top of page

Python Course

The objective of this course is to provide a structured and holistic learning experience, ensuring participants gain both theoretical knowledge and practical skills in the domains of Python programming, data analysis, machine learning, and artificial intelligence.

 

Overall Course Objectives:

  • Comprehensive Understanding: Provide a comprehensive understanding of Python, data analysis, statistics, machine learning, and artificial intelligence concepts.

  • Hands-on Experience: Develop practical skills through hands-on projects, assignments, and real-world applications.

  • Problem Solving: Enhance problem-solving skills by applying machine learning and AI techniques to solve real-world problems.

  • Project Development: Encourage participants to work on a final project integrating multiple concepts learned throughout the course.

  • Prepare for Industry: Equip participants with skills relevant to data science and AI roles in various industries.

  • Continuous Learning: Foster a mindset of continuous learning in the rapidly evolving fields of data science and AI.

FOUNDATIONS

MODULE 1: Introduction to Python

 

  • Python Basics

 

  • Data Types, control statements

 

  • Working with lists, dictionaries

 

  • Python Functions and Packages

 

  • Working with Data Structures, Arrays, Vectors & Data Frames

 

  • Jupyter Notebook – Installation & Function

 

  • Pandas, NumPy, Matplotlib, Seaborn

MODULE 2: Data Analysis and Preprocessing

 

  • Dispersion & Skewness

 

  • Uni & Multivariate Analysis

 

  • Data cleaning

 

  • Identifying and Normalizing Outliers

 

MODULE 3: Applied Statistics

 

  • Descriptive Statistics

 

  • Probability & Conditional Probability

 

  • Hypothesis Testing

 

  • Inferential Statistics

 

MACHINE LEARNING

 

MODULE 1: Supervised learning

 

  • Linear Regression

 

  • Multiple Variable Linear Regression

 

  • Logistic Regression

 

  • Naive Bayes Classifiers

 

  • k-NN Classification

 

  • Support Vector Machines

 

MODULE 2: Ensemble Techniques

 

  • Decision Trees

 

  • Bagging

 

  • Random Forests

 

  • Boosting

 

MODULE 3: Unsupervised learning

 

  • K-means Clustering

 

  • Hierarchical Clustering

 

  • Dimension Reduction-PCA

 

MODULE 4: Featurisation, Model Selection & Tuning

 

  • Feature engineering

 

  • Model selection and tuning

 

  • Model performance measures

 

  • Regularising Linear models

 

  • ML pipeline

 

  • Bootstrap sampling

 

  • Grid search CV

 

  • Randomized search CV

 

  • K fold cross-validation

 

 

ARTIFICIAL INTELLIGENCE

 

MODULE 1: Introduction to Neural Networks and Deep Learning

 

  • Introduction to Perceptron &

 

  • Neural Networks

 

  • Activation and Loss functions

 

  • Gradient Descent

 

  • Batch Normalization

 

  • TensorFlow & Keras for Neural Networks

 

  • Hyper Parameter Tuning

 

MODULE 2: CNN

 

  • Introduction to Convolutional Neural Networks

 

  • Introduction to Images

 

  • Convolution, Pooling, Padding & its Mechanisms

 

  • Forward Propagation & Backpropagation for CNNs

 

MODULE 3: NLP (Natural Language Processing)

 

  • Introduction to NLP

 

  • Stop Words

 

  • Tokenization

 

  • Stemming and Lemmatization

 

  • Bag of Words Model

 

  • Word Vectorizer

 

  • TF-IDF

 

MODULE 4: Introduction to Sequential data

 

  • RNNs and its Mechanisms

 

  • Vanishing & Exploding gradients in RNNs

 

  • LSTMs - Long short-term memory

 

  • GRUs - Gated Recurrent Unit

 

  • LSTMs Applications

 

  • Time Series Analysis

Final Project and Review

  • Participants work on a machine learning project

  • Final project presentations

  • Review and Q&A

Course Duration: 4-5 Months (6weeks+7weeks+6weeks + 1week)

Course Fee: ₹ 80,000 (₹ 30,000 + ₹ 25,000 + ₹25,000)

DALL·E 2023-12-02 21.54.18 - Create a realistic portrait of a person as a stock market tra
bottom of page