With **An Introductory Guide to Data Science and Machine Learning** you will get a 40% discount on yearly plans and a 20% monthly discount oneducative.io. It is one of the popular courses from educative.io

This course will get you familiar with the state of data science and the related fields such as machine learning and big data.

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## Here is what you will Learn from 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
- The Good Traits of a Data Scientist
**2. Applications of Data Science**- Applications in Healthcare 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 Locations
- Measures of Variability
- Probability Distributions (Binomial and Bernoulli Distributions)
- Gaussian Distribution
- 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, MSE)
- Model Evaluation Measures (Median Absolute Error, 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, Light GBM and CatBoost
- Learning Curves
- Model Evaluation Part 1
- Model Evaluation Part 2
- Dummy Estimators and Handling Imbalance Class Problem
- Hyper-Parameter 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 Network
- 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
- Map Reduce Framework
- Apache Spark and it’s Components
**11. Where to go next ?**- Starting Career on Kaggle (Tips)
- Recommended Courses from Educative
- References and Acknowledgements