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Python for Aspiring Professionals

Description

Unlock the world of programming and technology with our Python for Aspiring Professionals course. Python is the ideal language for beginners and experienced learners alike, and this program serves as your gateway to the tech industry.

In this course, you’ll delve into the fundamentals of Python, learning its syntax and capabilities. You’ll also discover how to solve real-world problems, develop applications, and gain the problem-solving skills that are highly sought after in today’s job market.

Designed to be accessible to those with little to no prior coding experience, our course offers a supportive learning environment. We aim to empower aspiring professionals from diverse backgrounds to thrive in the ever-evolving tech landscape. Join us and take your first step towards a rewarding career in technology.

What you'll learn:

  • Build a complete understanding of Python programming from the ground up!
  • You will learn how to use the power of Python to solve tasks.
  • Learn to use Object Oriented Programming with classes!
  • Understand complex topics, like decorators.
  • Understand how to use both the Jupyter Notebook and create .py files
  • Build 2 Milestone projects in this Python course.

Who this course is for:

  • College Students
  • Non-IT and IT candidates.
  • Beginners who have never programmed before
  • Programmers switching languages to Python.
  • Intermediate Python programmers who want to level up their skills!

Course includes :

  • Integrated Development Environment (IDEs)
  • Version Control
  • Libraries - NumPy/ Pandas / Matplotlib and Seaborn / Requests / Beautiful Soup / SQLite / Tkinter.
  • Web Framework
  • Machine Learning and Data Analysis

Libraries :

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 :

  • No programming experience needed
  • All tools used in this course are free for you to use.
  • Internet,Laptop/PC
  • We start from the very basics

Course Syllabus

  • Python Basics
  • Python Data Types
  • Object Oriented Programming (OOP)
  • Advanced Python
  • Modules and Packages
  • Regular Expressions
  • Working with APIs (Application Programming Interfaces)
  • Database Interaction
  • Exception Handling
  • Web Development (using frameworks Flask)
  • 2- Milestone Projects in python.

Still have queries?

Course Syllabus In Detail :

  • About Python
  • Python Output/print function
  •  Python Data Types
  •  Python Variables
  • Python comments
  • Python Keywords and Identifiers
  • Python User Input
  • Python Type conversion
  • Python Literals
  • Start of the session
  • Python Operators
  • Python if-else
  • Python Modules
  • Python While Loop
  • Python for loop
  • Introduction
  • Solving Loop problems
  • Break, continue, pass statement in loops
  • Strings
  • String indexing
  • String slicing
  • Edit and delete a string
  • Operations on String
  • Common String functions
  • Introduction
  •  Array vs List
  • How lists are stored in a memory
  • Characteristics of Python List
  • Code Example of Lists
    • Create and access a list
    • append(), extend(), insert()
    • Edit items in a list
    • Deleting items from a list
    • Arithmetic, membership and loop operations on a List
    • Various List functions
    • List comprehension
    • 2 Ways to traverse a list
    • Zip() function
    • Python List can store any kind of objects
  • Disadvantages of Python list
  • Tuple
    • Create and access a tuple
    • Can we edit and add items to a tuple?
    • Deletion
    • Operations on tuple
    • Tuple functions
    • List vs tuple
    • Tuple unpacking
    • Zip () on tuple
  • Set
    • Create and access a set
    • Can we edit and add items to a set?
    • Deletion
    • Operations on set
    • set functions
    • Frozen set (immutable set)
    • Set comprehension
  • Dictionary
    • Create dictionary
    • Accessing items
    • Add, remove, edit key-value pairs
    • Operations on dictionary
    • Dictionary functions
    • Dictionary comprehensio
    • Zip() on dictionary
    • Nested comprehension
  • Create function
  • Arguments and parameters
  • args and kwargs
  • How to access documentation of a function
  • How functions are executed in a memory
  • Variable scope
  • Nested functions with examples
  • Functions are first class citizens
  • Deletion of function
  • Returning of function
  • Advantages of functions
  • Lambda functions
  • Higher order functions
  • map(), filter(), reduce()
  • What are classes and Objects?
  • Banking application coding
  • Methods vs Functions
  • Class diagram
  • Magic/Dunder methods
  • What is the true benefit of constructor?
  • Concept of ‘self’
  • Create Fraction Class
  • __str__, __add__, __sub__ , __mul__ , __truediv__
  • How objects access attributes
  • Attribute creation from outside of the class
  • Reference Variables
  • Mutability of Object
  • Encapsulation
  • Collection of objects
  • Static variables and methods
  • 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
  • Operator Overloading
  • What is Abstraction?
  • Bank Example Hierarchy
  • Abstract class
  • Coding abstract class (BankApp Class)
  • How File I/O is done
  • Writing to a new text file
  • What is open()?
  • append()
  • Writing many line
  • Saving a file
  • Reading a file -> read() and readline()
  • Using context manager -> with()
  • Seek and tell
  • Working with Binary file
  • Serialization and Deserialization
  • JSON module -> dump() and load()
  • Serialization and Deserialization of tuple, nested dictionary and
    custom object
  • Pickling
  • Pickle vs JSON
  • Syntax Error with Examples
  • Exception with Examples
  • Why we need to handle Exception?
  • Exception Handling (Try-Except-Else-Finally)
  • Handling Specific Error
  • Raise Exception
  • Create custom Exception
  • Namespaces
  • Scope and LEGB rule
  • Hands-on local, enclosing, global and built-in scope
  • Decorators with Example
  • What are iterators
  • What are iterables
  • How for loop works in Python?
  • Making your own for loop
  • Create your own range function
  • What is a generator?
  • Why to use Generator?
  • Yield vs Return
  • Generator Expression
  • Practical Examples
  • Benefits of generator
  • Creating and using modules
  • Organizing code into packages
  • Pattern matching using regular expressions
  • Sending HTTP requests and processing responses
  • JSON data manipulation
  • Connecting to databases (SQLite, MySQL, etc.)
  • Performing CRUD operations (Create, Read, Update, Delete)
  • Building web application with Python

