About Course

Machine learning

Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data. Instead of being explicitly programmed to perform a specific task, machine learning systems use statistical techniques to automatically learn and improve their performance over time.

The key idea behind machine learning is to enable computers to learn from experience or data and improve their performance on a specific task without being explicitly programmed. This is achieved through the use of algorithms that can analyze and interpret patterns in data, make predictions or decisions, and adapt their behavior based on new information.

Mathematics and Statistics
Programming and Software Engineering
Foundations of Machine Learning
Machine Learning Algorithms
Deep Learning
Evaluation Metrics
Optimization Algorithms
Feature Engineering:
Model Evaluation and Validation
Natural Language Processing (NLP)
Computer Vision
Reinforcement Learning
Ensemble Learning
Time Series Analysis
Ethics and Fairness in Machine Learning
Deployment and Productionization
Frameworks and Libraries
Big Data and Distributed Computing
Cloud Platforms
Specialized Topics

Course Content

Mathematics and Statistics

Linear Algebra

00:00
Calculus

00:00
Probability

00:00
Statistics

00:00

Programming and Software Engineering

Foundations of Machine Learning

Machine Learning Algorithms

Deep Learning

Evaluation Metrics

Optimization Algorithms

Feature Engineering:

Model Evaluation and Validation

Natural Language Processing (NLP)

Computer Vision

Reinforcement Learning

Ensemble Learning

Time Series Analysis

Ethics and Fairness in Machine Learning

Deployment and Productionization

Frameworks and Libraries

Big Data and Distributed Computing

Cloud Platforms

Specialized Topics

Student Ratings & Reviews

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About Course

Machine learning

What Will You Learn?

Course Content

Mathematics and Statistics

Linear Algebra

Calculus

Probability

Statistics

Programming and Software Engineering

Programming Languages

Version control

Software development practices

Foundations of Machine Learning

Supervised learning

Unsupervised learning

Reinforcement learning

Semi-supervised learning

Self-supervised learning

Transfer learning

Machine Learning Algorithms

Decision Trees

Random Forests

Support Vector Machines

K-Nearest Neighbors

Naive Bayes

Linear Regression

Logistic Regression

Neural Networks

Deep Learning

Neural network architecture

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

Transformers

Evaluation Metrics

Accuracy, Precision, Recall

F1 Score

ROC-AUC

Mean Squared Error (MSE)

Cross-Entropy Loss

Optimization Algorithms

Gradient Descent

Stochastic Gradient Descent (SGD)

Adam, RMSprop, Adagrad, etc.

Feature Engineering:

Feature scaling

Dimensionality reduction

One-Hot Encoding

Handling missing data

Model Evaluation and Validation

Cross-validation

Hyperparameter tuning

Bias-Variance tradeoff

Overfitting and underfitting

Natural Language Processing (NLP)

Text processing

Word embeddings

Named Entity Recognition (NER)

Sentiment Analysis

Computer Vision

Image processing

Object detection

Image classification

Reinforcement Learning

Markov Decision Processes (MDPs)

Q-Learning

Policy Gradient methods

Ensemble Learning

Bagging and boosting

AdaBoost

Gradient Boosting Machines (GBM)

XGBoost, LightGBM, CatBoost

Time Series Analysis

Autoregressive Integrated Moving Average (ARIMA)

Seasonal decomposition

Long Short-Term Memory (LSTM) networks for time series

Ethics and Fairness in Machine Learning

Bias and fairness

Explainability and interpretability