Curriculum
- 16 Sections
- 174 Lessons
- 48 Hours
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- Introduction and Brief Introduction12
- 1.1Data Collection – The Foundation of Data Science
- 1.2Data Cleaning and Preprocessing– Turning Raw Data into Usable Insights
- 1.3Data Exploration and Analysis (EDA)
- 1.4Feature Engineering – Transforming Data into Insights
- 1.5Data Visualization – Communicating Insights Effectively
- 1.6Machine Learning and Modeling – Building Intelligent Systems
- 1.7Model Evaluation and Validation – Ensuring Reliable Predictions
- 1.8Model Deployment –Bringing Machine Learning Models to Life
- 1.9Big Data Technologies– Managing and Analyzing Massive Datasets
- 1.10Data Ethics and Governance –Responsible AI and Data Practices
- 1.11Business Understanding and Domain Expertise
- 1.12Communication and Storytelling– Turning Data into Impactful Narratives
- Python Programming Basics8
- Data Science Essentials8
- 3.1Introduction to Data Science Essentials
- 3.2Introduction to NumPy for Numerical Computing
- 3.3Advanced NumPy Operations
- 3.4Introduction to Pandas for Data Manipulation
- 3.5Data Cleaning and Preparation with Pandas
- 3.6Data Aggregation and Grouping in Pandas
- 3.7Data Visualization with Matplotlib and Seaborn
- 3.8Exploratory Data Analysis (EDA) Project
- Mathematics for Machine Learning8
- 4.1Introduction to Mathematics for Machine Learning
- 4.2Linear Algebra Fundamentals
- 4.3Advanced Linear Algebra Concepts
- 4.4Calculus for Machine Learning (Derivatives)
- 4.5Calculus for Machine Learning (Integrals and Optimization)
- 4.6Probability Theory and Distributions
- 4.7Statistics Fundamentals
- 4.8Math-Driven Mini Project – Linear Regression from Scratch
- Probability & Statistics for Machine Learning8
- 5.1Introduction to Probability and Statistics for Machine Learning
- 5.2Probability Theory and Random Variables
- 5.3Probability Distributions in Machine Learning
- 5.4Statistical Inference – Estimation and Confidence Intervals
- 5.5Hypothesis Testing and P-Values
- 5.6Types of Hypothesis Tests
- 5.7Correlation and Regression Analysis
- 5.8Statistical Analysis Project – Analyzing Real-World Data
- Introduction to Machine Learning10
- 6.1Introduction to Machine Learning
- 6.2Machine Learning Basics and Terminology
- 6.3Machine Learning Basics and Terminology
- 6.4Introduction to Supervised Learning and Regression Models
- 6.5Advanced Regression Models – Polynomial Regression and Regularization
- 6.6Introduction to Classification and Logistic Regression
- 6.7Model Evaluation and Cross-Validation
- 6.8More Than Accuracy: Communicating Model Performance to Non-Experts
- 6.9k-Nearest Neighbors (k-NN) Algorithm
- 6.10Supervised Learning Mini Project
- Feature Engineering & Model Evaluation8
- 7.1Introduction to Feature Engineering and Model Evaluation
- 7.2Introduction to Feature Engineering
- 7.3Data Scaling and Normalization
- 7.4Encoding Categorical Variables
- 7.5Feature Selection Techniques
- 7.6Creating and Transforming Features
- 7.7Model Evaluation Techniques
- 7.8Cross-Validation and Hyperparameter Tuning
- Advanced Machine Learning Algorithms8
- Model Tuning and Optimization9
- 9.1Introduction to Model Tuning and Optimization
- 9.2Introduction to Hyperparameter Tuning
- 9.3Grid Search and Random Search
- 9.4Advanced Hyperparameter Tuning with Bayesian Optimization
- 9.5Smarter Search: Defending Hyperparameter Optimization Strategy
- 9.6Regularization Techniques for Model Optimization
- 9.7Cross-Validation and Model Evaluation Techniques
- 9.8Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
- 9.9Optimization Project – Building and Tuning a Final Model
- Neural Networks and Deep Learning Fundamentals8
- 10.1Introduction to Neural Networks and Deep Learning Fundamentals
- 10.2Introduction to Deep Learning and Neural Networks
- 10.3Forward Propagation and Activation Functions
- 10.4Loss Functions and Backpropagation
- 10.5Gradient Descent and Optimization Techniques
- 10.6Building Neural Networks with TensorFlow and Keras
- 10.7Building Neural Networks with PyTorch
- 10.8Neural Network Project – Image Classification on CIFAR-10
- Convolutional Neural Networks (CNNs)7
- 11.1Introduction to Convolutional Neural Networks (CNNs)
- 11.2Convolutional Layers and Filters
- 11.3Pooling Layers and Dimensionality Reduction
- 11.4Building CNN Architectures with Keras and TensorFlow
- 11.5Building CNN Architectures with PyTorch
- 11.6Regularization and Data Augmentation for CNNs
- 11.7CNN Project – Image Classification on Fashion MNIST or CIFAR-10
- Recurrent Neural Networks (RNNs) and Sequence Modeling7
