This comprehensive course focuses on a combination of essential data science techniques, machine learning algorithms, and deep learning methodologies. Students will learn how to process and analyze data, build models using machine learning, and work with deep learning techniques such as Neural Networks, Time Series Prediction, and Natural Language Processing (NLP). The course includes hands-on projects that allow students to apply the concepts learned on real-world datasets, providing them with practical experience for the job market.

Overview: (22 hours 27 minutes)

The course is structured into 30 days, starting with basic data handling and progressing through to advanced machine learning and deep learning applications. It covers key tools like Pandas for data manipulation, Matplotlib and Plotly for data visualization, and Scikit-learn for implementing machine learning algorithms. The course also introduces advanced concepts such as Neural Networks, Time Series Prediction, and NLP, offering a well-rounded education in data science.

Projects:

1. Exploratory Data Analysis on Sales Dataset: Perform data cleaning, visualization, and analysis on a sales dataset.
2. World Population Dataset Analysis: Analyze and visualize trends in global population data.
3. Power BI Dashboard on Netflix Dataset: Build an interactive Power BI dashboard to visualize data from Netflix.
4. ML Classification Project: Work on a machine learning project to classify data using various algorithms.
5. Predicting Sales for FMCG Company: Use machine learning models to predict sales for a fast-moving consumer goods company.
6. Churn Prediction using Artificial Neural Networks (ANN): Predict customer churn based on historical data using deep learning techniques.
7. Carbon Dioxide Emission Prediction: Build a model to predict CO2 emissions based on relevant data.
8. Stock Price Prediction with Neural Networks: Predict stock prices using neural networks.
9. Fake News Prediction using NLP: Create a model to detect fake news using natural language processing.
10. Twitter Sentiment Analysis using NLP and LSTM: Perform sentiment analysis on Twitter data using LSTM (Long Short-Term Memory).

Tools Required:

1. Python: The primary programming language for implementing machine learning and deep learning algorithms.
2. Pandas: For data manipulation and analysis.
3. NumPy: For handling large multidimensional arrays and matrices.
4. Matplotlib/Plotly: For data visualization.
5. Scikit-learn: For implementing machine learning algorithms like classification, regression, and clustering.
6. Power BI: For creating dashboards and interactive reports.
7. TensorFlow/Keras: For building deep learning models, especially artificial neural networks.
8. OpenCV: For computer vision tasks.
9. Natural Language Toolkit (NLTK)/SpaCy: For natural language processing.
10. LSTM (Long Short-Term Memory): A type of recurrent neural network for handling sequential data, especially for time series and NLP tasks.

Skills Required:

1. Data Handling: Proficiency in data cleaning, exploration, and visualization.
2. Machine Learning Fundamentals: Understanding of key machine learning algorithms (linear regression, decision trees, SVM, etc.).
3. Deep Learning Basics: Familiarity with neural networks, including ANN, CNN, and RNN.
4. NLP: Knowledge of text analysis and text mining techniques using NLP tools like NLTK.
5. Statistical Analysis: Ability to perform basic statistics and hypothesis testing.
6. Model Evaluation: Understanding how to evaluate model performance using metrics such as accuracy, precision, recall, F1-score, etc.
7. Time Series Analysis: Knowledge of forecasting methods and applications.
8. Visualization: Ability to create meaningful and effective visualizations to represent data insights.

Day-wise Topics List:

Day 01 - Introduction, Data Collection

Day 02 - Pandas Library

Day 03 - Numpy and Data Concepts

Day 04 - Matplotlib, Plotly

Day 5 - Project I - Exploratory Data Analysis on Sales Dataset

Day 6 - Project 2 - World Population Dataset Analysis

Day 7 - Project 3 - Power BI Dashboard on Netflix Dataset

Day 8 - Statistics 1

Day 9 - Statistics 2

Day 10 - Statistics 3

Day 11 - Sklearn Library (Scaling, Encoding)

Day 12 - Sklearn Library (Null, Outlier Handling)

Day 13 - Sklearn Library (Train-Test, Feature Selection)

Day 14 - ML Algorithm (Linear Models)

Day 15 - ML Algorithm (SVM, Decision Trees)

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

Day 17 - Hyperparameter Tuning, Model Evaluation

Day 18 - Project - ML Classification Project

Day 19 - Predicting the Sales of Products for an FMCG Company

Day 20 - Additional Internship Concepts

Day 21 - Deep Learning Introduction

Day 22 - Project 6 - Churn Prediction using ANN

Day 23 - CNN, RNN, LSTM

Day 24 - Time Series Prediction Introduction

Day 25 - Time Series Prediction (Continued)

Day 26 - Carbon Dioxide Emission Prediction

Day 27 - Stock Price Prediction with Neural Network

Day 28 - NLP Introduction

Day 29 - Project 9 - Fake News Prediction using NLP

Day 30 - Twitter Sentiment Analysis using NLP and LSTM