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:
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Comprehensive Understanding: Provide a comprehensive understanding of Python, data analysis, statistics, machine learning, and artificial intelligence concepts.
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Hands-on Experience: Develop practical skills through hands-on projects, assignments, and real-world applications.
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Problem Solving: Enhance problem-solving skills by applying machine learning and AI techniques to solve real-world problems.
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Project Development: Encourage participants to work on a final project integrating multiple concepts learned throughout the course.
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Prepare for Industry: Equip participants with skills relevant to data science and AI roles in various industries.
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Continuous Learning: Foster a mindset of continuous learning in the rapidly evolving fields of data science and AI.
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FOUNDATIONS
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MODULE 1: Introduction to Python
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Python Basics
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Data Types, control statements
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Working with lists, dictionaries
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Python Functions and Packages
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Working with Data Structures, Arrays, Vectors & Data Frames
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Jupyter Notebook – Installation & Function
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Pandas, NumPy, Matplotlib, Seaborn
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MODULE 2: Data Analysis and Preprocessing
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Dispersion & Skewness
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Uni & Multivariate Analysis
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Data cleaning
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Identifying and Normalizing Outliers
MODULE 3: Applied Statistics
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Descriptive Statistics
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Probability & Conditional Probability
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Hypothesis Testing
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Inferential Statistics
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MACHINE LEARNING
MODULE 1: Supervised learning
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Linear Regression
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Multiple Variable Linear Regression
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Logistic Regression
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Naive Bayes Classifiers
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k-NN Classification
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Support Vector Machines
MODULE 2: Ensemble Techniques
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Decision Trees
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Bagging
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Random Forests
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Boosting
MODULE 3: Unsupervised learning
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K-means Clustering
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Hierarchical Clustering
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Dimension Reduction-PCA
MODULE 4: Featurisation, Model Selection & Tuning
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Feature engineering
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Model selection and tuning
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Model performance measures
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Regularising Linear models
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ML pipeline
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Bootstrap sampling
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Grid search CV
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Randomized search CV
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K fold cross-validation
ARTIFICIAL INTELLIGENCE
MODULE 1: Introduction to Neural Networks and Deep Learning
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Introduction to Perceptron &
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Neural Networks
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Activation and Loss functions
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Gradient Descent
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Batch Normalization
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TensorFlow & Keras for Neural Networks
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Hyper Parameter Tuning
MODULE 2: CNN
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Introduction to Convolutional Neural Networks
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Introduction to Images
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Convolution, Pooling, Padding & its Mechanisms
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Forward Propagation & Backpropagation for CNNs
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MODULE 3: NLP (Natural Language Processing)
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Introduction to NLP
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Stop Words
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Tokenization
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Stemming and Lemmatization
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Bag of Words Model
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Word Vectorizer
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TF-IDF
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MODULE 4: Introduction to Sequential data
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RNNs and its Mechanisms
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Vanishing & Exploding gradients in RNNs
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LSTMs - Long short-term memory
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GRUs - Gated Recurrent Unit
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LSTMs Applications
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Time Series Analysis
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Final Project and Review
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Participants work on a machine learning project
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Final project presentations
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Review and Q&A
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Course Duration: 4-5 Months (6weeks+7weeks+6weeks + 1week)
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Course Fee: ₹ 80,000 (₹ 30,000 + ₹ 25,000 + ₹25,000)
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