From Pythonista to Machine Learning Engineer

A comprehensive course transforming Python developers into skilled Machine Learning Engineers ready for real-world challenges.
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Introduction to Machine Learning

Unit 1: What is Machine Learning?

Unit 2: The Machine Learning Workflow

Unit 3: Setting Up Your Environment

Unit 4: Environment Setup Continued

Python Fundamentals for Machine Learning

Unit 1: Python Data Structures Refresher

Unit 2: More Data Structures

Unit 3: Functions, Modules, and OOP

NumPy for Numerical Computing

Unit 1: Introduction to NumPy Arrays

Unit 2: Array Indexing and Slicing

Unit 3: Mathematical Operations with NumPy

Unit 4: Broadcasting in NumPy

Pandas for Data Analysis

Unit 1: Pandas Data Structures

Unit 2: Data Input and Output

Unit 3: Data Cleaning and Transformation

Unit 4: Data Exploration and Analysis

Data Visualization with Matplotlib and Seaborn

Unit 1: Matplotlib Fundamentals

Unit 2: Customizing Matplotlib Plots

Unit 3: Seaborn for Advanced Visualization

Introduction to Data Preprocessing

Unit 1: Why Preprocess Data?

Unit 2: Data Preprocessing Techniques

Unit 3: Practical Data Cleaning

Handling Missing Values

Unit 1: Identifying Missing Data

Unit 2: Handling Missing Data - Deletion

Unit 3: Handling Missing Data - Imputation

Unit 4: Advanced Imputation Techniques

Data Scaling and Normalization

Unit 1: Why Scale Your Data?

Unit 2: MinMaxScaler Deep Dive

Unit 3: StandardScaler Explained

Unit 4: RobustScaler: Handling Outliers

Unit 5: Choosing the Right Scaler

Encoding Categorical Variables

Unit 1: Understanding Categorical Data

Unit 2: Label Encoding

Unit 3: One-Hot Encoding

Unit 4: Beyond the Basics

Feature Engineering

Unit 1: Feature Engineering Fundamentals

Unit 2: Creating New Features

Unit 3: Advanced Feature Engineering

Introduction to Supervised Learning

Unit 1: Supervised Learning Fundamentals

Unit 2: Data for Supervised Learning

Unit 3: Supervised Learning Workflow

Linear Regression

Unit 1: Foundations of Linear Regression

Unit 2: Implementing Linear Regression with Scikit-learn

Unit 3: Evaluating Linear Regression Models

Logistic Regression

Unit 1: Logistic Regression Fundamentals

Unit 2: Implementing Logistic Regression with Scikit-learn

Unit 3: Evaluating Logistic Regression Models

Decision Trees

Unit 1: Decision Tree Fundamentals

Unit 2: Implementing Decision Trees with Scikit-learn

Unit 3: Visualizing and Interpreting Decision Trees

Random Forests

Unit 1: Random Forest Fundamentals

Unit 2: Building Random Forests with Scikit-learn

Unit 3: Advanced Concepts and Tuning

Model Evaluation Metrics

Unit 1: Classification Metrics

Unit 2: More Classification Metrics

Unit 3: Regression Metrics

Cross-Validation

Unit 1: Understanding Cross-Validation

Unit 2: K-Fold Cross-Validation

Unit 3: Stratified K-Fold Cross-Validation

Bias-Variance Tradeoff

Unit 1: Understanding Bias and Variance

Unit 2: Identifying Underfitting and Overfitting

Unit 3: Techniques to Address Over/Underfitting

Hyperparameter Tuning

Unit 1: Understanding Hyperparameters

Unit 2: Introduction to Automated Tuning

Unit 3: Grid Search

Unit 4: Random Search

Grid Search

Unit 1: Understanding Grid Search

Unit 2: Implementing Grid Search with Scikit-learn

Unit 3: Analyzing and Using Grid Search Results

Random Search

Unit 1: Random Search Fundamentals

Unit 2: Implementing Random Search with Scikit-learn

Unit 3: Advanced Random Search Techniques

Introduction to Unsupervised Learning

Unit 1: Understanding Unsupervised Learning

Unit 2: Applications of Unsupervised Learning

Unit 3: Unsupervised Learning in Practice

K-Means Clustering

Unit 1: K-Means Clustering Fundamentals

Unit 2: Implementing K-Means with Scikit-learn

Unit 3: Evaluating and Improving K-Means

Hierarchical Clustering

Unit 1: Understanding Hierarchical Clustering

Unit 2: Implementing Hierarchical Clustering with Scikit-learn

Unit 3: Visualizing Dendrograms

Principal Component Analysis (PCA)

