial Regression";i:1;s:36:"Limitations of Polynomial Regression";i:2;s:24:"Practical Implementation";}
Ridge Regression
  • a:2:{i:0;s:16:"Lasso Regression";i:1;s:24:"Practical Implementation";}
Classification
  • a:1:{i:0;s:20:"Regression Algorithm";}
Logistic Regression
  • a:3:{i:0;s:33:"Assumption of Logistic Regression";i:1;s:34:"Limitations of Logistic Regression";i:2;s:24:"Practical Implementation";}
Decision Tree
  • a:3:{i:0;s:22:"Entropy and Gini Index";i:1;s:16:"Information Gain";i:2;s:24:"Practical Implementation";}
Capstone Projects
  • a:0:{}
Advance House Price Prediction using Regression Algorithms
  • having arobust prediction model is crucial for both buyers and sellers to makeinformed decisions. This project employs advanced machine learningtechniques to enhance the accuracy and efficiency of house pricepredictions.";}
  • a:1:{i:0;s:431:"The Advanced House Price Prediction project leverages state-of-the-artregression algorithms to provide accurate and reliable predictions for realestate prices. In the dynamic and competitive real estate market
Netflix Movie Recommendation System using Content-BasedFiltering
  • Netflix recognizes the importance ofoffering tailored suggestions to users
  • ensuring they discover content thataligns with their preferences. This project utilizes content-basedrecommendation techniques to create a recommendation engine.";}
  • a:1:{i:0;s:472:"The Netflix Movie Recommendation System project aims to enhance userexperience by leveraging advancedmachine learning algorithms to providepersonalised and relevant movierecommendations. With an extensivelibraryof movies and TV shows

Course Syllabus

  • What is AI & ML?
  • Applications of AI & ML
  • Machine Learning Workflow
  • Why Python for AI & ML?
  • Setting up Python Environment
  • Writing & Running Python Code

  • Installing & Importing Packages
  • Python Fundamentals
  • Variables & Identifiers

  • Assigning Values to Variables
  • Dynamic Typing in Python
  • Reserved Keywords in Python
  • Writing Meaningful Comments
  • Control Flow & Loops
  • Arithmetic, Comparison, Logical, Assignment, Membership Operators

  • Introduction to NumPy

  • Conditional Statements- if, elif, else Statements
  • Iterative Statements (Loops) - for and while Loops
  • Nested Loops
  • Loop Control Statements
  • break, continue, pass
  • Data Structures in Python
  • Lists & Tuples
  • Creating, Accessing, and Modifying Lists & Tuples
  • List & Tuple Methods
  • Dictionaries & Sets
  • Key-Value Pairs in Dictionaries
  • Set Operations
  • Advanced Control Statements
  • break, continue, pass (Detailed with Use Cases)
  • Functions in Python
  • User-defined Functions
  • Function Definition & Calling
  • Function Arguments & Return Values
  • Built-in Functions
  • Commonly Used Functions (len(), sum(), max(), etc.)
  • Lambda Functions
  • Anonymous Functions & Their Applications
  • Advanced Python Concepts
  • List & Dictionary Comprehensions
  • Writing Efficient Python Code
  • Examples & Use Cases
  • Exception Handling
  • try, except, finally Blocks
  • Handling Multiple Exceptions
  • Raising Custom Exceptions
  • NumPy Array vs Python List
  • Creation of 1D, 2D, and 3D Arrays
  • Special NumPy Functions: zeros(), ones(), full(), etc.
  • Random Number Generation
  • Data Type Conversion
  • Memory Management in NumPy
  • Arithmetic Operations
  • Statistical Operations
  • Sorting, Joining, and Splitting
  • Transpose, Reshape, and Broadcasting
  • Introduction to Pandas
  • Series and DataFrames
  • Creating DataFrames using Lists and Dictionaries
  • Insert and Delete Operations
  • Arithmetic Operations
  • Indexing and Slicing
  • Reading Data from CSV & JSON Files
  • Exploratory Data Analysis (EDA)
  • Handling Missing Data
  • Handling Duplicate Data
  • Outliers Detection and Treatment
  • Join, Concat, and Merge Operations
  • Date-Time Functionalities
  • groupby(), Transpose, Reshape

