The focus of the course is to provide students with an introduction to Data Science and Machine Learning using the Python programming language. Our course is designed to give you an overview of Python and associated tools used in machine learning. Classification, Regression and Clustering algorithms will be explored in this course focused on hand-on real life practical examples and experiences. This course gives you a first-hand learning experience about Machine learning. Comprehensive hands on exercises are integrated throughout course reinforces learning and sharpen one’s competency. If you are a machine learning beginner and looking to finally get started using Python, this course is designed for you.
Who Can Enroll For The Course?
Professionals who are looking for up-skilling and developing their career opportunities. Candidates should have good analytical, logical skills, awareness to programming languages and/or scripting knowledge. The professionals who are listed below can take up this course :
- Professionals aiming to be part of Data Analytics
- Software Developers and/or engineers
- System Analysts
- People from data science/analytics background
- College students
Benefits
- Master Python scripts fundamentals
- Learn core Python scripting, functions to facilitate code reuse
- Handling errors and exceptions properly
- Using Python standard library
- Explore Python’s object-oriented features
- Learn fundamentals of Data visualization
- Learn fundamentals of Deep Learning
Curriculum for this Course
- Built-in data types: strings, integers, floats, lists
- Introduction to built-in functions
- How to write custom Python functions
- Reading and writing data from CSV and TXT Files
- Anaconda: Installation & Overview
- Create Numpy Arrays
- Numpy Operations
- Numpy for Vector and Matrix Arithmetic
- Broadcasting with Numpy
- Numpy for statistical operations
- Data Structures in Python
- Read CSV Data using Pandas
- Read Excel Data using Pandas
- Data indexing and selection
- Aggregation, Filtering in Pandas
- Combining and merging of datasets
- Basic data handling
- Importing the libraries
- Reading datasets
- Removing NA's/No values from data
- Reshaping
- Data grouping based on Qualitative attributes
- Pivoting
- Encoding categorical data
- Splitting dataset into training and test set
- Merging and joining Data frames
- Feature scaling
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression models performance
- K-Nearest Neighbors(K-NN)
- Support Vector Machine(SVM)
- Decision Tree Classification
- Random Forest Classification
- Classification model selection
- Evaluating performance of classification models
- Logistic Regression
- K-Means clustering
- Hierarchical clustering
- Basic concept and introduction to tools available for visualization
- 2D plots & 3D plots using Matplotlib
- 2D plots & 3D plots using Plotly
- 3D animation in plots
- Interactive 3D plots using plotly
- Applications of Deep learning
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks(CNN)
- Principal Component Analysis(PCA)
- Linear Discriminant Analysis (LDA)
Student Reviews
Warning: count(): Parameter must be an array or an object that implements Countable in /home/trainon/public_html/beta/wp-content/themes/trainon/template-parts/content-course-single.php on line 78
