Data Science Training

• What Will We Cover

• Data Collection – The Foundation of Data Science

• Data Cleaning and Preprocessing – Turning Raw Data into Usable Insights

• Data Exploration and Analysis (EDA)

• Feature Engineering – Transforming Data into Insights

• Data Visualization – Communicating Insights Effectively

• Machine Learning and Modeling – Building Intelligent Systems

• Model Evaluation and Validation – Ensuring Reliable Predictions

• Model Deployment – Bringing Machine Learning Models to Life

• Big Data Technologies – Managing and Analyzing Massive Datasets

• Data Ethics and Governance – Responsible AI and Data Practices

• Business Understanding and Domain Expertise

• Communication and Storytelling – Turning Data into Impactful Narratives

• Whats Next: Bootcamp Deep Dive

• Introduction to Python and Development Setup

• Control Flow in Python

• Functions and Modules

• Data Structures (Lists, Tuples, Dictionaries, Sets)

• Working with Strings

• File Handling

• Pythonic Code and Project Work

• Resources for the Entire Course

• Introduction to NumPy for Numerical Computing

• Advanced NumPy Operations

• Introduction to Pandas for Data Manipulation

• Data Cleaning and Preparation with Pandas

• Data Aggregation and Grouping in Pandas

• Data Visualization with Matplotlib and Seaborn

• Exploratory Data Analysis (EDA) Project

• Linear Algebra Fundamentals

• Advanced Linear Algebra Concepts

• Calculus for Machine Learning (Derivatives)

• Calculus for Machine Learning (Integrals and Optimization)

• Probability Theory and Distributions

• Statistics Fundamentals

• Math-Driven Mini Project – Linear Regression from Scratch

• Probability Theory and Random Variables

• Probability Distributions in Machine Learning

• Statistical Inference – Estimation and Confidence Intervals

• Hypothesis Testing and P-Values

• Types of Hypothesis Tests

• Correlation and Regression Analysis

• Statistical Analysis Project – Analyzing Real-World Data

• Machine Learning Basics and Terminology

• Introduction to Supervised Learning and Regression Models

• Advanced Regression Models – Polynomial Regression and Regularization

• Introduction to Classification and Logistic Regression

• Model Evaluation and Cross-Validation

• More Than Accuracy: Communicating Model Performance to Non-Experts

• k-Nearest Neighbors (k-NN) Algorithm

• Supervised Learning Mini Project

• Introduction to Feature Engineering

• Data Scaling and Normalization

• Encoding Categorical Variables

• Feature Selection Techniques

• Explaining Feature Importance to Domain Experts

• Creating and Transforming Features

• Model Evaluation Techniques

• Cross-Validation and Hyperparameter Tuning

• Introduction to Ensemble Learning

• Bagging and Random Forests

• Boosting and Gradient Boosting

• Introduction to XGBoost

• LightGBM and CatBoost

• Handling Imbalanced Data

• Advanced Model Stacking

• Ensemble Learning Mini Project

• Introduction to Hyperparameter Tuning

• Grid Search and Random Search

• Advanced Hyperparameter Tuning with Bayesian Optimization

• Defending Hyperparameter Optimization Strategy

• Regularization Techniques for Model Optimization

• Cross-Validation and Model Evaluation Techniques

• Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV

• Optimization Project – Building and Tuning a Final Model

• Introduction to Deep Learning and Neural Networks

• Forward Propagation and Activation Functions

• Loss Functions and Backpropagation

• Gradient Descent and Optimization Techniques

• Building Neural Networks with TensorFlow and Keras

• Building Neural Networks with PyTorch

• Neural Network Project – Image Classification on CIFAR-10

• Justifying Your Neural Network Architecture

• Introduction to Convolutional Neural Networks

• Convolutional Layers and Filters

• Pooling Layers and Dimensionality Reduction

• Building CNN Architectures with Keras and TensorFlow

• Building CNN Architectures with PyTorch

• Introduction to Sequence Modeling and RNNs

• Understanding RNN Architecture and Backpropagation Through Time (BPTT)

• Long Short-Term Memory (LSTM) Networks

• Gated Recurrent Units (GRUs)

