Technical courses

Machine Learning Course

By completing this course, you'll gain the skills to: Build and deploy machine learning models for various applications. Analyze complex datasets and extract valuable insights. Solve real-world problems using machine learning techniques. Explore career opportunities in data science and machine learning.

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120 hours

WDA

About Course

Our Machine Learning course equips you with the skills to build and apply machine learning models to solve complex problems. You’ll learn about various machine learning algorithms, data preprocessing, model evaluation, and deployment.

Machine Learning Fundamentals: Understand the principles of machine learning, including supervised, unsupervised, and reinforcement learning.
Python for Machine Learning: Gain proficiency in Python, a popular language for machine learning, and learn to use libraries like NumPy, Pandas, Scikit-learn, and TensorFlow.
Data Preprocessing and Feature Engineering: Prepare data for analysis and extract relevant features.
Machine Learning Algorithms: Implement various machine learning algorithms, such as regression, classification, clustering, and deep learning.
Model Evaluation and Deployment: Evaluate the performance of machine learning models and deploy them for real-world applications.

Course Content

Module 1: Introduction to Machine Learning

Machine learning concepts and applications
Supervised, unsupervised, and reinforcement learning
Python for machine learning

Module 2: Data Preprocessing and Feature Engineering

Module 3: Supervised Learning

Module 4: Unsupervised Learning

Module 5: Deep Learning

Module 6: Model Deployment and Production

Instructors

WDA

4.4

35 Students

57 Courses

No Review Yet

Level Expert
Duration 120 hours
Last Updated September 6, 2024
Certificate Certificate of completion

Machine Learning Course

About Course

What Will You Learn?

Course Content

Module 1: Introduction to Machine Learning

Machine learning concepts and applications

Supervised, unsupervised, and reinforcement learning

Python for machine learning

Module 2: Data Preprocessing and Feature Engineering

Data cleaning and preprocessing techniques

Feature selection and extraction

Feature scaling and normalization

Module 3: Supervised Learning

Regression analysis (linear, logistic)

Classification algorithms (decision trees, random forests, support vector machines)

Model evaluation metrics

Module 4: Unsupervised Learning

Clustering algorithms (k-means, hierarchical clustering)

Dimensionality reduction techniques

Anomaly detection

Module 5: Deep Learning

Neural networks and deep learning architectures

Convolutional neural networks (CNNs)

Recurrent neural networks (RNNs)

Natural language processing

Module 6: Model Deployment and Production

Deploying machine learning models

Model monitoring and maintenance

MLOps best practices

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Instructors

WDA

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