Data Science & Machine Learning With Python

Description

Uncover the world of data science and machine learning with our comprehensive course. Dive into the foundations of Python programming, the preferred language of data professionals, and master the art of data analysis and predictive modeling.

In this hands-on program, you will explore the entire data science pipeline, from data collection and cleansing to advanced machine learning algorithms. With practical, real-world projects, you’ll learn to transform data into valuable insights, making informed decisions and predictions.

Join us to acquire the skills in high demand, and embark on a career where you can unlock the potential of data and shape the future. Whether you’re a beginner or an experienced professional, this course will empower you to harness the power of data science and machine learning with Python.

Languages and Tools Covered :

Why to choose AppliedTech :

4 months internship

This intensive Four-month internship

Portfolio

Resume +Github

Interview Prep

The Interview Prep

Program Eligibility Criteria and Prerequisites :

Course Syllabus

Python
SQL
Scientific Computation -Numpy
Working with Data- Pandas
Visualizing Data- Matplotlib,Seaborn
Probability
Statistics
Linear Algebra
Calculus
ML(Supervised,Unsupervised)
DL(Basics)
Flask
Streamlit
Git
Case study and Projects

Still have queries?

Course Syllabus In Detail :

1. Basics of Python Programming

Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
Assignments and Interview Questions

2. Python Data Types

Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
Session 5: Tuples + Set + Dictionary

- Tuple
- Operations on tuple
- Set functions
Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
Assignments and Interview Questions

3. Python Functions

Create functions.
Arguments and parameters
args and kwargs
map(), filter(), reduce()
Assignments and Interview Questions

4. Object Oriented Programming (OOP)

Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
Session 8: OOP Part2
- Encapsulation
- Collection of objects
Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
Session on Abstraction
- What is Abstraction?
- Abstract class
3 Interview Questions

5. Advanced Python

Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
Session 12: Decorators
- Decorators with Examples
Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
4 Interview Questions

6. Numpy

Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.

7. Pandas

Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.

8. Advanced Pandas

Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
Session 21: MultiIndex Series and DataFrames
Session on Pandas Case Study

9. Data Visualization

Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots

10. Data Visualization Continued

Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot

11. Data Preprocessing and Analysis Process

Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
Data Preprocessing steps.

12. SQL Basics

Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL

13. Descriptive Statistics

Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.

14. Probability Distributions

Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
  - Mathematical Transformation
  - Log Transform

- - Reciprocal Transform / Square or sqrt Transform
  - Power Transformer
  - Box-Cox Transform

15. Inferential Statistics

Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
Session 44: Confidence Intervals
- Confidence Interval
  - Ways to calculate CI
  - Applications of CI
  - Confidence Intervals in code

16. Hypothesis Testing

Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
  - Single sample t-Test
  - Independent 2-sample t-Test

- - Paired 2 sample t-Test
  - Code examples of all of the above

Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
Session on ANOVA
- F-distribution
- One/Two-way ANOVA

17. Linear Algebra

Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?

18. Linear Regression

Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
  - Supervised Machine Learning
  - Unsupervised Machine Learning
  - Semi supervised Machine Learning
  - Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
  - Data collection
  - Insufficient/Labelled data
  - Non-representative date
  - Poor quality data
  - Irrelevant features
  - Overfitting and Underfitting
  - Offline learning
  - Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
  - Why we need Polynomial Regression?
  - Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
  - What is multicollinearity?
  - How to detect and remove Multicollinearity
  - Correlation
  - VIF (Variance Inflation Factor)

19. Gradient Descent

Session 51: Gradient descent from scratch
- What is Gradient Descent?
- Intuition
- Mathematical Formulation
- Effect of Learning Rate
- Adding m into the equation
- Effect of Loss function
Session 52 (part 1): Batch Gradient Descent
- Types of Gradient Descent
- Mathematical formulation
Session 52 (part 2): Stochastic Gradient Descent
- Problems with Batch GD
- Stochastic GD
- Visualization
- When to use stochastic GD

20. Regularization

Session on Regularization Part 1 | Bias-Variance Tradeoff
- Why we need to study Bias and Variance
- Expected Value and Variance
- Bias and Variance Mathematically
Session on Regularization Part 1 | What is Regularization
- What is Regularization?
- When to use Regularization?
Ridge Regression and Lasso Regression
- Intuition and example

