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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.

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Unlimited Duration

Last Updated

March 3, 2021

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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
    • Linear Regression 00:00:00
    • Polynomial Regression 00:00:00
    • Multiple Regression, and Predicting Car Prices 00:00:00
    • Logistic Regression 00:00:00
    • Logistic Regression 00:00:00
    • LDA : Linear Discriminant Analysis 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
    • User-Based Collaborative Filtering 00:00:00
    • Item-Based Collaborative Filtering 00:00:00
    • Finding Similar Movie 00:00:00
    • Better Accuracy for Similar Movies 00:00:00
    • Recommending movies to People 00:00:00
    • Improving your recommendations 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
    • Reinforcement Learning with Q-Learning and Gym 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
    • Deploying Models to Real-Time Systems 00:00:00
    • A/B Testing Concepts 00:00:00
    • T-Tests and P-Values 00:00:00
    • Hands-on With T-Tests 00:00:00
    • Determining How Long to Run an Experiment 00:00:00
    • A/B Test Gotchas 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

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