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AI & Machine Learning with Python Course | With Generative AI

AI & Machine with Python Development Certificate Course

Looking for the best AI and Machine Learning course with Python? Our expert-designed program offers in-depth training in artificial intelligence, machine learning algorithms, and Python programming, customized for students, job seekers, and working professionals. With real-time projects, mentorship from industry experts, and career guidance, this course prepares you for high-demand roles in data science, AI, and automation.

  • 160+ learners
  • Beginners

    • Offline, Online

Get In Touch!
  • Job Assistance

    Program

  • 1st Week of Every Month

    Starts On

  • 140+

    Hours Of Learning

  • 2+

    Industry Relevant Projects

What You Will Learn in AI and Machine Learning Course with Python

  • Python programming
  • Data analysis and preprocessing
  • Supervised learning algorithms
  • Unsupervised learning algorithms
  • Model training and evaluation
  • Deep learning with TensorFlow and Keras
  • Neural networks (ANN, CNN, RNN)
  • Natural Language Processing (NLP)
  • Sentiment analysis and text classification
  • Computer vision with OpenCV
  • Real-world AI/ML projects
  • Capstone project
  • Model deployment using Flask or Django
  • Cloud deployment basics
  • Version control with Git and GitHub
Course Fees

WITH THIS COURSE, YOU GET

₹22,000
  • Certificate
    Completion Certificate

    Stand out to your professional network

  • Level
    Course Level

    Beginners

  • Schedule
    Course Duration

    2 Months

  • Languages
    Languages

    English, Hindi

  • Badge
    Skill Badge

    Yes on Job Babu

Enrolled Now
Bag
Roles After Completion

AI Engineer, Machine Learning Engineer, Data Scientist, Data Analyst, Python Developer, Deep Learning Engineer, NLP Engineer, Business Intelligence Analyst, AI Research Assistant, Computer Vision Engineer

Learning
Learning Mode

Offline, Online

Skill
Minimum Eligibility

Any

There are 9 modules in this course

Well-structured & comprehensive curriculum designed according to latest trends and industry standards!

  • Overview of Python: History and Applications
  • Setting up the Development Environment (Anaconda, Jupyter Notebooks)
  • Basic Syntax: Variables, Data Types, and Operators
  • Understand Python's role in data science and machine learning.
  • Set up a working Python environment.
  • Write simple Python scripts using basic data types.
  • Install Anaconda and Jupyter Notebook.
  • Hands-on exercises: Create and manipulate variables.

  • Control Structures: If statements, Loops (for, while)
  • Data Structures: Lists, Tuples, Dictionaries, Sets
  • Use control structures to control program flow.
  • Implement various data structures in Python.
  • Coding exercises to manipulate lists and dictionaries.

  • Defining Functions, Parameters, Return Values
  • Understanding Scope and Lifetime of Variables
  • Importing and Using Modules
  • Write reusable functions to improve code efficiency.
  • Utilize built-in Python modules.
  • Create a calculator program using functions.
  • Explore and use at least three built-in Python modules.

  • Reading from and Writing to Files (Text, CSV)
  • Exception Handling with Try/Except Blocks
  • Handle file operations in Python.
  • Implement error handling to manage exceptions.
  • Project: Write a program that reads a CSV file, processes the data, and outputs results.

  • Concepts: Classes, Objects, Inheritance, Polymorphism
  • Dunder Methods and Special Attributes
  • Apply OOP principles in Python to create modular and maintainable code.
  • Build a simple library management system using OOP concepts.

  • Introduction to NumPy: Arrays and Mathematical Functions
  • Introduction to Pandas: DataFrames and Data Manipulation
  • Use NumPy for numerical computations.
  • Manipulate and analyze data using Pandas.
  • Data cleaning and analysis project using a sample dataset.

  • Introduction to Matplotlib and Seaborn
  • Creating Different Types of Plots: Line, Bar, Histogram, Scatter
  • Visualize data effectively using Python libraries.
  • Project: Create visualizations for the dataset used in the previous module.

  • Overview of Machine Learning Concepts
  • Types of Machine Learning: Supervised vs. Unsupervised Learning
  • Understand core machine learning concepts and terminology.
  • Discussion and analysis of real-world ML applications.

  • Simple and Multiple Linear Regression
  • Understanding the Cost Function and Optimization
  • Implement linear regression models using Scikit-Learn.
  • Project: Build a linear regression model to predict house prices.

  • Logistic Regression, Decision Trees, K-Nearest Neighbors
  • Model Evaluation Metrics (Accuracy, Precision, Recall)
  • Implement classification algorithms and evaluate model performance.
  • Classification project using a well-known dataset (e.g., Iris dataset).

  • K-Means Clustering, Hierarchical Clustering
  • Evaluating Clustering Results
  • Apply clustering techniques to discover patterns in data.
  • Project: Use K-Means to cluster customer data.
  • Report on clustering project, including visualizations.

  • Cross-Validation, Train/Test Split, Overfitting, and Underfitting
  • Assess model performance and avoid common pitfalls.
  • Hands-on exercises with different evaluation techniques on existing models.

  • Handling Missing Data, Categorical Variable Encoding
  • Improve model performance through effective feature engineering.
  • Project: Prepare a dataset for machine learning, focusing on feature engineering.

  • Random Forests, Boosting Techniques (e.g., AdaBoost, Gradient Boosting)
  • Utilize ensemble methods to improve model accuracy.
  • Build ensemble models and compare performance to individual models.
  • Presentation on the effectiveness of ensemble techniques.

  • Neural Networks Basics: Structure, Activation Functions
  • Introduction to TensorFlow/Keras
  • Build and train simple neural networks.
  • Project: Create a neural network for image classification (e.g., MNIST dataset).

  • Text Preprocessing Techniques (Tokenization, Stop Words)
  • Basic NLP Tasks: Sentiment Analysis, Text Classification
  • Apply NLP techniques to analyze text data.
  • Project: Build a sentiment analysis model using a movie reviews dataset.

Course Statistics

  • Salary
    50-70%

    Average Salary Hike

  • Money
    13 Lac

    Highest Salary

  • Vertical
    50+

    Career Transitions

  • Bag
    112+

    Hiring Partners

Master these Technologies

Flask

Flask

Python

Python

Django

Django

GitHub

GitHub

Api

API

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