21. K Nearest Neighbors

Session on K nearest Neighbors Part 1
- KNN intuition
- How to select K?
- Overfitting and Underfitting in KNN
- Limitations of KNN
Classification Metrics Part 1
- Accuracy
- How much accuracy is good?
- Problem with accuracy
- Confusion matrix
- Type 1 error ,Type 2 error
- Confusion matrix of multi-classification problems
- When accuracy is misleading
Classification Metrics Part 2
- Precision
- Recall
- F1 score

22. PCA

Session on Curse of Dimensionality
PCA
- Introduction
- Geometric Intuition of PCA
- Why is Variance important?
- What is covariance and covariance matrix?
- EigenVectors and Eigenvalues
- Step by step solution of PCA
- How to transform points?
- PCA step-by-step code in python

23. Model Evaluation & Selection

ROC Curve in Machine Learning
- ROC AUC Curve and it’s requirements
- Confusion matrix
- True Positive Rate (TPR)
- False Positive Rate (FPR)
- Different cases of TPR & FPR
Session on Cross Validation
- Why do we need Cross Validation?
- Hold-out approach
- Problem with Hold-out approach
- Why is the Hold-out approach used?
  - Leave One Out Cross Validation (LOOCV)
    1. Advantages
    2. Disadvantages
    3. When to use
  - K-Fold Cross Validation
    1. Advantages
    2. Disadvantages
    3. When to use
  - Stratified K-Fold CV
Session on Hyperparameter Tuning
- Parameter vs Hyperparameter
- Why the word “hyper” in the term
- Requirements
  - Grid Search CV
  - Randomized Search CV
- Can this be improved?

24. Naive Bayse

Crash course on Probability Part
- 5 important terms in Probability
  - Random Experiment
  - Trials
  - Outcome
  - Sample Space
  - Event
- Some examples of these terms
- Types of events
- What is probability
- Random variable
- Probability distribution of random variable
Crash course on Probability Part 2
- Conditional probability
- Intuition of Conditional Probability
- Bayes Theorem
Session 1 on Naive Bayes
- Intuition
- Mathematical formulation
- Naive Bayse on Textual data
Session 2 on Naive Bayes

25. Logistics Regression

Session 1 on Logistic Regression
- Some Basic Geometry
- Sigmoid Function
- Maximum Likelihood
- Log Loss
Session on Multiclass Classification using Logistic Regression
- What is Multiclass Classification
- How Logistic Regression handles Multiclass Classification Problems.
- One vs Rest (OVR) Approach
  - Intuition
  - Code
- Assumptions of Logistics Regression
Logistic Regression Hyperparameters

26. Support Vector Machines (SVM)

SVM Part 1 – Hard Margin SVM
- Introduction
- Maximum Margin Classifier
- Problems with Hard Margin SVM
- Soft Margin SVM
- Introduction of C
- Kernel’s Intuition
- Types of Kernels
Session on Maths Behind SVM Kernels
- SVM Dual Formulation
- The Similarity Perspective
- Kernel SVM

27. Feature Engineering

Session on Handling Missing Values Part – 1
- Feature SelectionFeature Engineering
  - Feature Transformation
  - Feature Selection
- Types of Missing Values
  - Missing Completely at random
  - Missing at Random
  - Missing Not at Random
- Techniques for Handling Missing Values
  - Removing Missing Values
  - Imputation
Session 2 on Handling Missing Data
- Univariate Imputation – Numerical Data
  - Mean/ Median Imputation
- Univariate Imputation – Categorical Data
  - Mode Imputation
  - Missing Category Imputation
Session 3 on Handling Missing Data – Multivariate Imputation
- KNN Imputer
  - Steps in KNN Imputation
  - Advantages and Disadvantages in KNN Imputation

28. Decision Trees

Session 1 on Decision Tree
- Introduction -Intuition behind DT
- Terminology in Decision Tree
- The CART Algorithm – Classification/ Regression
- Geometric Intuition of CART
- Advantages & Disadvantages of DT
Session 3 on Decision Tree
- Feature Importance
- Why Overfitting happens
- Pruning & its types
  - Pre-pruning
  - Post Pruning
- Cost Complexity Pruning and dtree viz

29. Ensemble Methods - Bagging

Introduction to Ensemble Learning
- Intuition
- Why use Bagging
- Introduction to Random Forest
- Feature Importance
Random Forest : Session 2
- Why Ensemble Techniques work?
- Random Forest Hyperparameters
- Advantages and Disadvantages of Random Forest

30. Gradient Boosting

Gradient Boosting : Session 1
- Boosting
- What is Gradient Boosting
- How Gradient Boosting works?
- Difference between Gradient Boosting and Gradient Descent
- Classification vs. Regression
- Prediction

