Why this course?

Description

Description: (Total Time: 19 hours, 12 minutes, and 46 seconds)

This module serves as an introduction to the fundamentals of Machine Learning. It covers the key concepts, types of machine learning, and the overall workflow of a machine learning project. You will get an overview of the data science process and the role of machine learning in solving complex problems.

Overview:

• What is Machine Learning?
• Supervised vs. Unsupervised Learning
• Key terms: Model, Training, Testing, Features, Labels
• Types of ML algorithms: Classification, Regression, Clustering, and more.
• The Machine Learning workflow: Data collection, cleaning, modeling, and evaluation.

Skills:

• Python Programming
• Data Analysis with Pandas and NumPy
• Machine Learning Algorithms (Classification, Regression, Clustering)
• Model Evaluation Techniques
• Data Preprocessing and Cleaning
• Deep Learning and Neural Networks

Tools:

• Jupyter Notebooks (for coding demos)
• Python (programming language)
• Various ML libraries (later modules introduce them in more detail)
• Pandas, NumPy, Scikit-learn, TensorFlow, Keras

Project:

• By the end of the course, you will have mastered the core principles and tools of machine learning and data science. You will have completed multiple mini-projects and applied machine learning algorithms to real-world problems, ranging from classification tasks to time-series predictions and deep learning applications.
• Project Examples=Iris Flower Classification ML , Heart Disease Prediction , House Price Prediction ,Deep Learning – Image Classification with CNN.
  
  Day 1 - Introduction to Machine Learning
  Day 2 - Overview of Python
  Day 3 - Python Important Concepts
  Day 4 - Pandas Library for Machine Learning
  Day 5 - NumPy Library for Machine Learning
  Day 6 - Data Visualization (Matplotlib & Seaborn)
  Day 7 - Introduction to ML, Data Collection & Preprocessing
  Day 8 - Data Wrangling
  Day 9 - Train-Test Split & ML Algorithms
  Day 10 - Iris Flower Classification ML
  Day 11 - Heart Disease Prediction ML
  Day 12 - Car Price Prediction
  Day 13 - House Price Prediction
  Day 14 - Movie Recommendation System
  Day 15 - Introduction to Natural Language Processing (NLP)
  Day 16 - Fake News Prediction Using NLP
  Day 17 - Introduction to Deep Learning
  Day 18 - Credit Card Fraud Detection
  Day 19 - Amazon Review Classification
  Day 20 - Image Classification using Convolutional Neural Networks (CNN)
  Day 21 - Car Brand Classification
  Day 22 - Gold Price Prediction
  Day 23 - Rainfall Prediction
  Day 24 - Breast Cancer Prediction
  Day 25 - Twitter Sentiment Analysis
  Day 26 - Hand Gesture Detection
  Day 27 - Time Series Prediction - Introduction (Part I)
  Day 28 - Time Series Prediction - Part II
  Day 29 - Carbon Dioxide Emission Prediction
  Day 30 - Stock Price Prediction