  • Introduction to Matplotlib
  • Line Plot
  • Bar Plot
  • Scatter Plot
  • Histogram
  • Pie Chart
  • 3D Plots
  • Introduction to Seaborn
  • Boxplot
  • Distplot
  • Heatmap

  • Fundamentals of Statistics
  • Introduction to Statistics
  • Types of Statistics
  • Descriptive Statistics vs Inferential Statistics
  • Population and Sample Data
  • Sampling Techniques:
  • Simple Random Sampling
  • Systematic Sampling
  • Stratified Sampling
  • Cluster Sampling
  • Variables & Data Distributions
  • Types of Variables
  • Quantitative vs Qualitative Variables
  • Frequency and Cumulative Frequency
  • Measures of Frequency
  • Measures of Central Tendency
  • Measures of Dispersion and Variance
  • Z-Score and Standard Deviation
  • Measures of Position or Data Distribution
  • Quartile, Quantile, Percentile, Pentile, Decile
  • Five Number Summary
  • Interquartile Range (IQR)
  • Effect of Outliers and Its Removal
  • Outlier Detection using Boxplot
  • Probability Distributions & Correlation
  • Normal (Gaussian) Distribution
  • Properties of Normal Distribution
  • Central Limit Theorem
  • Covariance & Pearson Correlation Coefficient
  • Inferential Statistical Tests
  • Confidence Interval
  • Regression Analysis
  • Hypothesis Testing
  • T-Test, Z-Test, F-Test, ANOVA Test, Chi-Square Test
  • Null Hypothesis & Alternate Hypothesis
  • P-Value & Significance Level

  • Introduction to Machine Learning
  • Difference Between AI, ML and DL
  • Applications of Machine Learning
  • Types of Machine Learning
  • Supervised / Unsupervised / Reinforcement
  • Parametric vs Non Parametric
  • Introduction to Scikit-Learn
  • Sample Dataset in Scikit-Learn
  • Goodness of Fit
  • Overfitting and Underfitting
  • Bias-Variance Tradeoff
  • L1 and L2 Regularization
  • Imbalanced Data

  • Optimizers for ML
  • Gradient Descent
  • Evaluation Metrics
  • Regression Metrics
  • Classification Metrics

  • Null Values Imputation
  • Outlier Detection
  • Univariate/ Bivariate/ Multivariate Analysis
  • Encoding Technique
  • OHE, Label Encoding, Target Encoding
  • Curriculum

  • Normalization
  • Standardization

  • Filter Methods
  • Wrapper Methods
  • Embedding Methods

  • Feature Engineering
  • Model Selection
  • Holdout Validation
  • K-fold Cross Validation
  • Stratified K-fold
  • Hyperparameter Tuning
  • GridSearchCV
  • RandomizedSearchCV

  • Regression Algorithm
  • Linear Regression
  • Assumption of Linear Regression
  • Limitations of Linear Regression
  • Practical Implementation

  • Assumption of Polynomial Regression
  • Limitations of Polynomial Regression
  • Practical Implementation

  • Lasso Regression
  • Practical Implementation

  • Regression Algorithm

  • Assumption of Logistic Regression
  • Limitations of Logistic Regression
  • Practical Implementation

  • Entropy and Gini Index
  • Information Gain
  • Practical Implementation

  • The Advanced House Price Prediction project leverages state-of-the-artregression algorithms to provide accurate and reliable predictions for realestate prices. In the dynamic and competitive real estate market, having arobust prediction model is crucial for both buyers and sellers to makeinformed decisions. This project employs advanced machine learningtechniques to enhance the accuracy and efficiency of house pricepredictions.

  • The Netflix Movie Recommendation System project aims to enhance userexperience by leveraging advancedmachine learning algorithms to providepersonalised and relevant movierecommendations. With an extensivelibraryof movies and TV shows, Netflix recognizes the importance ofoffering tailored suggestions to users, ensuring they discover content thataligns with their preferences. This project utilizes content-basedrecommendation techniques to create a recommendation engine.

