Product Description

Machine Learning Course – SyllabusMachine Learning Course – Syllabus
1. Getting Started (Basic)
Topics: Intro Getting What You Need (Software Setup) Python Basics Hands On:  Python Basics

2. Statistics and Probability Refresher, and Imp. Python Libs Tutorial (Basic)
Topics:  Types of Data Mean, Median, Mode Probability Density Function Probability Mass Function Common Data Distributions Hands On:  Mean, Median, and Mode Variation and Standard Deviation Percentiles and Moments Matplotlib Tutorial Numpy & Pandas Tutorial Scikit Learn Tutorial Covariance and Correlation

3. Machine Learning Foundations (Intermediate) Topics: I. Overview Introduction Decision Trees Naive Bayes Gradient Descent Linear Regression Logistic Regression SVM Neural Networks Kernel Method Recap and Exercise K-means Clustering Hierarchical Clustering ConclusionII. Model Training and Hyper Parameter TuningIII. Testing ModelsIV. Evaluation Metrics Confusion Matrix Accuracy Precision Recall F1 Score F-beta Score ROC Curve Regression MetricsV. Error Detection Model Complexity Graph K Fold Cross Validation Learning CurvesVI. Outro

4. Mastering Supervised Learning (Advanced) Topics:I. Supervised Learning IntroII. Introduction to Regression III. Decision Trees IV. Neural Networks V. SVMsVI. Naive Bayes VII. Ensemble B&B VIII. Outro Hands On: Mini Project on Regression Mini Project on Decision Trees Mini Project on Neural Networks  Mini Project on SVMs Mini Project on Naïve Bayes Mini Project on Ensemble B&B

5. Mastering Unsupervised Learning (Advanced) Topics:
I. Introduction to Unsupervised Learning II. Clustering III. Feature Scaling IV. Feature Selection V. PCA VI. Feature Transformation VII. Outro Hands On: Mini Project on Clustering

6. Capstone Project
Note: If student completes all the Mini Projects and the Exercises mentioned in the course successfully, he/she will have equipped with enough hands on and experience by using which one can develop production level Machine Learning Solution.