31. Clustering

Clustering and their types K-Means Clustering
K-Means++ Batch K-Means
Hierarchical Clustering DBSCAN

32. Capstone Project: Regression /Classification Project(1 Each)

Project overview in details
Data Cleaning
Feature Engineering
EDA (Exploratory Data Analysis)
Outlier Detection and Removal
Missing Value Imputation
Feature Selection
Model Evaluating
Model Selection
Prediction Web Interface – Streamlit
Deploying the application on AWS

33. GIT

What is Git?
What is VCS/SCM?
Why Git/VCS is needed?
Types of VCS
How Git works?
Installing git
Creating and Cloning repo
add, commit, add ., gitignore
seeing commits (log -> oneline)
Session on Git and GitHub:
Merging branches
Undoing changes
Working with a remote repo

34. Flask

Session on Web Development using Flask
- What is Flask library
- Why to use Flask?
- What is API?
- Building API using Flask
- Building login system with Rest API

35. Streamlit

Session on Tensors | Linear Algebra part 1(a)
- Session on Streamlit
- Introduction to Streamlit
- Features of Streamlit
- Benefits of Streamlit
- Flask vs Streamlit

Course Syllabus In Detail :

Basics of Python Programming

Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
Assignments and Interview Questions

Python Data Types

Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
Session 5: Tuples + Set + Dictionary

- Tuple
- Operations on tuple
- Set functions
Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
Assignments and Interview Questions

Python Functions

Create functions.
Arguments and parameters
args and kwargs
map(), filter(), reduce()
Assignments and Interview Questions

Object Oriented Programming (OOP)

Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
Session 8: OOP Part2
- Encapsulation
- Collection of objects
Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
Session on Abstraction
- What is Abstraction?
- Abstract class
3 Interview Questions

Advanced Python

Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
Session 12: Decorators
- Decorators with Examples
Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
4 Interview Questions

Numpy

Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.

Pandas

Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.

Advanced Pandas

Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
Session 21: MultiIndex Series and DataFrames
Session on Pandas Case Study

Data Visualization

Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots

Data Visualization Continued

Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot

Data Preprocessing and Analysis Process

Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
Data Preprocessing steps.

SQL Basics

Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL

Descriptive Statistics

Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.

Probability Distributions

Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
  - Mathematical Transformation
  - Log Transform

- - Reciprocal Transform / Square or sqrt Transform
  - Power Transformer
  - Box-Cox Transform

Inferential Statistics

Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
Session 44: Confidence Intervals
- Confidence Interval
  - Ways to calculate CI
  - Applications of CI
  - Confidence Intervals in code

Hypothesis Testing

Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
  - Single sample t-Test
  - Independent 2-sample t-Test

- - Paired 2 sample t-Test
  - Code examples of all of the above

Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
Session on ANOVA
- F-distribution
- One/Two-way ANOVA

Linear Algebra

Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?

Linear Regression

Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
  - Supervised Machine Learning
  - Unsupervised Machine Learning
  - Semi supervised Machine Learning
  - Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
  - Data collection
  - Insufficient/Labelled data
  - Non-representative date
  - Poor quality data
  - Irrelevant features
  - Overfitting and Underfitting
  - Offline learning
  - Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
  - Why we need Polynomial Regression?
  - Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
  - What is multicollinearity?
  - How to detect and remove Multicollinearity
  - Correlation
  - VIF (Variance Inflation Factor)

Basics of Python Programming

Session 1: Python Basics
- About Python
- Python Data Types
- Python Variables
- Python comments
- Python Keywords and Identifiers
- Python User Input
- Python Type conversion
- Python Literals
Session 2: Python Operators + if-else + Loops
- Python Operators
- Python if-else
- Python While Loop
- Python for loop
- Break, continue, pass statement in loops
Session 3: Python Strings
- String indexing
- String slicing
- Common String functions
Assignments and Interview Questions

Python Data Types

Session 4: Python Lists
- Array vs List
- How lists are stored in a memory
- All Operations on List
- List Functions
Session 5: Tuples + Set + Dictionary

- Tuple
- Operations on tuple
- Set functions
Session 6: Dictionary
- Operations on dictionary
- Dictionary functions
Assignments and Interview Questions

Python Functions

Create functions.
Arguments and parameters
args and kwargs
map(), filter(), reduce()
Assignments and Interview Questions

Object Oriented Programming (OOP)

