Machine Learning Fundamentals - An Introductory Guide to Data Science and Machine Learning

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An Introductory Guide to Data Science and Machine Learning

1.What Is Data Science ?

Data Science vs. Data Analysis vs. Data Engineering Descriptive and Predictive Analytics Data Science Life Cycle Structured vs. Semi-Structured vs. Unstructured Data

2.Applications of Data Science

Applications in Health care and Recommender Systems Image Analysis

3.Overview of Libraries

Beautiful Soup: Scraping Data from Simple HTML Beautiful Soup: Scraping Data from HTML Table Scrapy NumPy Basics NumPy Array Creation NumPy Array Manipulation Sorting NumPy Arrays Basic Statistics on Numpy Arrays Broadcasting in Numpy Arrays Pandas SpaCy: Part 1 SpaCy: Part 2 Seaborn

4.Probability and Statistics

Probability Statistics Joint Probability Conditional Probability and Bayes’ Theorem Measures of Location Measures of Variability Probability Distributions: Binomial and Bernoulli Distributions Probability Distributions: Gaussian Distribution Probability Distributions:: Poisson Distribution Skewness and Kurtosis Sampling Methods Key Concepts in Statistics Statistical Hypothesis Testing

5.Machine Learning Part-1

Machine Learning and its Types Deep Learning and Recommender Systems What Is Regression ?Univariate Linear Regression Multivariate Linear Regression Feature Scaling Linear Regression in Scikit-Learn Regularization (Lasso, Ridge, and ElasticNet Regression)Support Vector Regression Nearest Neighbour Regression Decision Tree Regression Feature Engineering and Categorical Variables Encoding Numerical Variables Transformation Feature Selection (Filter Methods)Feature Selection: Wrapper Methods Feature Selection: Intrinsic Methods Model Evaluation Measures: Explained Variance Score, MAE, and MSE Model Evaluation Measures: Median Absolute Error, and R^2 Score Dummy Regressors Cross Validation Case Study: House Prices Prediction Using Advanced Regression

6.Machine Learning Part-2

Types of Classification Problems Logistic Regression Support Vector Machines Decision Trees Naive Bayes: Part-1 Naive Bayes: Part-2 K-Nearest Neighbors Ensemble Learning: Part 1 Ensemble Learning: Part 2 XGBoost, LightGBM, and CatBoost Learning Curves Model Evaluation: Part 1 Model Evaluation: Part 2 Dummy Estimators and Handling Imbalance Class Problem Hyperparameter Optimization and Kaggle Competition

7.Machine Learning Part-3

Unsupervised Learning K-Means Clustering Hierarchical Clustering DBSCAN Clustering and Customer Segmentation Apriori Algorithm and Association Rules Principal Component Analysis for Dimensionality Reduction Semi-Supervised Learning Techniques

8.Deep Learning

What Is Deep Learning?Neural Networks Feedforward Neural Networks Backpropagation: Part 1 Backpropagation: Part 2 Convolutional Neural Network Recurrent Neural Networks LSTM Networks

9.Machine Learning Tools and Libraries

Automated Machine Learning Pandas Profiling and PyCaret RAPIDS (Using GPU for Fast Computations)

10.Big Data Tools and Technologies

What Is Big Data?Hadoop Ecosystem MapReduce Framework Apache Spark and it's Components

11.Where to go next ?

Starting Career on Kaggle Recommended Courses from Educative References and Acknowledgements

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Fundamentals of Data Science

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Machine Learning Fundamentals