Complete Python Course with 100% Placement(Bundle)
Python gives tremendous versatility, giving Python developers the freedom to enter a wide range of applications from software development, machine learning, data science, and web development. Learn complete Python course today and give your career a new dimension.-
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4.7/5
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Who should enroll in Complete Python Course with 100% Placement?
Students, Developers, Computer scientists, IT Industry Professionals, Data Scientists, Machine Learning Engineers, Data Analysts and professionals using Python for their tasks can enroll in this Course.
Why Should You Learn This course?
-
Beginner Friendly
Concise and easy to understand syntax -
Versatile
Over 125,000 Libraries available -
In High-Demand
Fastest growing Programming Language
Hiring Company
What you will learn
- Why Python is Important
- Transition from Python2 to Python3
- Installation of Python
- How to use your machine to code online
- Build your 1st Python code
- Constants, Reserved Words, & Variables
- Data Types in Python
- Using Conditions in Python
- Using Comparisons in Python
- Functions in Python
- Loops in Python
- Iterations in Python
- Introduction to Data Structure
- Strings & Functions
- Handling files in Python
- List & Dictionaries
- Sets & Tuples
- Constructors
- Inheritance & Polymorphism
- Regressions in Python
- Clustering and Cluster Analysis
- Linear Regression using Python
- Multiple Linear Regression
- Evaluation Metrics For Classification
- Machine Learning random forest algorithm
- Clustering algorithms in Machine Learning
- Clustering vs Classification
- Recommender system in Machine Learning
- Machine Learning Projects
- Decoding Model selection for Machine Learning
- Machine Learning Model Deployment using Flask
Technologies you'll Master
We have designed this course as per the Industry requirements. In this course, you will gain practical experience and learn various tools based on the most recent technologies, such as: Numpy, Seaborn, Pandas, Matplotlib, TensorFlow, Flask and much more….
Project
This project refers to detect whether a person is wearing a mask or not.
In this project we will use tensorflow framework created by Google for creating Deep Learning models.
In this project we build our own plagiarism checker to search vast database for stolen content.
In this Project we build a chatbot program that simulates human conversation or "chatter" through text or voice interactions.
This project aimed to predict breast cancer using different machine-learning approaches.
This project takes parameters such as temperature, humidity, and wind to forecast weather.
This project use a set of different strategies and algorithms to help users find the most relevant films.
This project will apply Machine Learning algorithm and Linear Regression to predict Healthcare Insurance costs.
This project refers to detect whether a person is wearing a mask or not.
In this project we will use tensorflow framework created by Google for creating Deep Learning models.
In this project we build our own plagiarism checker to search vast database for stolen content.
In this Project we build a chatbot program that simulates human conversation or "chatter" through text or voice interactions.
This project aimed to predict breast cancer using different machine-learning approaches.
This project takes parameters such as temperature, humidity, and wind to forecast weather.
This project use a set of different strategies and algorithms to help users find the most relevant films.
This project will apply Machine Learning algorithm and Linear Regression to predict Healthcare Insurance costs.
Course Journey
Phase 1
Video lectures and live classes
Dive into 100+ video lectures to build a strong foundation starting from the very basics to advanced levels. 24/7 learning support from industry experts to clear up any conceptual doubts.Phase 2
Focus on Live Projects
Capstone projects using real-world data sets with virtual labs for practical training.
Phase 3
Resume building, Mock interviews
Learn to make ATS-proof Latex Resume to get in eyes of the Recruiter easily. Get trained by experts, clear mock tests and Comp-coding rounds to get ready for Interviews.Phase 4
Internship & Placement
Get the required pre job experience to make your profile stand out. Make your dream of working in the tech company of your choices true with 100% assured placements.
Program outcomes
- In-depth knowledge in data analytics and data Visualisation..
- Good Command over Data Structure and algorithms, Machine learning,web scraping, and natural language processing..
- Mastery in essential concepts of data types, tuples, lists, dicts, basic operators, and functions..
- Hands on experience on Various live Projects..
- ATS Proof Latex Resume building and career-ready Skills..
- 100% Assured Internship and Placement..
Who will teach?
Jyoti Sharma
4.4 ReviewsOK Google Developer
B.Tech & M.Tech Computer Science at NIT Kurukshetra (Department Ranker 1, CGPA: 9.8) with experience in Android App Development and Machine Learning. I am highly enthusiastic about learning and teaching cutting-edge technologies. My interests include Teaching, Data Structures, Algorithms, and Problem Solving. Worked at Infoedge(Naukri), Ok Google, and got offers from various tech companies including Adobe, UrbanCompany, etc, I would love to create more developers by imparting what I know.
9 Topic
- Why Python
- Preparing your Machine (PC) for Python
- First Python Project & Assignment
- Constants, Reserved Words and Variables
- Module Assignment Solutions: Expressions and User
- 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
- Constants Reserved Words Variables Assignment
- Module Assignment Solution: Expressions and User inputs
- Conditions And Comparisons: Introduction and Use Cases
- Functions and Code Reuse 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
- Module Quiz: Functions in Python
- Module Assignment: Functions in Python
- Module Assignment Solution: Functions in Python
- Loops and Iterations 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
- Module Quiz: Loops in Python
- Module Assignment: Loops in Python
- Module Assignment Solution: Loops in Python
- BONUS VIDEOS
- Live Classes: Python
8 Topic
- Introduction to Strings and Functions
- Introduction Video: Data Structures Course Preview
- Introduction to Strings and LEN function Preview
- 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
- 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
- Module Quiz: Files in Python
- Module Assignment: Handling files in Python
- Module Assignment Solution: Handling files in Python
- 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()
- Moduel Quiz: Lists
- Module Assignment: Lists in Python
- Module Assignment Solution: Lists in Python
- 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
- Module Quiz: Dictionaries in Python
- Module Assignment: Dictionaries in Python
- Module Assignment Solution: Dictionaries in Python
- Tuples in Python
- Sets in Python
- 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
- Module Quiz: Sets in Python
- Module Assignment: Python Sets
- BONUS VIDEOS
- Live Classes: Data Structures
6 Topic
- 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
- Python OOPs Concepts
- Python Seaborn Tutorial
- Sets in Python Tutorial
- H2O Tutorial : Machine Learning Framework
- Types of data distribution
- Erros in Machine Learning Models
- 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
- Red Wine Project
- Project - Stop Watch
13 Topic
- 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
- Linear Regression Demo using Scikit Lean Code Link
- 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
1 Topic
- Building Professional Resumes
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