4.4/5
4.7/5
Who should enroll in Complete Python Tutorial?
What you will learn
- Importance of Python
- Describe the basics of the Python programming language
- Install Python and write your first program
- Use variables and constants to store, retrieve and calculate information
- Utilize core programming tools such as functions and loops
- Accomplish multi-step tasks like sorting or looping using tuples
- Create programs that are able to read and write data from files
- Store data as key/value pairs using Python dictionaries
- Start with basics of Object Oriented Programming
- Understanding class and object
- Encapsulation, abstraction, inheritance and polymorphism
- Using Scikit Learn in Machine Learning Techniques like Regression, Classification, Clustering, Recommender Systems
- Doing basic project in Python
- Select and process the data
- Process data using Graphs Library in Python
- Creating capstone project on Machine Learning using TensorFlow in Python
- Usage of Python Libraries
- Live Classes
- Machine Learning
Technologies you'll Master
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Who will teach?
Ishan Sharma
4.4 Reviews10+ Years of Industry Experience, Director at Perfect eLearning, ex. coke & nestle.
37 Topic
- WHY PYTHON
- Preparing your Machine (PC) for Python
- First Python Project & Assignment
- Constants, Reserved Words and Variables
- Constants in Python: Introduction, application and use cases
- Variable Reserved Words in Python: Introduction, application and use cases
- Data Types in Python
- Conversions in Python: Type and string conversions
- Taking user inputs in Python: Introduction and application
- Module Quiz: Constant Resever Words Variables
- Module Assignment : Expressions and User Input
- Module Assignment Solution: Expressions and User Input
- Conditions And Comparisons: Introduction and Use Cases
- Functions and Code Reuse in Python
- Module Quiz: Functions in Python
- Module Assignment: Functions in Python
- Module Assignment Solution: Functions in Python
- Understanding Functions in Python
- Understanding Functions with Arguments in Python
- Return Functions: Introduction and use cases
- Multiple Arguments in Python: Introduction and use cases
- Loops and Iterations in Python
- Module Quiz: Loops in Python
- Module Assignment: Loops in Python
- Module Assignment Solution: Loops in Python
- Loops in Python: Introduction
- Break Continue: Understanding and Use Cases
- FOR Loop: Understanding and Use Cases
- Demo: Loops Code Implmentation
- Loops and interation
- Demo: Mastering Loops with more Use Cases
- BONUS VIDEOS
- Live Classes: Python
- Introduction to Strings and Functions
- Introduction Video: Data Structures Course
- Introduction to Strings and LEN function
- String Looping: Introduction and Use Cases
- String Slicing: Introduction and Use Cases
- String Library Function: Introduction and Use Cases
- Stripping Whitespace: Introduction and Use Cases
- PARSING EXTRACTING
- Module Quiz: Strings in Python
- Module Assignment: Strings in Python
- Module Assignment Solution: Strings in Python
- Handling Files in Python
- Module Quiz: Files in Python
- Module Assignment: Handling files in Python
- Module Assignment Solution: Handling files in Python
- Introduction to Files in Python
- Counting Lines in a File in Python: Introduction and use cases
- Reading files in Python: Introduction and use cases
- Searching Lines in a File in Python: Introduction and use cases
- Naming Files with User Data: Introduction and use cases
- Lists in Python
- Moduel Quiz: Lists
- Module Assignment: Lists in Python
- Module Assignment Solution: Lists in Python
- Introduction to Lists in Python
- Finding Length of Range Objects in Python: len()
- Continuation of lists and their use cases
- Calculating Sum and Average of Lists in Python
- Spliting Strings in to Lists in Python: split()
- Dictionaries in Python
- Module Quiz: Dictionaries in Python
- Module Assignment: Dictionaries in Python
- Module Assignment Solution: Dictionaries in Python
- Understanding Dictionaries in Python
- Distinguishing Lists and Dictionaries in Python: Lists Vs. Dictionaries
- Demo: Understanding Get Function in Python
- Iterating over dictionaries with loops
- Tuples in Python
- Sets in Python
- Module Quiz: Sets in Python
- Module Assignment: Python Sets
- Uderstanding Sets in Python
- Creating Sets in Python
- Creating Empty Dictionaries in Python: Empty Set
- Understanding Add and Update in Sets
- Deleting Elements of Sets in Python
- Distinguishing Sets, Lists and Dictionaries in Python: Sets Vs. Lists Vs. Dictionaries
- Python Set Operations (Union, Intersection, Difference and Symmetric Difference)
- Understanding Python Set Methods
- BONUS VIDEOS
- Live Classes: Data Structures
- Object Oriented Programming: Introduction to OOPs
- Constructors in Python
- Encapsulation And Abstraction in Python
- Inheritance And Polymorphism in Python
- Module Quiz and Assignment
- Python Tutorials
- Project: Red Wine - Predicting Qualtiy of Wines
- Dealing with Missing Values in Python
- Understanding Machine Learning: Use Case Scenarios
- Project: Medical Cost Prediction
- Python Libraries Guides
- Matplotlib: Creating Static, Animated, and Interactive visualizations in Python
- Pandas: Analysing Data in Python
- *Args And **Kwargs: Passing a variable number of arguments to a Python function
- NumPy: Working with Arrays in Python
- Sets in Python
- Python OOPs Concepts
- Exploratory Data Analysis aka EDA on Red Wine Data - Part 2
- Exploratory Data Analysis aka EDA on Red Wine Data - Part 1
- Python Test Quiz
- Tutorial - Seaboar
- Python Projects - Stop Watch
- H2O Tutorial - Machine Learning Framework
- Various Types of Data Distribution
- Various Types of Errors in Machine Learning Models
- Machine Learning Introduction
- Regression in Python
- Introduction to Regression
- Various Regression Techniques: Linear, Polynomial, Stepwise, Ridge, Lasso and ElasticNet
- Linear Regression using Python
- Linear Regression Techniques: Slopes And Intercept
- Linear Regression using SKLEARN Coding
- Demo Machine Code: Linear Regression using SKLEARN Coding
- Demo Code Explaination: Linear Regression using SKLEARN Coding
- Errors in Linear Techniques
- R-Squared or Coefficient of Determination
- Multiple Linear Regression (MLR)
- Polynomial Vs. Non Linear Regression
- Module Quiz: Regression in Python
- Machine Learning with Python: Classification
- Introduction to Classification
- K-Nearest Neighbor(KNN) Algorithm for Machine Learning Introduction
- Working with K-Nearest Neighbor(KNN) Algorithm for Machine Learning
- Evaluation Metrics for Classification
- Machine Learning Decision Tree Classification Algorithm
- Support Vector Machine (SVM) Algorithm in Machine Learning
- Machine Learning Random Forest Algorithm
- Module Quiz: Classification
- Clustering or Cluster Analysis in Machine Learning
- Recommender Systems in Machine Learning
- Python Project 1( Beginner Level): Building Your Own Guess Game
- Python Project 2 ( Intermediate Level): Playing with Graphs (Data Science Project)
- Python Project 3 (Advanced Level): Machine Learning (Deep Learning Project With Tensorflow)
- Live Class: Machine Learning
- Python Capstone Projects
- Machine Learning Workflow Optimization Techniques
- Understanding Machine Learning Workflow
- Decoding Model Selection for Machine Learning
- Understanding Overfitting in Machine Learning
- Understanding Underfitting in Machine Learning
- Understanding the Bias-Variance Tradeoff
- Introduction to Dimensionality Reduction for Machine Learning
- Gradient Descent algorithm and its Variants
- Machine Learning Model Deployment
- Developer Showcase: GitHub
- Building Professional Resumes
