Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R

Students : 1,087,024
Duration : 42.5 hours on-demand video , 5 coding exercises & 40 articles
Avg Rating : 4.5 (190,055 ratings)
Original Price : $119.99
Discount Price : $12
Instructor / Provider : Hadelin de Ponteves, Kirill Eremenko, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

Course Structure

• Part 1: Data Preprocessing – Set up the foundation for data analysis and machine learning.
• Part 2: Regression – Learn regression models and evaluate their effectiveness.
• Part 3: Classification – Explore various classification algorithms and their applications.
• Part 4: Clustering – Understand clustering techniques to group and analyze data.
• Part 5: Association Rule Learning – Extract insights using Apriori and Eclat algorithms.
• Part 6: Reinforcement Learning – Implement learning algorithms like UCB and Thompson Sampling.
• Part 7: Natural Language Processing – Process and analyze textual data with machine learning techniques.
• Part 8: Deep Learning – Build and train advanced deep learning models.
• Part 9: Dimensionality Reduction – Simplify datasets with PCA, LDA, and kernel PCA.
• Part 10: Model Selection & Boosting – Optimize and enhance models for maximum accuracy.

Frequently Bought Together

Foundations of Data Science and Machine Learning:

• Data Preprocessing: Handle missing data, process categorical data, and follow a general data preprocessing template.
• Regression Techniques: Master regression methods including:
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Model evaluation and regularization techniques.

Classification Methods:

• Explore classification algorithms:
  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM) and Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluate classification model performance.

Clustering:

• Understand and implement:
  • K-Means Clustering
  • Hierarchical Clustering

Association Rule Learning:

• Dive into algorithms like:
  • Apriori
  • Eclat

Reinforcement Learning:

• Master core reinforcement learning techniques:
  • Upper Confidence Bound (UCB)
  • Thompson Sampling
  • Q-Learning

Natural Language Processing (NLP):

• Process and analyze textual data using techniques like:
  • Bag-of-Words model
  • Decision Tree, Random Forest, and Max Entropy algorithms for NLP tasks.

Deep Learning:

• Build and optimize deep learning models:
  • Artificial Neural Networks (ANNs)
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)

Dimensionality Reduction:

• Reduce feature dimensions with:
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Kernel PCA

Model Selection and Boosting:

• Enhance model performance through:
  • k-Fold Cross Validation
  • Parameter Tuning and Grid Search
  • XGBoost for boosting model accuracy.

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