Session 7: OOP Part1
- What is OOP?
- What are classes and Objects?
- Methods vs Functions
- Magic/Dunder methods
- What is the true benefit of constructor?
- Concept of ‘self’
- __str__, __add__, __sub__ , __mul__ , __truediv__
Session 8: OOP Part2
- Encapsulation
- Collection of objects
Session 9: OOP Part3
- Class Relationship
- Inheritance and Inheritance class diagram
- Constructor example
- Types of Inheritance (Single, Multilevel, Hierarchical,Multiple )
- Code example and diamond problem
- Polymorphism
- Method Overriding and Method Overloading
Session on Abstraction
- What is Abstraction?
- Abstract class
3 Interview Questions

Advanced Python

Session 10: File Handling + Serialization & Deserialization
- How File I/O is done
- Writing to a new text file
- append()
- Reading a file -> read() and readline()
- Seek and tell
- Working with Binary file
- Serialization and Deserialization
- JSON module -> dump() and load()
- Pickling
Session 11: Exception Handling
- Syntax/Runtime Error with Examples
- Why we need to handle Exception?
- Exception Handling (Try-Except-Else-Finally)
- Handling Specific Error
- Raise Exception
- Create custom Exception
- Exception Logging
Session 12: Decorators
- Decorators with Examples
Session on Generator
- What is a generator?
- Why to use Generator?
- Yield vs Return
4 Interview Questions

Numpy

Session 13: Numpy Fundamentals
- Numpy Theory
- Numpy array
- Matrix in numpy
- Array operations
- Scalar and Vector operations
Session 14: Advanced Numpy
- Numpy array vs Python List
- Broadcasting
- Mathematical operations in numpy
- Sigmoid in numpy
- Mean Squared Error in numpy
- Various functions like sort, append, concatenate, percentile, flip, Set functions, etc.

Pandas

Session 16: Pandas Series
- What is Pandas?
- Introduction to Pandas Series
- Series Methods
Session 17: Pandas DataFrame
- Introduction Pandas DataFrame
- Creating DataFrame and read_csv()
- Selecting cols and rows from dataframe
- Filtering a Dataframe
- Adding new columns
Session 18: Important DataFrame Methods
- Sort, index, reset_index, isnull, dropna, fillna, drop_duplicates, value_counts, apply etc.

Advanced Pandas

Session 19: GroupBy Object
- What is GroupBy?
- Applying builtin aggregation fuctions on groupby objects
Session 20: Merging, Joining, Concatenating
- Pandas concat method
- Merge and join methods
- Practical implementations
Session 21: MultiIndex Series and DataFrames
Session on Pandas Case Study

Data Visualization

Session 23: Plotting Using Matplotlib
- Get started with Matplotlib
- Plotting simple functions, labels, legends, multiple plots
- About scatter plots
- Bar chart
- Histogram
- Pie chart
- Changing styles of plots

Data Visualization Continued

Session 25: Plotting Using Seaborn
- Why seaborn?
- Categorical Plots
- Stripplot
- Swarmplot
- Categorical Distribution Plots
- Boxplot
- Violinplot
- Barplot

Data Preprocessing and Analysis Process

Session on Data Cleaning and Data Preprocessing Case Study
- Quality issues
- Tidiness issues
- Data Cleaning
Session 29: Exploratory Data Analysis (EDA)
- Introduction to EDA
- Why EDA?
- Steps for EDA
- Univariate, Bivariate Analysis
- Feature Engineering
Data Preprocessing steps.

SQL Basics

Session 30: Database Fundamentals
- Introduction to Data and Database
- CRUD operations
- Types of Database
- MySQL workbench
- DDL ,DML ,DQL ,DCL Commands
- Selecting & Retrieving Data with SQL
- Filtering, Sorting, and Calculating Data with SQL
- Sub Queries and Joins in SQL

Descriptive Statistics

Session 38: Descriptive Statistics Part 1
- What is Statistics?
- Types of Statistics
- Population vs Sample
- Types of Data
- Measures of central tendency
- Measure of Dispersion
- Quantiles and Percentiles
- Five Number Summary
- Boxplots
- Scatterplots
- Covariance
- Correlation
Probability Distribution Functions (PDF, CDF, PMF)
- Random Variables
- Probability Distributions
- Probability Distribution Functions and its types
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF) of PMF
- Probability Density Function (PDF)
- Density Estimation
- Parametric and Non-parametric Density Estimation
- Kernel Density Estimate (KDE)
- Cumulative Distribution Function (CDF) of PDF.