Requirements

  • Good Wifi
  • Laptop

Description

Machine Learning with Python

This course provides a comprehensive introduction to Machine Learning (ML) using Python, covering key concepts, real-world applications, data preprocessing, model building, and optimization techniques. It combines statistics, machine learning, data analysis, and domain expertise to uncover patterns, trends, and correlations that can help organizations make informed decisions and drive business growth. Through hands-on projects and practical implementation, you will gain expertise in building and evaluating ML models using Python libraries like NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and more.

  • Gain a comprehensive introduction to Machine Learning (ML) using Python.
  • Learn key concep.ts, real-world applications, data preprocessing, model building, and optimization techniques
  • Combine statistics, machine learning, data analysis, and domain expertise to uncover patterns, trends, and correlations.
  • Develop skills in building and evaluating ML models using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and more.
  • Engage in hands-on projects and practical implementation to enhance expertise.

Who this course is for:

  • Beginners looking to start a career in Machine Learning & Data Science.
  • Software developers and engineers who want to apply ML techniques to real-world applications.
  • Anyone interested in building ML models using Python and understanding how AI is shaping industries.

Meet your instructors

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Sourav Kapil

View Profile

Sourav Kapil is a Data Science and AI Trainer with over 4 years of professional experience in Machine Learning, Artificial Intelligence, and Data Science. Having trained 1,500+ students, he is passionate about making AI accessible and impactful for learners at all levels. With a strong background...