Unit 1: Introduction to Dimensionality Reduction

Unit 2: PCA: The Algorithm

Unit 3: Implementing PCA with Scikit-learn

Unit 4: PCA in Practice

Introduction to Neural Networks

Unit 1: Neural Network Fundamentals

Unit 2: Deep Dive into Network Types

Unit 3: Key Concepts

Building Neural Networks with TensorFlow/Keras

Unit 1: Setting Up Your Environment

Unit 2: Building Your First Neural Network

Unit 3: Training and Evaluating

Unit 4: Going Deeper

Training Neural Networks

Unit 1: Fundamentals of Neural Network Training

Unit 2: Keras Implementation and Monitoring

Unit 3: Advanced Training Techniques

Evaluating Neural Networks

Unit 1: Performance Metrics

Unit 2: Overfitting and Underfitting

Unit 3: Preventing Overfitting

Hyperparameter Tuning for Neural Networks

Unit 1: Understanding Hyperparameters

Unit 2: Manual Tuning & Introduction to Automated Techniques

Unit 3: Hands-on with Grid Search

Unit 4: Hands-on with Random Search

Unit 5: Advanced Tuning & Best Practices

Neural Networks for Classification

Unit 1: Classification Basics with NNs

Unit 2: Building Blocks for Classifiers

Unit 3: Building and Evaluating Classifiers

Neural Networks for Regression

Unit 1: Regression NNs: The Basics

Unit 2: Building and Training Regression Models

Unit 3: Evaluating and Improving Regression Models

Introduction to Model Deployment

Unit 1: Model Deployment Fundamentals

Unit 2: Deployment Technologies and Tools

Unit 3: Deployment Strategies

Deploying Models with Flask

Unit 1: Setting Up Flask

Unit 2: Loading and Using ML Models

Unit 3: Creating API Endpoints

Creating API Endpoints

Unit 1: API Endpoint Fundamentals

Unit 2: Request and Response Formats

Unit 3: Handling Different Request Types

Testing the API

Unit 1: Introduction to API Testing

Unit 2: Testing with Postman

Unit 3: Testing with Curl

Deploying Models with FastAPI

Unit 1: Setting Up FastAPI

Unit 2: Loading and Using ML Models

Unit 3: API Enhancements and Best Practices

Containerization with Docker

Unit 1: Docker Fundamentals

Unit 2: Building Docker Images for ML

Unit 3: Running and Managing Containers

Deploying to Cloud Platforms (AWS, Azure, GCP)

Unit 1: Cloud Deployment Overview

Unit 2: Deploying to AWS SageMaker

Unit 3: Deploying to Azure and GCP

Model Monitoring and Maintenance

Unit 1: Introduction to Model Monitoring

Unit 2: Practical Monitoring Techniques

Unit 3: Model Retraining and Maintenance

Introduction to Natural Language Processing (NLP)

Unit 1: NLP Fundamentals

Unit 2: Text Preprocessing

Unit 3: Core NLP Tasks

Text Preprocessing Techniques

Unit 1: Introduction to Text Preprocessing

Unit 2: Tokenization Techniques

Unit 3: Stemming and Lemmatization

Unit 4: Stop Word Removal

Text Vectorization

Unit 1: Introduction to Text Vectorization

Unit 2: Bag of Words (BoW)