Probability Distributions

Session 41: Normal Distribution
- How to use PDF in Data Science?
- 2D density plots
- Normal Distribution (importance, equation, parameter, intuition)
- Standard Normal Variate (importance, z-table, empirical rule)
- Skewness
- Use of Normal Distribution in Data Science
Session 42: Non-Gaussian Probability Distributions
- Kurtosis and Types
- Transformation
  - Mathematical Transformation
  - Log Transform

- - Reciprocal Transform / Square or sqrt Transform
  - Power Transformer
  - Box-Cox Transform

Inferential Statistics

Session 43: Central Limit Theorem
- Bernouli Distribution
- Binomial Distribution
- Intuition of Central Limit Theorem (CLT)
- CLT in code
Session 44: Confidence Intervals
- Confidence Interval
  - Ways to calculate CI
  - Applications of CI
  - Confidence Intervals in code

Hypothesis Testing

Session 45: Hypothesis Testing (Part 1)
- Key idea of hypothesis testing
- Null and alternate hypothesis
- Steps in Hypothesis testing
- Performing z-test
- Rejection region and Significance level
- Type-1 error and Type-2 Error
- One tailed vs. two tailed test
- Applications of Hypothesis Testing
- Hypothesis Testing in Machine Learning
Session 46: Hypothesis Testing (Part 2) | p-value and t-tests
- What is p-value?
- Interpreting p-value
- T-test
- Types of t-test
  - Single sample t-Test
  - Independent 2-sample t-Test

- - Paired 2 sample t-Test
  - Code examples of all of the above

Session on Chi-square test
- Chi-square test
- Goodness of fit test (Steps, Assumptions, Examples)
- Test for Independence (Steps, Assumptions, Examples)
- Applications in machine learning
Session on ANOVA
- F-distribution
- One/Two-way ANOVA

Linear Algebra

Session on Tensors | Linear Algebra part 1(a)
- What are tensors?
- 0D, 1D and 2D Tensors
- Nd tensors
- Example of 1D, 2D, 3D, 4D, 5D tensors
Session on Vectors | Linear Algebra part 1(b)
- What is Linear Algebra?
- What are Vectors?
- Vector example in ML
- Row and Column vector
- Distance from Origin
- Euclidean Distance
- Scalar Addition/Subtraction (Shifting)
- Vector Addition/Subtraction
- Dot product
- Angle between 2 vectors
Linear Algebra Part 2 | Matrices (computation)
- What are matrices?
- Types of Matrices
- Matrix Equality
- Scalar Operation
- Matrix Addition, Subtraction, multiplication
- Transpose of a Matrix
- Determinant
- Inverse of Matrix
Linear Algebra Part 3 | Matrices (Intuition)
- Basis vector
- Linear Transformations
- Linear Transformation in 3D
- Matrix Multiplication as Composition
- Determinant and Inverse
- Transformation for non-square matrix?

Linear Regression

Session 48: Introduction to Machine Learning
- About Machine Learning (History and Definition)
- Types of ML
  - Supervised Machine Learning
  - Unsupervised Machine Learning
  - Semi supervised Machine Learning
  - Reinforcement Learning
- Batch/Offline Machine Learning
- Instance based learning
- model-based learning
- Instance vs model-based learning
- Challenges in ML
  - Data collection
  - Insufficient/Labelled data
  - Non-representative date
  - Poor quality data
  - Irrelevant features
  - Overfitting and Underfitting
  - Offline learning
  - Cost
- Machine Learning Development Life-cycle
- Different Job roles in Data Science
- Framing a ML problem | How to plan a Data Science project
Session 49: Simple Linear regression
- Introduction and Types of Linear Regression
- Intuition of simple linear regression
- How to find m and b?
- Regression Metrics
- MAE, MSE, RMSE, R2 score, Adjusted R2 score
Session 50: Multiple Linear Regression
- Introduction to Multiple Linear Regression (MLR)
- Mathematical Formulation of MLR
- Error function of MLR
- Session on Polynomial Regression
  - Why we need Polynomial Regression?
  - Formulation of Polynomial Regression
- Session on Assumptions of Linear Regression
- Session 53: Multicollinearity
  - What is multicollinearity?
  - How to detect and remove Multicollinearity
  - Correlation
  - VIF (Variance Inflation Factor)

Skills Covered

Python Programming

Data Analysis

Data Visualization

Statistics

Machine Learning

Model Evaluation

Feature Engineering

Big Data Tools

Natural Language Processing (NLP)

Deep Learning

Time Series Analysis

Model Deployment

Data Ethics and Privacy

Real-World Projects

Collaboration and Communication

Version Control

Testimonials

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