ratings
Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that will teach you core skills and concepts in modern machine learning practices.
PRIVATE
Course Access
Unlimited Duration
Last Updated
March 3, 2021
Students Enrolled
Total Reviews
Posted by
Certification
This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. In this course you will learn about:
• Getting Started & Optional Python Quick Refresher
• Statistics and Probability Refresher and Python Practice
• Probability Density Function; Probability Mass Function; Naive Bayes
• Predictive Models
• Machine Learning with Python
• Recommender Systems
• KNN and PCA
• Reinforcement Learning
• Dealing with Real-World Data
• Experimental Design / ML in the Real World
• Time Permitting: Deep Learning and Neural Networks
Course Curriculum
-
- Installation: Getting Started and Overview 00:00:00
- LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) 00:00:00
- Python Refresher 00:00:00
- Introducing the Pandas, NumPy and Scikit-Learn Library 00:00:00
-
- Types of Data 00:00:00
- Mean, Median, Mode 00:00:00
- Using mean, median, and mode in Python 00:00:00
- Variation and Standard Deviation 00:00:00
- Common Data Distributions 00:00:00
- Percentiles and Moments 00:00:00
- A Crash Course in matplotlib 00:00:00
- Advanced Visualization with Seaborn 00:00:00
- Covariance and Correlation 00:00:00
- Conditional Probability 00:00:00
- Naive Bayes: Concepts 00:00:00
- Bayes’ Theorem 00:00:00
- Naive Bayes 00:00:00
- Spam Classifier with Naive Bayes 00:00:00
- Supervised vs. Unsupervised Learning, and Train/Test 00:00:00
- Using Train/Test to Prevent Overfitting 00:00:00
- Understanding a Confusion Matrix 00:00:00
- Measuring Classifiers (Precision, Recall, F1, AUC, ROC) 00:00:00
- K-Means Clustering 00:00:00
- K-Means: Clustering People Based on Age and Income 00:00:00
- Measuring Entropy 00:00:00
- LINUX: Installing GraphViz 00:00:00
- Decision Trees: Concepts 00:00:00
- Decision Trees: Predicting Hiring Decisions 00:00:00
- Ensemble Learning 00:00:00
- Support Vector Machines (SVM) Overview 00:00:00
- Using SVM to Cluster People using scikit-learn 00:00:00
- K-Nearest-Neighbors: Concepts 00:00:00
- Using KNN to Predict a Rating for a Movie 00:00:00
- Dimensionality Reduction; Principal Component Analysis (PCA) 00:00:00
- PCA with the Iris Data Set 00:00:00
- Bias / Variance Tradeoff 00:00:00
- K-Fold Cross-Validation 00:00:00
- Data Cleaning and Normalization 00:00:00
- Cleaning Web Log Data 00:00:00
- Normalizing Numerical Data 00:00:00
- Detecting Outliers 00:00:00
- Feature Engineering and the Curse of Dimensionality 00:00:00
- Imputation Techniques for Missing Data 00:00:00
- Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE 00:00:00
- Binning, Transforming, Encoding, Scaling, and Shuffling 00:00:00
- Group Project & Presentation or Review 00:00:00
- Deep Learning Prerequisites 00:00:00
- The History of Artificial Neural Networks 00:00:00
- Deep Learning in the TensorFlow Playground 00:00:00
- Deep Learning Details 00:00:00
- Introducing TensorFlow 00:00:00
- Using TensorFlow 00:00:00
- Introducing Keras 00:00:00
- Using Keras to Predict Political Affiliations 00:00:00
- Convolutional Neural Networks (CNN’s) 00:00:00
- Using CNN’s for Handwriting Recognition 00:00:00
- Recurrent Neural Networks (RNN’s) 00:00:00
- Using an RNN for Sentiment Analysis 00:00:00
- Transfer Learning 00:00:00
- Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters 00:00:00
- Deep Learning Regularization with Dropout and Early Stopping 00:00:00
- The Ethics of Deep Learning 00:00:00
- Learning More about Deep Learning 00:00:00
Course Reviews
ashar hafeez
0
62
Students
About Instructor
Pak
Course Events
[wplms_eventon_events]
