There is a very interesting Data Science course that was uploaded today.
The organization and contents of the course are exceptional.
Unlike many other courses, this one appears to offer both (a) a very realistic/accurate group of fundamentals, and (b) a very robust statistical (I'd say borderline advanced; far more than any other beginner course).
I have not watched this course; however, I looked-up the Table of Contents online and review all the sample videos. The course on SoftArchive does not include the Table of Contents and this is what's crucial to appreciate about this course -- for that reason, I've re-formatted into this post.
The URL on SoftArchive: [Login to see the link]
Learn Data Mining & Machine Learning with Python
Introduction -- 11 lectures • 25min
Introduction to Course
Control a pace of a videoPreview
Introduction to Data MiningPreview
Data Mining Definition1 question
Business Applications of Data MiningPreview
Data Mining Process Pyramid
Introduction to Machine LearningPreview
Machine Leaning Sub-fields.1 question
How Does Machine Learning Work
Train and Test Sets.1 question
Machine Learning Algorithms Types
Machine Leaning Types1 question
Reinforcement Learning overview
Course Rating
Setup Programming Environment -- 8 lectures • 37min
Install Anaconda package
Introduction to Jupyter
Introduction to Python Part-1(Create Lists)
Introduction to Python Part-2 (Create Tuples & Dictionaries)
Introduction to Python Part-3 (Loops & Functions)
Introduction to Pandas Library
Introduction to NumPy & Matplotlib Libraries
Introduction to Scikit-learn Library
Supervised Learning Algorithms -- 50 lectures • 3hr 29min
Introduction to Supervised Learning AlgorithmsPreview
Types of Variables
Data Types1 question
Introduction to Regression Analysis
Regression Model1 question
Regression Model Slope
Regression Slope1 question
The Intercept Value
The Intercept Value1 question
R-Squared Value
P-Value
Simple Linear Regression
Concepts used in Machine Learning
Overview on the dataset
Create Simple Linear Regression Model in Python-Part 1Preview
Create Simple Linear Regression Model in Python-Part
Create Simple Linear Regression Model in Python-Part
Create Simple Linear Regression Model in Python-Part
Create Simple Linear Regression Model in Python-Part
Multiple Linear Regression
Dummy Variables
Dummy Variables Trap
Stepwise Approach
Assumptions of Multiple Linear Regression
Overview on the business problem data
Create Multiple Linear Regression Model in Python-Part
Create Multiple Linear Regression Model in Python-Part
Create Multiple Linear Regression Model in Python-Part
Create Multiple Linear Regression Model in Python-Part
Polynomial Regression
Overview on the business problem data
Create Polynomial Regression Model in Python-Part
Create Polynomial Regression Model in Python-Part
Create Polynomial Regression Model in Python-Part
Course Rating
Introduction to Classification
Introduction to Logistic Regression
Confusion Matrix
Standard Scaler
Overview on the business problem data
Create Logistic Regression Model in Python-Part
Create Logistic Regression Model in Python-Part
KNN Classification Algorithm
Create KNN Model in Python
Support Vector Machine (SVM) Classification Algorithm
Create Support Vector Machine in Python
Naive Bayes Algorithm Part
Naive Bayes Algorithm Part
Create Naive Bayes Model in Python
Decision Tree Algorithm
Create Decision Tree Model in Python
Random Forest Algorithm
Create Random Forest Model in Python
Course Rating
Unsupervised Learning Algorithms -- 15 lectures • 1hr 14min
Review Unsupervised Learning Algorithms
Hierarchical Clustering Algorithm
Dendrogram Diagram Method
Overview on the business problem data
Create Hierarchical Clustering Algorithm in Python-
Create Hierarchical Clustering Algorithm in Python-
K-means Clustering Algorithm
Using Elbow Method to Determine Optimal Number of Clusters
Create K-means Clustering Algorithm Model in Python -
Create K-means Clustering Algorithm Model in Python -
Association Rules (Market Basket Analysis)
Overview on the business problem data
Create Association Rules (Market Basket Analysis) Model in Python -
Create Association Rules (Market Basket Analysis) Model in Python -
Create Association Rules (Market Basket Analysis) Model in Python -
Deep Learning -- 10 lectures • 44min
Introduction to Deep Learning
Use Deep Learning in Classification
How Does Deep Learning Work
Activation Functions
What is Tensorflow
Introduction to the Deep Learning Problem and Dataset
Create Artificial Neural Network Model in Python Part-
Create Artificial Neural Network Model in Python Part-
Create Artificial Neural Network Model in Python Part-
Course Rating
Appendix Statistics Overview -- 41 lectures • 2hr 9min
What is Statistics
Sample And Population
Descriptive and Inferential Statistics
Data types
Visualize Data
Histogram
Central Tendency Measures
Variability Measures
Calculate Central and Variability Measures (Practical)
Symmetry and skewness in data
Correlation and Covariance
Introduction to Inferential Statistics
Discrete Probability Distributions
Normal Distribution
Variable standardization
Variable standardization Demo
Introduction to Central Limit Theorem
Estimators
Introduction to Confidence Interval
Calculate Confidence Interval for one Sample with a Known Population Variance
Introduction to the Business Problem
Calculate Confidence Interval in Excel
t - Distribution
Calculate Confidence Interval for one Sample with a Unknown Population Variance
Reduce Margin of Error
Confidence Interval for two Dependent Samples
Calculate Confidence Interval for two Dependent Samples in Excel
Confidence Interval for two Independent Samples with a Known Population Variance
Calculate Confidence Interval for two Independent Samples Known Var in Excel
Confidence Interval for two Independent Samples Unknown Population Variance
What is a Statistical Hypothesis
Types of Hypotheses
P-Value
Link to z-value Calculator
Testing a Hypothesis for one Sample, Variance is Known
Testing the Hypothesis in Excel
Testing a Hypothesis for one Sample, Variance is Unknown
Testing a Hypothesis for two Dependent Samples
Link to t-value Calculator
Testing a Hypothesis for two Independent Samples, Variance is Known
Testing a Hypothesis for two Independent Samples, Variance is Unknown