Why this course?

Description

Description: (Total Time: 23 hours, 36 minutes, and 56 seconds)

This 30-day course is a comprehensive guide to mastering Python programming, Data Science, Machine Learning, and Deep Learning. Through real-world projects, you'll learn essential skills like data manipulation, machine learning algorithms, deep learning models (ANN, CNN, RNN, LSTM), and natural language processing (NLP). Key projects include credit card fraud detection, stock price prediction, and Twitter sentiment analysis. By the end, you'll be proficient in tools such as Pandas, Scikit-learn, TensorFlow, and Keras, equipping you with the skills to tackle industry-level data challenges.

Course Overview:
 

• Data manipulation and preprocessing
• Data visualization techniques
• Machine Learning models and algorithms
• Deep Learning models (CNN, RNN, LSTM)
• Natural Language Processing (NLP)
• Model evaluation and optimization

Projects:
 

• Sale Data Analysis using Pandas 
• World Population Dataset EDA
• Credit Card Fraud Detection 
• Calories Burnt Prediction 
• Music Dataset Clustering & PCA
• Churn Prediction using ANN
• Carbon Dioxide Emission Prediction 
• Stock Price Prediction with Neural Network 
• Fake News Prediction using NLP 
• Twitter Sentiment Analysis using NLP and LSTM 
   

  Skills 

  Python Programming
• Data Science Techniques
• Data Visualization
• Machine Learning Algorithms:
• Deep Learning:
• Time-Series Forecasting
• Natural Language Processing (NLP
• Model Evaluation
  
  TOOLS:
• Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow,               Keras, NLTK
• Deep Learning Frameworks: TensorFlow, Keras
• Machine Learning Algorithms: Linear Regression, SVM, Decision Trees, Naive             Bayes,  Random Forest, KNN, etc.
• Data Visualization Tools: Matplotlib, Seaborn
• Time-Series Forecasting Models: ARIMA, LSTM
• NLP Libraries: NLTK, SpaCy
   

Day 1 - Introduction, Python Installation

Day 2 - Python Basics Sessions: 

Day 3 - Python Data Structures Sessions

Day 4 - Python Programming Fundamentals Sessions

Day 5 - Class and Object Sessions

Day 6 - Pandas Library Sessions

Day 7 - Project 1 - Sale Data Analysis using Pandas Sessions

Day 8 - Working with NumPy Arrays Sessions

Day 9 - Data visualization with matplotlib and Seaborn Sessions

Day 10 Project II - World Population Dataset EDA Sessions

Day 12 - SKlearn Library (Null, Outlier Handing) Sessions

Day 14 - ML Algorithm (linear Models) Session

Day 15 - ML Algorithm (SVM, Decision Trees) Sessions

Day 16 - ML Algorithm (Ensemble Algorithms, Naive Bayes, KNN) Sessions

Day 17 - Hyperparameter Tuning, Model Evaluation Sessions

Day 18 Project 3 - Credit Card Fraud Detection Sessions

Day 19 Project 4 - Calories Burnt Prediction Sessions

Day 20 - Project 5 - Music Dataset Clustering, PCA Sessions

Day 21 - Deep Learning Introduction Sessions

Day 23 - CNN, RNN, LSTM Session

Day 25 - Time Series Prediction Introduction Sessions: 2 | Time: 33 min 32 

Day 26 - Project 7 - Carbon Dioxide Emission Prediction Sessions

Day 27 - Project 8 - Stock Price Prediction with Neural Network Sessions

Day 28 - NLP Introduction Sessions

Day 29 - Project 9 - Fake News Prediction using NLP Sessions

Day 30 - Project 10 - Twitter Sentiment Analysis using NLP and LSTM Session