Skills Required:

1. Basic understanding of Python programming, including knowledge of variables, loops, functions, and data structures such as lists and dictionaries.
2. Familiarity with data analysis libraries like Pandas and NumPy for data manipulation, cleaning, and analysis.
3. Understanding of basic mathematics and statistics, particularly linear algebra, probability, and statistics, to comprehend machine learning algorithms.
4. Problem-solving abilities to apply machine learning techniques to diverse datasets.
5. Knowledge of machine learning concepts such as supervised learning, classification, regression, and model evaluation techniques including accuracy, precision, recall, and F1 score.
6. Understanding of deep learning concepts and techniques, including neural networks and Convolutional Neural Networks (CNNs), especially for tasks like image recognition.
7. Experience with model deployment using tools like Flask or Django to deploy machine learning models as web applications and make them accessible in production environments.

Tools Required:

1. Python programming language, the core language for machine learning tasks and libraries.
2. Pandas, a library for data manipulation, cleaning, and analysis.
3. NumPy, a library for numerical operations and handling large datasets.
4. Scikit-learn, a machine learning library for implementing common algorithms such as regression, classification, and clustering.
5. Matplotlib and Seaborn, for data visualization and creating plots and graphs to analyze data and model performance.
6. TensorFlow and Keras, deep learning libraries used to build, train, and evaluate neural networks and other advanced machine learning models.
7. Flask or Django, frameworks used for deploying machine learning models and building web applications.
8. Jupyter Notebooks, an interactive development environment for writing and testing machine learning code.
9. Cloud platforms like Google Colab, AWS, or Microsoft Azure, for running computations and hosting models on the cloud.

Projects:

1. Iris Flower Classification: 
2. Rainfall Prediction
3. Loan Status Prediction: 
4. Titanic Survival Prediction: 
5. Fake News Detection 
6. Credit Card Fraud Detection:
7. Heart Disease Prediction:  
8. Movie Recommendation System:
9. Covid-19 Prediction using CNN:  
10. Model Deployment:
     

    Day 1 - Introduction to ML

    Day 2 - Basic Python Programming

    Day 3 - Python Programming - II

    Day 4 - Pandas Libraries for Machine Learning

    Day 5 - NumPy Libraries for Machine Learning

    Day 6 - Data Visualization

    Day 7 - Data Collection Processing Part-I

    Day 8 - Data Collection Processing Part-II

    Day 9 - Train Test Model

    Day 10 - Iris Flower Classification

    Day 11 - Rain Fall Prediction

    Day 12 - Loan Status Prediction

    Day 13 - Titanic Survival Prediction

    Day 14 - Fake News Detection

    Day 15 - Credit Card Fraud Detection

    Day 16 - Heart Disease Prediction

    Day 17 - Diabetic Prediction using Machine Learning

    Day 18 - Breast Cancer Prediction

    Day 19- Presentation of Project

    Day 20 -Presentation of Project 2

    Day 21 - Electricity Price Prediction

    Day 22 - Car Price Prediction

    Day 23 - House Price Prediction

    Day 24 - Gold Price Prediction

    Day 25 - Big Mart Sale Prediction

    Day 26 - Medical Insurance Price Prediction

    Day 27 - Customer Segmentation using Machine Learning

    Day 28 - Movie Recommendation System

    Day 29 - Covid-19 Prediction Using CNN

    Day 30 - Model Deployment