machine learning basic

Course Features

Skill level:

Beginner

Duration:

 3 Months

Projects:

 Yes

Practical Ratio:

 30 : 70

Assessments:

 Yes

Quizzes:

 Yes

Course Details

The course aims to train participants to become familiar with data science and machine learning. It contains all the majorly used algorithms along with a lot of practical exercises & end-to-end data science implementations. The course starts with Python and also covers Statistics and Mathematics fundamentals used in Machine Learning.

Prerequisite

Language: Python or R.
Theory: Basic Mathematical Concepts.

Course Outcome

At the end of the course, you should be able to :

  • Gain a deep insight about the concept of Data Science
  • Apply Statistics & Algebra concepts in Machine Learning
  • Use & Apply the fundamental algorithms of Machine Learning for business use cases

Curriculum

  • Basics of Data Science & Machine Learning.
  • Different Tools – R, Python, Matlab etc.
  • Data Science Use Cases
  • Exploratory Analysis
  • Distributions, CDF, PDF
  • Distribution Modeling
  • Variable Relationships & Estimation
  • Hypothesis Testing
  • Regression & Linear Least Square
  • Analytic Methods
  • End-2-End cycle of ML Implementation
  • Data Cleaning & Wrangling
  • Data Compression & Dimensionality Reduction
  • Missing Values & Outlier
  • Feature Generation & Importance
  • ML for Feature Engineering & Pre-Processing
  • Gradient Descent
  • Linear Regression
  • Logistic Regression
  • Regularization
  • Multiple Variables
  • Time Series
  • Decision Trees
  • Random Forest
  • Boosting Trees
  • XGBoost
  • Support Vector Machines
  • Principal Component Analysis
  • Ridge Regression
  • Spectral Clustering
  • KNN
  • K-Means
  • Hierarchical Clustering
  • Data Split Practices
  • Cross Validation
  • K-Fold Validation
  • Confusion Matrix
  • ROC Curves
  • Mean Absolute/Square Errors & R-Square
  • Ensemble Learning & Model Stacking
  • Visualize Model Outputs & Intermediate Outputs
  • Python Tools for Visualizations
  • Setting up Environment
  • Pickle File Creation & loading
  • Flask/Django using Python.
  • Deploying & Prediction