Python Data Course Description
Our Python Machine Learning Course is a practical, instructor-led course for learners with basic Python knowledge who want to develop real-world machine learning skills. You will learn how to use Python and scikit-learn to prepare and analyse data and build, test and evaluate machine learning models. The course introduces supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, feature engineering and data preprocessing. Through practical exercises and examples, you will gain hands-on experience applying machine learning techniques to real-world problems and develop a solid foundation for continuing your studies in Python machine learning and artificial intelligence.
Who should attend the Python Data Course?
Most attendees are in-work Data Professional, with working Python knowledge. Private individuals are very welcome.
Python Data Course Details
Our Style: Hands-on, Practical
Location: Online, Instructor-led
Download: anaconda.com, install Pandas and NumPy.
Duration: 1 Data
Times: check the booking link
Why choose this course?
- Hands-on Learning: Get practical Python experience through real-world machine learning examples and exercises.
- Expert Python Instructors: Learn from industry Python machine learning professionals with proven expertise.
- Career Advancement: Boost your resume with valuable Python machine learning skills, making you more competitive in the job market.
- Flexible Scheduling: Choose a time that works for you, with our online, instructor-led Python sessions.
Python Data Course Outline
Supervised Machine Learning:
Classification Algorithms:
Classification Algorithms:
Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
Regression Algorithms:
Linear, Polynomial.
Unsupervised Machine Learning:
Clustering Algorithms: K-means clustering, Hierarchical Clustering
Dimension Reduction Algorithms:
Principal Component Analysis Latent Dirichlet allocation (LDA)
Association Algorithms:
Apriori, Euclat
Other machine learning Algorithms:
Reinforcement learning Algorithms: Q-Learning
Ensemble Methods Algorithms:
Stacking, bagging, boosting.
Random
Forest Random Forest, Gradient Boosting
Neural Networks and Deep Leaning Algorithms:
Convolutional Network (CNN)
Data Exploration and Preprocessing:
The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.
Injecting domain knowledge in the process, attributes are extracted from the data and engineered into Machine Learning algorithms.
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