Numbers, operators, comments, and Data Types (Int, Float, Bool etc.).
Conditional Logic. {IF, ELIF, AND, OR, ==, etc.}.
Loops. {For and While}.
List, List comprehensions.
Dictionaries, Tuples, and Sets.
Functions and Lambda Functions.
HTTP requests.
Object Oriented Programming.
Iterators, Generators and Decorators.
File IO, Working with CSV.
Regular Expressions.
Libraries { Pandas, Numpy, Matplotlib, Scikit, Statsmodels}
Select.
Joins {Right Join, Left Join}.
Group by, Having, Order by etc.
Types of data {Numerical, Categorical, Ordinal etc.}.
Measures of Central Tendency.
Variance and Standard Deviation.
Probability Density Functions.
Probability Mass Functions.
Probability Distribution Functions {Bernoulli distribution, Normal Distribution, Poisson’s Distribution etc.}.
Powerbi.
Tableau.
Excel {VLook up, DAX}.
WEKA.
Regression {Linear Regression, Multinomial Regression, Polynomial Regression}.
Classification {Logistic Regression, Support Vector Machine}.
Clustering {K-Means, K-NN}.
Association.
Dimensionality Reduction {Principal Component Analysis, Linear Discriminant Analysis, Decision Trees}.
Bagging and Boosting {XGBoost, AdaBoost}.
Ensemble Learning.
Model Evaluation Metrics {MAPE, RMSE, MSE, confusion Matrix etc.}.
Reinforcement Learning.
Model Deployment {Docker, Joblib, Pickle, Kubernetes}.
Machine Learning Project.
Hadoop.
Apache Ambari.
Apache Spark.D.S.C Data Science Track Curriculum.
Cloud Computing
Amazon Web Services
Google Cloud Platform
Microsoft Azure
Artificial Neural Networks.
Convolutional Neural Networks.
Recurrent Neural Networks.
Generative Adversarial Networks.
Transfer Learning.
Tensor flow.
Keras.
Happy Learning.