About Program
This online course aims to make you master in all the basic and advanced level skills in the various tools and technologies involved in the field of Data Science and Machine Learning.
Who Can Apply for the Course?
– Individuals with a bachelor’s degree and a keen interest to learn ML and Data Science
– IT professionals looking for a career transition as Data Scientists and Machine Learning Engineers
– Professionals aiming to move ahead in their IT career
– Developers and Project Managers
– Freshers who aspire to build their career in the field of ML and Data Science
Curriculum for this Course
Introduction to Python
- Introduction
- Installation and Introduction to Jupyter Notebook
- Basics
- Lists
- Tuples
- Dictionaries
- Sets
Python for Data Science - Numpy
- Introduction to Numpy
- Numpy Arrays
- Numpy Array Indexing
- Numpy Operations
Python for Data Science - Pandas
- Introduction to Pandas
- Indexing and Selecting Data
- Merge and Append
- Grouping and Summarizing Dataframes
- Lambda functions and Pivot tables
- Getting and Cleaning Data
Data Visualisation in Python
- Introduction to Data Visualisation
- Plots using Matplotlib
- Plots using Seaborn
- Plots using Plotly
- Interactive Plots
Exploratory Data Analysis
- Data Sourcing
- Data Cleaning
- Univariate Analysis
- Segmented Analysis
- Bivariate Analysis
- Derived Metrics
Error Metrics
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- MSE and RMSE
Linear Regression
- Simple Linear Regression
- Multiple Linear Regression
- Model Building
- Model Evaluation
Logistic Regression
- Univariate Logistic Regression
- Multivariate Logistic Regression - Model Building
- Multivariate Logistic Regression - Model Evaluation
Tree Models
- Decision Tree
- Random Forest
Support Vector Machines
- SVM Theory
- Support Vector Machines with Python
K Means Clustering
- K Means Theory
- K Means with Python
Dimensionality Reduction
- Principal Component Analysis
- PCA with Python
Neural net and deep learning fundamentals
