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- Course Curriculum
Quick Facts
| particular | details | |||
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 385 ₹2,240
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Course Overview
Introduction To Jupyter Notebook
- Jupyter Notebook: Getting Started With Anaconda
- Jupyter Notebook: Code Vs Markdown Vs Raw
- Jupyter Notebook: Working With Text
- Jupyter Notebook: Working With Code(Saving & Exporting Notebook)
Introduction To Google Colab
- Google Colab: Overview
- Google Colab: Working With Text
- Google Colab: Saving & Exporting Notebook
- Guide To Setup Jupyter Notebook(Anaconda) and Google Colab
- Loading Dataset In Colab
Python For Data Science And Machine Learning
- Lecture Resources
- Introduction to Python
- Data Types In Python
- List In Python
- Operations On Python List-Split,Count,Index, Reverse,Append,Remove,Pop,Sort,Copy
- Range in Python
- List Comprehension In Python
- Dataframe In Python
- SET In Python
- Dictionary In Python
- Functions In Python
- Conditional Statements In Python
Numpy
- Introduction To Numpy
- Operations On Numpy
- Indexing And Slicing 1
- Indexing And Slicing 2
- Numpy Assignment
Pandas
- Pandas Dataframe 1: Series
- Iloc and Loc For Pandas Dataframe
- Pandas Dataframe
- Operations On Pandas Dataframe: Slicing Columns, Adding/Drop Columns and Rows
- Operations On Pandas DataFrame: Setting Column Index, Rename DataFrame Columns
- Reading Dataset In Pamdas
- Working With Dataset: Checking for Missing/Null Values, Head of dataset,Describe
- Working With Dataset: Fillna, Sorting,Value_Counts,Concatenation,Join&Merge
- Join And Merge In Pandas
- Pandas Exercise
Data Visualisation: MatplotIib And Seaborn
- Data Visualisation: Matplotlib and Seaborn
- Data Visualisation 2
- Data Visualisation 3
- Univariate Data Analysis
- Bivariate And Multivariate Data Analysis
- Data Visualisation Assignment
Python Assignment
- Python Assignment
Statistics For Data Science And Machine Learning
- Lecture Resources
- Overview
- Statistical Methods
- Data Types
- Data Sources | Common Statistical Terms
- Frequency Distribution
- Central Tendency (Mean, Median, Mode)
- Measure Of Dispersion
- The Five(5) Number Summary
- Correlation
- Probability
- Baye's Theorem
- Normal Distribution-Empirical Rule | Central Limit Theorem | Skewness
- Hypothesis Testing
- Statistics Exercise
Machine Learning
- Lecture Resources
- Introduction
- Applications Of Machine Learning
Supervised Machine Learning
- Supervised Machine Learning
- Difference Between Regression And Correlation
Linear Regression
- Linear Regression
- Lab Session 1: Exploratory Data Analysis(EDA)
- Lab Session 2: EDA| Dealing With Categorical And Missing Values
- Lab Session 3: Building Linear Regression Model
- Linear Regression Assignment
Logistic Regression
- Introduction 1
- Introduction 2
- Lab Session 1: Exploratory Data Analysis(EDA)
- Lab Session 2: Exploratory Data Analysis(EDA)
- Lab Session 3: Building Logistic Regression Model
- Logistic Regression Assignment
Naive Bayes Classifier
- Introduction
- Lab Session
K-Nearest Neighbor (KNN)
- Introduction
- KNN Distance Measures
- Lab Session
- Lab Session: Building KNN Model
- Choosing K In K-NN
- KNN Assignment
Support Vector Machine (SVM)
- Introduction
- Dealing With Linearly Inseparable Points
- Lab Session 1: Exploratory Data Analysis(EDA)
- Lab Session 2: Building A Support Vector Machine (SVM) Model
Unsupervised Machine Learning: Clustering
- Difference Between KNN and K-Means Algorithms
K-Means Clustering
- What Is K-Means Clustering?
- Lab Session 1: Exploratory Data Analysis(EDA)
- Choosing K in K-Means-The Elbow Method
Hierarchical Clustering
- Introduction
- Dendrograms And Cophenetic correlation
- Lab Session
Unsupervised Learning Project
- Assignments for Unsupervised Machine learning
Hands-On Project
- Introduction
Breast Cancer Detection Using SVM And KNN
- Lecture Resources
- Introduction
- Summary Of Dataset
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Model Building
Bank Note Analysis
- Introduction
- Lecture Resources
- Bank Note Analysis-EDA
- Bank Note Analysis-Getting Our Dataset Ready For Model Building
- Model Building 1
- Model Building 2
- Model Building 3
Predicting Compressive Strength of Concrete-(Compare performance of 18 models)
- Lecture Resources
- Introduction
- Data Preprocessing
- Exploratory Data Analysis (EDA) 1
- Exploratory Data Analysis (EDA) 2
- Feature Engineering
- Model Building 1
- Model Building 2
Credit Card Fraud Detection-(Dealing With Data Imbalance)
- Introduction
- Exploratory Data Analysis (EDA)
- Model Building 1
- Model Building 2
Stock Market Clustering Using K-Means Algorithm
- Lecture Resources
- Introduction
- Data Extraction And Analysis
- Model Building
BigMart Sales Prediction
- Lecture Resources
- Introduction
- Exploratory Data Analysis (EDA)
- Feature Engineering, Selection And Transformation
- Model Building
Amazon Employee Access Challenge
- Lecture Resources
- Introduction
- Exploratory Data Analysis (EDA)
- Model Building 1
- Model Building 2
Project Assignment
- Based on the given data, will a customer subscribe to a term deposit or not?
Streamlit Project
- Lecture Resources
- Streamlit Demo
- Introduction to Streamlit 1
- Introduction to Streamlit 2
- Introduction to Streamlit 3
- Building Your First Streamlit App
- Building Your First Streamlit App-Advance 1
- Building Your First Streamlit App-Advance 2
- Building Your First Streamlit App-Advance 3
- Project Assignment
Flask Tutorial
- Introduction To Flask
- Create Your First Flask App
- Create Your First Flask App: Linking HTML File
- Create Your First Flask App: Linking CSS File
Flask PROJECT and DEPLOYMENT
- Lecture Resources
- Flask Introduction
- Introduction to Dataset
- Exploratory Data Analysis (EDA)
- Model Building
- Hands-On With Flask
- Creating The Necessary Folders
- Creating Folder Contents
- Final Deployment
- Project Assignment
Heroku Deployment
- Lecture Resources
- Heroku Introduction
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Further Data Preparation
- Model Building And Hyperparameter Tuning
- Heroku Deployment 1
- Heroku Deployment 2
- Project Assignment
Google Cloud Deployment
- Lecture Resources
- Google Cloud Introduction
- GCP Deployment Lesson
- Project Assignment
Amazon Web Service(AWS) Deployment
- Lecture Resources
- AWS Introduction
- Dataset
- App.py File
- PART 1
- PART 2
- Project Assignment
Microsoft Azure Deployment
- Lecture Resources
- Azure Deployment
- Project Assignment
Final Project
- Introduction
- Final Machine Learning Project
Instructors
Articles
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