Unit 3: TF-IDF

Unit 4: Advanced Vectorization

Sentiment Analysis

Unit 1: Introduction to Sentiment Analysis

Unit 2: Lexicon-Based Sentiment Analysis

Unit 3: Machine Learning for Sentiment Analysis

Text Classification

Unit 1: Text Classification Fundamentals

Unit 2: Feature Extraction Techniques

Unit 3: Model Building and Evaluation

Introduction to Computer Vision

Unit 1: Computer Vision Fundamentals

Unit 2: Image Preprocessing Techniques

Unit 3: Advanced Preprocessing & CV Tasks

Image Preprocessing Techniques

Unit 1: Image Resizing

Unit 2: Grayscaling Images

Unit 3: Image Augmentation

Image Classification

Unit 1: Image Classification Fundamentals

Unit 2: Building Your First CNN

Unit 3: Training and Evaluation

Object Detection

Unit 1: Introduction to Object Detection

Unit 2: Classical Object Detection Algorithms

Unit 3: Modern Object Detection Algorithms

Ethical Considerations in Machine Learning

Unit 1: Introduction to Ethics in ML

Unit 2: Understanding Bias in ML

Unit 3: Mitigating Bias

Fairness in Machine Learning

Unit 1: Understanding Fairness

Unit 2: Fairness Metrics

Unit 3: Mitigation Techniques

Explainable AI (XAI)

Unit 1: Introduction to Explainable AI

Unit 2: LIME: Local Interpretable Model-Agnostic Explanations

Unit 3: SHAP: SHapley Additive exPlanations

MLOps: Introduction to Machine Learning Operations

Unit 1: MLOps Fundamentals

Unit 2: Automation in MLOps

Unit 3: MLOps in Practice

Version Control with Git

Unit 1: Git Fundamentals

Unit 2: Basic Git Operations

Unit 3: Branching and Merging

Continuous Integration and Continuous Delivery (CI/CD)

Unit 1: CI/CD Fundamentals

Unit 2: Setting Up a CI/CD Pipeline

Unit 3: Automating Model Training and Deployment

Model Registry

Unit 1: Introduction to Model Registries

Unit 2: Using a Model Registry

Unit 3: Advanced Model Registry Concepts

Advanced Regression Techniques

Unit 1: Polynomial Regression

Unit 2: Regularization Techniques

Unit 3: Model Comparison and Selection

Advanced Classification Techniques

Unit 1: Intro to Support Vector Machines

Unit 2: Kernel Functions in SVM

Unit 3: Implementing and Evaluating SVM

Ensemble Methods in Depth

Unit 1: Boosting Fundamentals

Unit 2: Gradient Boosting in Detail

Unit 3: Implementation and Tuning

Time Series Analysis Fundamentals

Unit 1: Intro to Time Series

Unit 2: Decomposition and Stationarity

Unit 3: Basic Forecasting Techniques

Deep Learning Architectures

Unit 1: Introduction to Deep Learning Architectures

Unit 2: Convolutional Neural Networks (CNNs) in Detail

Unit 3: CNNs for Image Classification

Recurrent Neural Networks (RNNs)

Unit 1: RNN Fundamentals

Unit 2: LSTMs: Long Short-Term Memory Networks

Unit 3: GRUs: Gated Recurrent Units

Unit 4: Advanced RNN Concepts

Generative Adversarial Networks (GANs)

Unit 1: GANs: The Big Picture

Unit 2: Building Blocks of GANs

Unit 3: Training and Beyond

Reinforcement Learning Fundamentals

Unit 1: Intro to Reinforcement Learning

Unit 2: Q-Learning

Unit 3: Deep Q-Networks (DQN)

Advanced Data Visualization Techniques

Unit 1: Interactive Plotting with Plotly

Unit 2: Bokeh for Web-Based Visualizations

Unit 3: Dashboard Design and Best Practices

Cloud-Based Machine Learning Services

Unit 1: Introduction to Cloud ML

Unit 2: Model Training in the Cloud

Unit 3: Model Deployment and Management

Building a Machine Learning Portfolio

Unit 1: Portfolio Fundamentals

Unit 2: Building Your Portfolio

Unit 3: Sharing and Showcasing

Preparing for Machine Learning Interviews

Unit 1: Understanding the Interview Landscape

Unit 2: Technical Deep Dive

Unit 3: Advanced Topics and System Design

Unit 4: Final Preparations