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Course Syllabus

Introduction to AI, ML & Python
  • a:6:{i:0;s:16:"What is AI & ML?";i:1;s:23:"Applications of AI & ML";i:2;s:25:"Machine Learning Workflow";i:3;s:23:"Why Python for AI & ML?";i:4;s:29:"Setting up Python Environment";i:5;s:29:"Writing & Running Python Code";}
Introduction to Python Libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)
  • a:3:{i:0;s:31:"Installing & Importing Packages";i:1;s:19:"Python Fundamentals";i:2;s:23:"Variables & Identifiers";}
Operators in Python
  • Assignment
  • Comparison
  • Logical
  • Membership Operators";}
  • a:6:{i:0;s:29:"Assigning Values to Variables";i:1;s:24:"Dynamic Typing in Python";i:2;s:27:"Reserved Keywords in Python";i:3;s:27:"Writing Meaningful Comments";i:4;s:20:"Control Flow & Loops";i:5;s:65:"Arithmetic
Exploratory Data Analysis (EDA) using NumPy, Pandas, Matplotlib & Seaborn
  • a:0:{}
NumPy
  • a:1:{i:0;s:21:"Introduction to NumPy";}
Pandas
  • 2D
  • Accessing
  • Concat
  • Joining
  • Reshape
  • Reshape";}
  • Transpose
  • and 3D Arrays";i:32;s:54:"Special NumPy Functions: zeros()
  • and Broadcasting";i:40;s:22:"Introduction to Pandas";i:41;s:21:"Series and DataFrames";i:42;s:48:"Creating DataFrames using Lists and Dictionaries";i:43;s:28:"Insert and Delete Operations";i:44;s:21:"Arithmetic Operations";i:45;s:20:"Indexing and Slicing";i:46;s:34:"Reading Data from CSV & JSON Files";i:47;s:31:"Exploratory Data Analysis (EDA)";i:48;s:21:"Handling Missing Data";i:49;s:23:"Handling Duplicate Data";i:50;s:32:"Outliers Detection and Treatment";i:51;s:34:"Join
  • and Merge Operations";i:52;s:25:"Date-Time Functionalities";i:53;s:29:"groupby()
  • and Modifying Lists & Tuples";i:8;s:20:"List & Tuple Methods";i:9;s:19:"Dictionaries & Sets";i:10;s:31:"Key-Value Pairs in Dictionaries";i:11;s:14:"Set Operations";i:12;s:27:"Advanced Control Statements";i:13;s:47:"break
  • and Splitting";i:39;s:36:"Transpose
  • continue
  • continue
  • elif
  • else Statements";i:1;s:50:"Iterative Statements (Loops) - for and while Loops";i:2;s:12:"Nested Loops";i:3;s:23:"Loop Control Statements";i:4;s:21:"break
  • etc.";i:33;s:24:"Random Number Generation";i:34;s:20:"Data Type Conversion";i:35;s:26:"Memory Management in NumPy";i:36;s:21:"Arithmetic Operations";i:37;s:22:"Statistical Operations";i:38;s:31:"Sorting
  • etc.)";i:20;s:16:"Lambda Functions";i:21;s:40:"Anonymous Functions & Their Applications";i:22;s:24:"Advanced Python Concepts";i:23;s:32:"List & Dictionary Comprehensions";i:24;s:29:"Writing Efficient Python Code";i:25;s:20:"Examples & Use Cases";i:26;s:18:"Exception Handling";i:27;s:27:"try
  • except
  • finally Blocks";i:28;s:28:"Handling Multiple Exceptions";i:29;s:25:"Raising Custom Exceptions";i:30;s:26:"NumPy Array vs Python List";i:31;s:33:"Creation of 1D
  • full()
  • max()
  • ones()
  • pass (Detailed with Use Cases)";i:14;s:19:"Functions in Python";i:15;s:22:"User-defined Functions";i:16;s:29:"Function Definition & Calling";i:17;s:34:"Function Arguments & Return Values";i:18;s:18:"Built-in Functions";i:19;s:51:"Commonly Used Functions (len()
  • pass";i:5;s:25:"Data Structures in Python";i:6;s:14:"Lists & Tuples";i:7;s:49:"Creating
  • sum()
  • a:54:{i:0;s:49:"Conditional Statements- if
Matplotlib & Seaborn
  • a:11:{i:0;s:26:"Introduction to Matplotlib";i:1;s:9:"Line Plot";i:2;s:8:"Bar Plot";i:3;s:12:"Scatter Plot";i:4;s:9:"Histogram";i:5;s:9:"Pie Chart";i:6;s:8:"3D Plots";i:7;s:23:"Introduction to Seaborn";i:8;s:7:"Boxplot";i:9;s:8:"Distplot";i:10;s:7:"Heatmap";}
Statistics for Machine Learning
  • ANOVA Test
  • Chi-Square Test";i:34;s:38:"Null Hypothesis & Alternate Hypothesis";i:35;s:28:"P-Value & Significance Level";}
  • Decile";i:20;s:19:"Five Number Summary";i:21;s:25:"Interquartile Range (IQR)";i:22;s:34:"Effect of Outliers and Its Removal";i:23;s:31:"Outlier Detection using Boxplot";i:24;s:39:"Probability Distributions & Correlation";i:25;s:30:"Normal (Gaussian) Distribution";i:26;s:33:"Properties of Normal Distribution";i:27;s:21:"Central Limit Theorem";i:28;s:44:"Covariance & Pearson Correlation Coefficient";i:29;s:29:"Inferential Statistical Tests";i:30;s:19:"Confidence Interval";i:31;s:19:"Regression Analysis";i:32;s:18:"Hypothesis Testing";i:33;s:51:"T-Test
