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.
Unlimited Duration
March 3, 2021
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

Students