Course Syllabus In Detail :

  • 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
  • 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
  • Create functions.
  • Arguments and parameters
  • args and kwargs
  • map(), filter(), reduce()
  • Assignments and Interview Questions
  • 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
  • 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
  • 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.
  • 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.
  • 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
  • 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
  • Session 25: Plotting Using Seaborn
    • Why seaborn?
    • Categorical Plots
    • Stripplot
    • Swarmplot
    • Categorical Distribution Plots
    • Boxplot
    • Violinplot
    • Barplot
  • 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.
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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?
  • 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)
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    1.  
  • 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
  • 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
  • Create functions.
  • Arguments and parameters
  • args and kwargs
  • map(), filter(), reduce()
  • Assignments and Interview Questions
  • 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
  • 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
  • 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.
  • 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.
  • 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
  • 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
  • Session 25: Plotting Using Seaborn
    • Why seaborn?
    • Categorical Plots
    • Stripplot
    • Swarmplot
    • Categorical Distribution Plots
    • Boxplot
    • Violinplot
    • Barplot
  • 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.
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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?
  • 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)
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Testimonials

What they say

“My child joined the Python for GenNext course at AppliedTech, and I’m amazed at how quickly they’ve picked up coding and problem-solving skills. It’s not just a course; it’s an inspiring journey that’s fostering a love for technology from an early age.”
Prajakta S.Mother
"AppliedTech has been a game-changer in my career. The quality of education and the support I received were exceptional. Thanks to their expert trainers and hands-on learning, I now work in a cutting-edge tech job I’ve always dreamed of.”
Siddharth PandeStudent
"Swapnil is the best trainer I have ever seen. He is very calm and supportive."
Subhodh P. KaleStudent
"The trainer is very knowledgeable and passionate about the subject. Loved his teaching style."
Darshan PatilStudent
"I feel immensely grateful for being a part of this outstanding academic community that has empowered me to pursue my dreams and achieve success in my career."
Olivia J.Student
"I loved the way they take care of everything related to the course and support."
JiaStudent
"I learned Stats and ML and I liked the way sir taught me the concepts. Easy to understand."
Shriya Hegde.Student

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