  • F-Test
  • Pentile
  • Percentile
  • Quantile
  • Z-Test
  • a:36:{i:0;s:26:"Fundamentals of Statistics";i:1;s:26:"Introduction to Statistics";i:2;s:19:"Types of Statistics";i:3;s:48:"Descriptive Statistics vs Inferential Statistics";i:4;s:26:"Population and Sample Data";i:5;s:20:"Sampling Techniques:";i:6;s:22:"Simple Random Sampling";i:7;s:19:"Systematic Sampling";i:8;s:19:"Stratified Sampling";i:9;s:16:"Cluster Sampling";i:10;s:30:"Variables & Data Distributions";i:11;s:18:"Types of Variables";i:12;s:37:"Quantitative vs Qualitative Variables";i:13;s:34:"Frequency and Cumulative Frequency";i:14;s:21:"Measures of Frequency";i:15;s:28:"Measures of Central Tendency";i:16;s:35:"Measures of Dispersion and Variance";i:17;s:30:"Z-Score and Standard Deviation";i:18;s:41:"Measures of Position or Data Distribution";i:19;s:47:"Quartile
Machine Learning
  • ML and DL";i:2;s:32:"Applications of Machine Learning";i:3;s:25:"Types of Machine Learning";i:4;s:41:"Supervised / Unsupervised / Reinforcement";i:5;s:28:"Parametric vs Non Parametric";i:6;s:28:"Introduction to Scikit-Learn";i:7;s:30:"Sample Dataset in Scikit-Learn";i:8;s:15:"Goodness of Fit";i:9;s:28:"Overfitting and Underfitting";i:10;s:22:"Bias-Variance Tradeoff";i:11;s:24:"L1 and L2 Regularization";i:12;s:15:"Imbalanced Data";}
  • a:13:{i:0;s:32:"Introduction to Machine Learning";i:1;s:32:"Difference Between AI
Cost and Loss Functions
  • a:5:{i:0;s:17:"Optimizers for ML";i:1;s:16:"Gradient Descent";i:2;s:18:"Evaluation Metrics";i:3;s:18:"Regression Metrics";i:4;s:22:"Classification Metrics";}
EDA and Data Wrangling
  • Label Encoding
  • Target Encoding";i:5;s:10:"Curriculum";}
  • a:6:{i:0;s:22:"Null Values Imputation";i:1;s:17:"Outlier Detection";i:2;s:44:"Univariate/ Bivariate/ Multivariate Analysis";i:3;s:18:"Encoding Technique";i:4;s:36:"OHE
Feature Scaling
  • a:2:{i:0;s:13:"Normalization";i:1;s:15:"Standardization";}
Feature Selection
  • a:3:{i:0;s:14:"Filter Methods";i:1;s:15:"Wrapper Methods";i:2;s:17:"Embedding Methods";}
Cross-Validation Techniques
  • a:8:{i:0;s:19:"Feature Engineering";i:1;s:15:"Model Selection";i:2;s:18:"Holdout Validation";i:3;s:23:"K-fold Cross Validation";i:4;s:17:"Stratified K-fold";i:5;s:21:"Hyperparameter Tuning";i:6;s:12:"GridSearchCV";i:7;s:18:"RandomizedSearchCV";}
Machine Learning Algorithms
  • a:5:{i:0;s:20:"Regression Algorithm";i:1;s:17:"Linear Regression";i:2;s:31:"Assumption of Linear Regression";i:3;s:32:"Limitations of Linear Regression";i:4;s:24:"Practical Implementation";}
Polynomial Regression
  • a:3:{i:0;s:35:"Assumption of Polynomial Regression";i:1;s:36:"Limitations of Polynomial Regression";i:2;s:24:"Practical Implementation";}
Ridge Regression
  • a:2:{i:0;s:16:"Lasso Regression";i:1;s:24:"Practical Implementation";}
Classification
  • a:1:{i:0;s:20:"Regression Algorithm";}
Logistic Regression
  • a:3:{i:0;s:33:"Assumption of Logistic Regression";i:1;s:34:"Limitations of Logistic Regression";i:2;s:24:"Practical Implementation";}
Decision Tree
  • a:3:{i:0;s:22:"Entropy and Gini Index";i:1;s:16:"Information Gain";i:2;s:24:"Practical Implementation";}
Capstone Projects
  • a:0:{}
Advance House Price Prediction using Regression Algorithms
  • having arobust prediction model is crucial for both buyers and sellers to makeinformed decisions. This project employs advanced machine learningtechniques to enhance the accuracy and efficiency of house pricepredictions.";}
  • a:1:{i:0;s:431:"The Advanced House Price Prediction project leverages state-of-the-artregression algorithms to provide accurate and reliable predictions for realestate prices. In the dynamic and competitive real estate market
Netflix Movie Recommendation System using Content-BasedFiltering
  • Netflix recognizes the importance ofoffering tailored suggestions to users
  • ensuring they discover content thataligns with their preferences. This project utilizes content-basedrecommendation techniques to create a recommendation engine.";}
  • a:1:{i:0;s:472:"The Netflix Movie Recommendation System project aims to enhance userexperience by leveraging advancedmachine learning algorithms to providepersonalised and relevant movierecommendations. With an extensivelibraryof movies and TV shows

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