Machine Learning A-Z
Learning and practicing various Machine Learning algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning. Some additional content in the field of Deep Learning and Neural Networks are also covered.
Skills for certificate:
Python
Scikit Learn
NumPy
Pandas
Matplotlib
Seaborn
Keras
TensorFlow
Jupyter Notebooks
Git
GitHub
Poetry
Computer Vision
Mathematics
Linear Algebra
Probability
Problem Solving
Project Management
Critical Thinking
Creativity
Adaptability
Object Oriented Programming
Algorithms
Machine Learning
Data Science
Continuous Integration
Neural Networks
Data Visualisation
Artificial Intelligence
Deep Learning
Reinforcement Learning
Version Control
Machine Learning A-Z
UC-8b801bf6-1e95-4195-8286-36000142e133
Description
Learning and practicing various Machine Learning algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning. Some additional content in the field of Deep Learning and Neural Networks are also covered.
Learning Objectives
- Understanding and implementing data cleaning techniques including normalization, handling missing data, and encoding categorical data
- Understanding and applying regression concepts
- Implementing, interpreting, and comparing simple linear regression models
- Creating, analyzing, and comparing multiple linear regression models
- Applying, comparing, and interpreting polynomial regression to linear regression
- Using, tuning, and evaluating SVR for regression tasks
- Building, evaluating, and comparing decision tree regression models
- Constructing, assessing, and comparing random forest regression models
- Understanding and applying classification algorithms
- Applying, interpreting, and evaluating logistic regression for classification problems
- Using K-NN for classification, selecting optimal K values, and evaluating performance
- Implementing SVM for classification, understanding SVM kernel functions, and comparing kernel SVM with linear SVM
- Applying, interpreting, and evaluating Naive Bayes for classification tasks
- Building, analyzing, and comparing decision tree classifiers
- Creating, comparing, and evaluating random forest classifiers with decision tree classifiers
- Understanding and applying clustering techniques
- Applying K-means clustering to datasets, selecting optimal cluster numbers, and evaluating performance
- Using hierarchical clustering methods, interpreting dendrograms, and evaluating results
- Understanding and applying association rule concepts
- Implementing Apriori algorithm, extracting frequent item sets, and comparing with Eclat algorithm
- Applying Eclat algorithm, extracting frequent item sets, and comparing with Apriori algorithm
- Understanding, applying, and comparing reinforcement learning principles
- Using, analyzing, and comparing UCB for decision-making tasks
- Applying, analyzing, and comparing Thompson Sampling with UCB for decision-making
- Understanding and applying NLP techniques for text analysis using various libraries and tools
- Understanding and applying deep learning models
- Building, training, and evaluating ANN models
- Implementing, analyzing, and evaluating CNNs for image processing
- Understanding and applying dimensionality reduction techniques
- Applying, interpreting, and comparing PCA for feature reduction with LDA
- Using, implementing, and analyzing kernel PCA for nonlinear data
- Understanding and applying model selection and boosting methods
- Applying K-Fold Cross Validation, using Grid Search for hyperparameter tuning, and implementing XGBoost for model boosting
- Evaluating and comparing performance of models using XGBoost