Machine Learning With Python
IIIT-Bhagalpur offers Certified project based learning Program in “Machine Learning with Python”
Machine Learning is field of computer science that uses
statistical techniques to give computer systems the ability to
learn with Data without explicitly being programmed. This
course exposes you to different classes of machine learning
algorithms like supervised, unsupervised and reinforcement
algorithms. This course imparts you the necessary skills
and practical exposure on skillsets like data pre-processing,
dimensional reduction, model evaluation and also exposes
you to different machine learning algorithms like regression,
clustering, decision trees, random forest, Naive Bayes and
Q-Learning.
Especially developed in collaboration with academia & AI Industry
experts to reskill and retool working professionals towards
Artificial Intelligence space, this program offers the benefits of
Experienced Faculty Led Sessions: Live-Interactive Online classes
and practical exposure. Program content & structure designed in
collaboration with faculty and Industry experts.
6
Interactive Modules
50
Hours of Learning
2
Industry Projects
6
Assignments
This course is focused on building industry-ready professionals who can work on Machine Learning & it will provide in-depth understanding of Artificial Neural Networks, Data Analytics for Machine Learning and its mechanism. Furthermore, you will be provided with project based Learning which is an important to gain expertise in Artificial Intelligence. With this program, one will be able to automate real life scenarios using Machine Learning Algorithms. As part of the course, practical use cases of Machine Learning with Python programming language will be discussed for better & improved learning experience.
Syllabus Structure
What you will learn from this course
- Machine Learning Categories
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Frameworks for Building Machine Learning Systems
- Machine Learning Python Packages
- NumPy
- Pandas
- Matplotlib
- Machine Learning Core Libraries
- Dealing with Missing Data
- Handling Categorical Data
- Normalizing Data
- Feature Construction or Generation
- Exploratory Data Analysis
- Univariate Analysis
- Multivariate Analysis
- Classification
- Collecting, preparing and exploring the data
- Regression
- Multivariate regression
- KNN algorithm
- Classification using Decision Trees and rules
- The naïve Bayes classification
- Preparing and exploring data
- Training a model on the data
- Evaluating the model performance
- Logistic Regression
- Support Vector Machine
- Artificial Neural Network
- Clustering as a machine learning task
- Hierarchical Clustering
- K-means
- Finding Value of k
- Principal Component Analysis (PCA)
- Hidden Markov Mode
- Architecture of CNN
- Types of layers in CNN
- Building an image classifier using CNN
- Deep Learning with CNN
Our Faculty






Course FAQ
Required Skill-Set for Data Analytics - To be good at data analysis, one needs to have strong analytical and numerical skills and must have a thorough understanding of computer software(s) like Querying Language (SQL, Hive, Pig), scripting Language (Python, Matlab), Statistical Language (R, SAS, SPSS), and Excel.
- Participants will be able to understand and use python data science libraries as a tool for data analytics
- Participants will be able to create Python codes for the above techniques
- Participants will be create visualizations using python
- Job opportunities: Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist