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Curriculum in Machine Learning

Introduction to Machine Learning

Overview of Machine Learning

Types of Machine Learning algorithms

Applications of Machine Learning in various domains

Introduction to Python and popular ML libraries

NumPy, Pandas, and scikit-learn

Data Preprocessing and Exploration

Data cleaning and handling missing values

Data transformation techniques (scaling, normalization)

Feature selection and engineering

Exploratory Data Analysis (EDA) using visualizations

Supervised Learning Algorithms

Linear Regression

Logistic Regression

Decision Trees and Random Forests

Support Vector Machines (SVM)

Naive Bayes Classifier

Unsupervised Learning Algorithms

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Association Rule Learning (Apriori algorithm)

Anomaly detection

Model Evaluation and Validation

Training and testing datasets

Cross-validation techniques

Evaluation metrics (accuracy, precision, recall, F1-score)

Confusion matrix and ROC curves

Bias-Variance tradeoff

Ensemble Methods and Model Selection

Bagging and Random Forests

Boosting and AdaBoost

Stacking and Blending

Hyperparameter tuning

Model selection techniques

Deep Learning Fundamentals

Introduction to Neural Networks

Activation functions

Backpropagation and Gradient Descent

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Deep Learning Applications

Image Classification

Object Detection and Localization

Natural Language Processing (NLP)

Sequence-to-Sequence Models

Generative Adversarial Networks (GANs)

Reinforcement Learning

Markov Decision Processes (MDPs)

Q-Learning and Value Iteration

Policy Gradient Methods

Deep Q-Networks (DQN)

Applications of Reinforcement Learning

Advanced Topics in Machine Learning

Transfer Learning

Explainable AI and Interpretability

Time Series Analysis

Autoencoders and Variational Autoencoders

Model Deployment and serving with cloud platforms

Curriculum in Machine Learning

Machine Learning Is Preferred by Over 35% of Developers. Machine Learning is the most widely used and in-demand programming language in the IT industry.

Overview of Machine Learning
Types of Machine Learning algorithms
Applications of Machine Learning in various domains
Introduction to Python and popular ML libraries
NumPy, Pandas, and scikit-learn

Data cleaning and handling missing values
Data transformation techniques (scaling, normalization)
Feature selection and engineering
Exploratory Data Analysis (EDA) using visualizations

Linear Regression
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines (SVM)
Naive Bayes Classifier

K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Association Rule Learning (Apriori algorithm)
Anomaly detection

Training and testing datasets
Cross-validation techniques
Evaluation metrics (accuracy, precision, recall, F1-score)
Confusion matrix and ROC curves
Bias-Variance tradeoff

Bagging and Random Forests
Boosting and AdaBoost
Stacking and Blending
Hyperparameter tuning
Model selection techniques

Introduction to Neural Networks
Activation functions
Backpropagation and Gradient Descent
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)

Image Classification
Object Detection and Localization
Natural Language Processing (NLP)
Sequence-to-Sequence Models
Generative Adversarial Networks (GANs)

Markov Decision Processes (MDPs)
Q-Learning and Value Iteration
Policy Gradient Methods
Deep Q-Networks (DQN)
Applications of Reinforcement Learning

Transfer Learning
Explainable AI and Interpretability
Time Series Analysis
Autoencoders and Variational Autoencoders
Model Deployment and serving with cloud platforms

Iris Flower Classification

Use the famous Iris dataset to classify iris flowers into different species using basic classification algorithms like K-Nearest Neighbors or Decision Trees.

Handwritten Digit Recognition

Build a simple digit recognition system using the MNIST dataset and basic machine learning algorithms like Support Vector Machines or Logistic Regression.