1. Introduction to AI, ML, and Data Science

• 1.1 Overview of AI, ML, and Data Science – Definitions & Real-world Use Cases
• 1.2 Key Differences Between AI, ML, and Data Science
• 1.3 AI vs. ML vs. Deep Learning: Understanding the Relationships
• 1.4 Setting Up Your Environment
  • Python Installation, Jupyter Notebook, and Key Libraries (Numpy, Pandas, Matplotlib)

2. Python for Data Science and ML: Essential Tools

• 2.1 Python Basics: Data Types, Variables, Loops, and Functions
• 2.2 Data Manipulation with Numpy and Pandas
• 2.3 Data Visualization
  • Matplotlib, Seaborn, Plotly
• 2.4 Data Preprocessing
  • Handling Missing Values, Encoding Categorical Data, Scaling & Normalization

3. Exploratory Data Analysis (EDA)

• 3.1 Data Exploration Techniques
  • Descriptive Statistics, Correlation Analysis, Feature Distribution
• 3.2 Feature Engineering
  • Handling Outliers, Feature Transformation, Feature Selection

4. Supervised Learning: Core Algorithms

• 4.1 Regression Analysis
  • Linear Regression, Polynomial Regression, Ridge & Lasso Regression
• 4.2 Classification Algorithms
  • Logistic Regression, K-Nearest Neighbors (KNN)
• 4.3 Decision Trees & Random Forests
  • Concepts, Building Trees, Hyperparameter Tuning
• 4.4 Support Vector Machines (SVM)
  • Concepts, Kernel Methods, Hyperplane Optimization
• 4.5 Naive Bayes Classifier
• 4.6 Model Evaluation Techniques
  • Accuracy, Precision, Recall, F1-Score, ROC-AUC

5. Unsupervised Learning: Finding Patterns

• 5.1 Clustering Algorithms
  • K-Means Clustering, Hierarchical Clustering, DBSCAN
• 5.2 Dimensionality Reduction Techniques
  • PCA (Principal Component Analysis), t-SNE, UMAP
• 5.3 Association Rule Learning
  • Apriori, Eclat Algorithms
• 5.4 Anomaly Detection
  • Isolation Forest, One-Class SVM

6. Advanced Machine Learning Concepts

• 6.1 Ensemble Learning Techniques
  • Bagging, Boosting (AdaBoost, Gradient Boosting), Stacking
• 6.2 Gradient Boosting Machines (GBM)
  • XGBoost, LightGBM, CatBoost
• 6.3 Model Optimization
  • Hyperparameter Tuning, Grid Search, Random Search
• 6.4 Deep Learning Basics
  • Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)

7. AI & Deep Learning

• 7.1 Introduction to AI & Neural Networks
  • Feedforward Neural Networks, Backpropagation
• 7.2 Convolutional Neural Networks (CNNs)
  • Image Classification, Object Detection
• 7.3 Recurrent Neural Networks (RNNs) & LSTMs
  • Sequence Prediction, Natural Language Processing (NLP)
• 7.4 Generative Adversarial Networks (GANs)
  • Generating Synthetic Data, Image Synthesis

8. Natural Language Processing (NLP)

• 8.1 Text Preprocessing
  • Tokenization, Lemmatization, Stop Word Removal
• 8.2 Sentiment Analysis
• 8.3 Text Classification
  • TF-IDF, Word Embeddings (Word2Vec, GloVe)
• 8.4 Transformer Models
  • BERT, GPT, Transfer Learning in NLP

9. Data Science for Business Intelligence

• 9.1 Introduction to Business Intelligence (BI)
• 9.2 Data Warehousing, ETL Processes
• 9.3 Dashboard Creation & Data Reporting
• 9.4 Building Predictive Models for Business Decisions

10. Hands-On Projects for Practical Learning

• 10.1 Predicting House Prices with Linear Regression
  • Use a housing dataset to build a regression model predicting house prices based on various features.
• 10.2 Spam Detection with Logistic Regression
  • Implement a spam classifier using Logistic Regression to predict whether an email is spam or not.
• 10.3 Clustering Customers with K-Means
  • Perform customer segmentation using K-Means clustering on an e-commerce dataset.
• 10.4 Building a Random Forest Model for Classification
  • Use the Random Forest algorithm to classify different species of flowers using the famous Iris dataset.
• 10.5 Sentiment Analysis with NLP
  • Implement a sentiment analysis model using natural language processing techniques to classify movie reviews as positive or negative.
• 10.6 Image Classification with CNNs
  • Build a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset.
• 10.7 Building a Recommender System
  • Create a recommendation system for movies or products using collaborative filtering.
• 10.8 Time Series Forecasting with RNNs
  • Use Recurrent Neural Networks (RNNs) to predict future stock prices based on historical data.
• 10.9 Anomaly Detection with Isolation Forest
  • Detect anomalies in transaction data using the Isolation Forest algorithm.
• 10.10 Deploying a Machine Learning Model
  • Learn how to deploy a trained ML model using Flask or Django, and serve it as an API for real-world use cases.