- 12.1Introduction to Sequence Modeling and RNNs
- 12.2Understanding RNN Architecture and Backpropagation Through Time (BPTT)
- 12.3Long Short-Term Memory (LSTM) Networks
- 12.4Gated Recurrent Units (GRUs)
- 12.5Text Preprocessing and Word Embeddings for RNNs
- 12.6Sequence-to-Sequence Models and Applications
- 12.7RNN Project – Text Generation or Sentiment Analysis
- Transformers and Attention Mechanisms8
- 13.1Introduction to Attention Mechanisms
- 13.2Introduction to Transformers Architecture
- 13.3Self-Attention and Multi-Head Attention in Transformers
- 13.4Positional Encoding and Feed-Forward Networks
- 13.5Hands-On with Pre-Trained Transformers – BERT and GPT
- 13.6Advanced Transformers – BERT Variants and GPT-3
- 13.7Transformer Project – Text Summarization or Translation
- 13.8BERT or GPT? Advising on the Best Tool for Document Summarization
- Transfer Learning and Fine-Tuning8
- 14.1Introduction to Transfer Learning
- 14.2Transfer Learning in Computer Vision
- 14.3Fine-Tuning Techniques in Computer Vision
- 14.4Transfer Learning in NLP
- 14.5Fine-Tuning Techniques in NLP
- 14.6Domain Adaptation and Transfer Learning Challenges
- 14.7Transfer Learning Project – Fine-Tuning for a Custom Task
- 14.8Start Smart: Making the Case for Transfer Learning in Production
- Machine Learning Algorithms & Implementation27
- 15.11. Linear Regression Implementation in Python
- 15.22. Ridge and Lasso Regression Implementation in Python
- 15.33. Polynomial Regression Implementation in Python
- 15.44. Logistic Regression Implementation in Python
- 15.55. K-Nearest Neighbors (KNN) Implementation in Python
- 15.66. Support Vector Machines (SVM) Implementation in Python
- 15.77. Decision Trees Implementation in Python
- 15.88. Random Forests Implementation in Python
- 15.99. Gradient Boosting Implementation in Python
- 15.1010. Naive Bayes Implementation in Python
- 15.1111. K-Means Clustering Implementation in Python
- 15.1212. Hierarchical Clustering Implementation in Python
- 15.1313. DBSCAN (Density-Based Spatial Clustering of Applications w Noise)
- 15.1414. Gaussian Mixture Models(GMM) Implementation in Python
- 15.1515. Principal Component Analysis (PCA) Implementation in Python
- 15.1616. t-Distributed Stochastic Neighbor Embedding (t-SNE) Implementation in Python
- 15.1717. Autoencoders Implementation in Python
- 15.1818. Self-Training Implementation in Python
- 15.1919. Q-Learning Implementation in Python
- 15.2020. Deep Q-Networks (DQN) Implementation in Python
- 15.2121. Policy Gradient Methods Implementation in Python
- 15.2222. One-Class SVM Implementation in Python
- 15.2323. Isolation Forest Implementation in Python
- 15.2424. Convolutional Neural Networks (CNNs) Implementation in Python
- 15.2525. Recurrent Neural Networks (RNNs) Implementation in Python
- 15.2626. Long Short-Term Memory (LSTM) Implementation in Python
- 15.2727. Transformers Implementation in Python
- Projects On Data Science30
- 16.11: Basic Calculator using Python
- 16.22: Image Classifier using Keras and TensorFlow
- 16.33: Simple Chatbot using predefined responses
- 16.44: Spam Email Detector using Scikit-learn
- 16.55: Handwritten Digit Recognition with MNIST dataset
- 16.66: Sentiment Analysis on text data using NLTK
- 16.77: Movie Recommendation System using cosine similarity
- 16.88: Predict House Prices with Linear Regression
- 16.99: Weather Forecasting using historical data
- 16.1010: Basic Neural Network from scratch
- 16.1111: Stock Price Prediction using historical data w/ simple Linear Regression
- 16.1212: Predict Diabetes using logistic regression
- 16.1313: Dog vs. Cat Classifier with CNN
- 16.1414: Tic-Tac-Toe AI using Minimax Algorithm
- 16.1515: Credit Card Fraud Detection using Scikit-learn
- 16.1616: Iris Flower Classification using decision trees
- 16.1717: Simple Personal Assistant using Python speech libraries
- 16.1818: Text Summarizer using Gensim
- 16.1919: Fake Product Review Detection using NLP techniques
- 16.2020: Detect Emotion in Text using Natural Language Toolkit (NLTK)
- 16.2121: Book Recommendation System using collaborative filtering
- 16.2222: Predict Car Prices using Random Forest
- 16.2323: Identify Fake News using Naive Bayes
- 16.2424: Create a Resume Scanner using keyword extraction
- 16.2525: Customer Churn Prediction using classification algorithms
- 16.2626: Named Entity Recognition (NER) using spaCy
- 16.2727: Predict Employee Attrition using XGBoost
- 16.2828. Disease Prediction (e.g., Heart Disease) using ML algorithms
- 16.2929. Movie Rating Prediction using Collaborative Filtering
- 16.3030. Automatic Essay Grading using BERT