Introduction to AI, ML, and Data Science
Machine Learning Fundamentals
- What are models in AI?
- Machine learning landscape 101
- Machine learning landscape 102
- Interpolation vs Extrapolation: Common Methods with Python Code
Python Programming for Machine Learning
- Top 5 Python programs you must know
- Essential Python Snippets for Data Cleaning and Preparation
- Python Snippets for Outliers Treatment: Essential Snippets for Data Cleaning and Preparation
Statistics for Machine Learning
- Understanding Probability and Making Informed Decisions: Basics of Probability [Release Date: May 25, 2023]
- What is Probability Distribution Explained in Simple Terms? [Release Date: June 1, 2023]
- Understanding Permutation and Combination [Release Date: June 8, 2023]
- What are quartiles, deciles and percentiles in statistics?
- Standard deviation and variance in statistics
- What is data distribution in machine learning?
- Skewness for a data distribution
- Kurtosis for a data distribution
- Interpretation of Covariance and Correlation
- Lorenz Curve and Gini Coefficient Explained
- Normalization vs Standardization
- What is hypothesis testing in data science?
- What do you mean by Weight of Evidence (WoE) and Information Value (IV)?
- Statistics Interview Questions 101
Regression Analysis
- Linear Regression for Beginners: A Simple Introduction
- Linear Regression, heteroskedasticity & myths of transformations
- Bayesian Linear Regression Made Simple with Python Code
- Logistic Regression for Beginners
- Understanding Confidence Interval, Null Hypothesis, and P-Value in Logistic Regression
- Logistic Regression: Concordance Ratio, Somers’ D, and Kendall’s Tau
Machine Learning Techniques
- Regression Imputation: A Technique for Dealing with Missing Data in Python
- Dealing with categorical features with high cardinality: Feature Hashing
- A creative way to deal with class imbalance (without generating synthetic samples)
- Curse of Dimensionality: An Intuitive and practical explanation with Examples
- How to generate and interpret a roc curve for binary classification?
Credit Scoring
Time Series Analysis
- Introduction to time series
- Ensure time series forecasts stay within limits
- Mean directional accuracy of time series forecast
Advanced Topics
- The Simplest Guide to Federated Learning: The Future of Machine Learning
- Federated Learning with TensorFlow: A Practical Guide with Example Code
- Understanding KL Divergence in Machine Learning: Applications and Improving Model Accuracy
Generative AI
- Introduction to Generative AI: GenAI 101
- Fundamentals of Machine Learning for Generative AI: GenAI 101
Google Advanced Data Analytics Professional Certificate
I highly recommend checking out this incredibly informative and engaging professional certificate Training by Google on Coursera:
Google Advanced Data Analytics Professional Certificate
There are 7 Courses in this Professional Certificate that can also be taken separately.
- Foundations of Data Science: Approx. 21 hours to complete. SKILLS YOU WILL GAIN: Sharing Insights With Stakeholders, Effective Written Communication, Asking Effective Questions, Cross-Functional Team Dynamics, and Project Management.
- Get Started with Python: Approx. 25 hours to complete. SKILLS YOU WILL GAIN: Using Comments to Enhance Code Readability, Python Programming, Jupyter Notebook, Data Visualization (DataViz), and Coding.
- Go Beyond the Numbers: Translate Data into Insights: Approx. 28 hours to complete. SKILLS YOU WILL GAIN: Python Programming, Tableau Software, Data Visualization (DataViz), Effective Communication, and Exploratory Data Analysis.
- The Power of Statistics: Approx. 33 hours to complete. SKILLS YOU WILL GAIN: Statistical Analysis, Python Programming, Effective Communication, Statistical Hypothesis Testing, and Probability Distribution.
- Regression Analysis: Simplify Complex Data Relationships: Approx. 28 hours to complete. SKILLS YOU WILL GAIN: Predictive Modelling, Statistical Analysis, Python Programming, Effective Communication, and regression modeling.
- The Nuts and Bolts of Machine Learning: Approx. 33 hours to complete. SKILLS YOU WILL GAIN: Predictive Modelling, Machine Learning, Python Programming, Stack Overflow, and Effective Communication.
- Google Advanced Data Analytics Capstone: Approx. 9 hours to complete. SKILLS YOU WILL GAIN: Executive Summaries, Machine Learning, Python Programming, Technical Interview Preparation, and Data Analysis.
It could be the perfect way to take your skills to the next level! When it comes to investing, there’s no better investment than investing in yourself and your education. Don’t hesitate – go ahead and take the leap. The benefits of learning and self-improvement are immeasurable.