• Text Preprocessing and Word Embeddings for RNNs

• Sequence-to-Sequence Models and Applications

• RNN Project – Text Generation or Sentiment Analysis

• Introduction to Attention Mechanisms

• Introduction to Transformers Architecture

• Self-Attention and Multi-Head Attention in Transformers

• Positional Encoding and Feed-Forward Networks

• Hands-On with Pre-Trained Transformers – BERT and GPT

• Advanced Transformers – BERT Variants and GPT-3

• Transformer Project – Text Summarization or Translation

• Advising on the Best Tool for Document Summarization

• Introduction to Transfer Learning

• Transfer Learning in Computer Vision

• Fine-Tuning Techniques in Computer Vision

• Transfer Learning in NLP

• Fine-Tuning Techniques in NLP

• Domain Adaptation and Transfer Learning Strategies

• Case Studies in Transfer Learning

• Project on Transfer Learning and Fine-Tuning

• Introduction to Machine Learning Algorithms

• Linear Regression Implementation in Python

• Ridge and Lasso Regression Implementation in Python

• Polynomial Regression Implementation in Python

• Logistic Regression Implementation in Python

• K-Nearest Neighbors (KNN) Implementation in Python

• Support Vector Machines (SVM) Implementation in Python

• Decision Trees Implementation in Python

• Random Forests Implementation in Python

• Gradient Boosting Implementation in Python

• Naive Bayes Implementation in Python

• K-Means Clustering Implementation in Python

• Hierarchical Clustering Implementation in Python

• DBSCAN Implementation in Python

• Gaussian Mixture Models (GMM) Implementation in Python

• Principal Component Analysis (PCA) Implementation in Python

• t-Distributed Stochastic Neighbor Embedding (t-SNE) Implementation in Python

• Autoencoders Implementation in Python

• Self-Training Implementation in Python

• Q-Learning Implementation in Python

• Deep Q-Networks (DQN) Implementation in Python

• Policy Gradient Methods Implementation in Python

• One-Class SVM Implementation in Python

• Isolation Forest Implementation in Python

• Convolutional Neural Networks (CNNs) Implementation in Python

• Recurrent Neural Networks (RNNs) Implementation in Python

• Long Short-Term Memory (LSTM) Implementation in Python

• What is Machine Learning in the context of TensorFlow?

• Introduction to TensorFlow

• TensorFlow vs. Other Machine Learning frameworks

• Installing TensorFlow

• Setting up your Development Environment

• Verifying the Installation

• Introduction to Tensors

• Tensor Operations

• Constants, Variables, and Placeholders

• TensorFlow Computational Graph

• Creating and Running a TensorFlow Session

• Managing Graphs and Sessions

• Building a Simple Feedforward Neural Network

• Activation Functions

• Loss Functions and Optimizers

• Introduction to Keras API

• Building Complex Models with Keras

• Training and Evaluating Models

• Introduction to CNNs

• Building and Training CNNs with TensorFlow

• Transfer Learning with Pre-trained CNNs

• Introduction to RNNs

• Building and Training RNNs with TensorFlow

• Applications of RNNs: Language Modeling, Time Series Prediction

• Saving and Loading Models

• TensorFlow Serving for Model Deployment

• TensorFlow Lite for Mobile and Embedded Devices

• Introduction to Distributed Computing with TensorFlow

• TensorFlow's Distributed Execution Framework

• Scaling TensorFlow with TensorFlow Serving and Kubernetes

• What will we cover

• Introduction to PyTorch

• Getting Started with PyTorch

• Working with Tensors

• Autograd and Dynamic Computation Graphs

• Building Simple Neural Networks

• Loading and Preprocessing Data

• Model Evaluation and Validation

• Advanced Neural Network Architectures

• Transfer Learning and Fine-Tuning

• Handling Complex Data

• Model Deployment and Production

• Debugging and Troubleshooting

• Distributed Training and Performance Optimization

• Custom Layers and Loss Functions

• Research-oriented Techniques

• Integration with Other Libraries

• Contributing to PyTorch and Community Engagement

• Basic Calculator using Python

• Image Classifier using Keras and TensorFlow

• Simple Chatbot using predefined responses

• Spam Email Detector using Scikit-learn

• Handwritten Digit Recognition with MNIST dataset

• Sentiment Analysis on text data using NLTK

• Movie Recommendation System using cosine similarity

• Predict House Prices with Linear Regression

• Weather Forecasting using historical data

• Basic Neural Network from scratch

• Stock Price Prediction using historical data w/ simple Linear Regression