Week 1: Introduction to Data Science
1. Motivation, popularity, objectives and outcomes of the course.
2. Difference between AI, Machine Learning and Data Science.
3. Basic introduction of python, Google Colab and their features
4. Popular Dataset Repositories along with discussion on some datasets
Week 2: Statistical concepts
1. Introduction to statistics, types of statistics, types of data and describing data.
2. Measures of centrality and variance.
3. Sampling and hypothesis testing.
4. Introduction to probability theory.
Week 3: Basic Python: Part-I
1. Data Types, Input/Output, Operators: Precedence and Associativity.
2. Decision Making and Looping
3. Function and its syntax, Positional arguments, Keyword arguments etc.
4. Exercises on Loops, decision making and functions
Week 4: Basic Python: Part-II
1. String: Operations & Functions, List: Accessing List, Slicing, Cloning/Copy etc.
2. Set and Tuple: Accessing, Slicing, Operations & Functions.
3. Dictionary: Properties, Accessing Dictionary, Problems on Dictionary.
4. Exercises on String, Set, Tuple and Dictionary
Week 5: Advance Python: Part-I
1. Object Oriented Programming (OOP) concepts, Classes, Constructor, Inheritance and Polymorphism.
2. Introduction to Module, Package: Creation and Hierarchy
3. Error, Handling Exceptions and File Handling
4. Exercises on OOP concepts, module, exception and file handling
Week 6: Advance Python: Part-II
1. Numpy: Basics, Array Attributes, Slicing (1D, 2D and 3D), Copy and View.
2. Numpy Array Iteration, Linear algebra operations array, array operations.
3. Pandas: Basics, Series and DataFrames, Load files to DataFrames and Methods of Pandas
4. Exercises on Numpy and Pandas.
Week 7: Advance Python: Part-III
1. Data Visualization: Histogram, Barplot, Kernel density estimation curve, Correlation matrix plot, Scatter plot etc.
2. Sklearn library: Basics, Properties and functionalities.
3. Introduction to Deep Learning Frameworks: Tensorflow, Keras and PyTorch.
4. Exercises in data visualization and sklearn library.
Week 8: Data Preprocessing
1. Data Preprocessing, Handling missing values, Class Imbalance and its remedies
2. Feature Scaling, Transformation, Discretization, Image and Text Preprocessing
3. Dimensionality Reduction, Feature Ranking, Feature Selection and Feature Extraction
4. Exercises on Data Preprocessing and Dimensionality Reduction
Week 9: Supervised Learning
1. Machine Learning, Supervised Learning, Regression and Classification, Training and Testing data.
2. Linear Regression, K-Nearest Neighbor and Decision Tree algorithms.
3. Performance measures: Error, Accuracy, Precision, Recall, Confusion Matrix and AUC Score.
4. Exercises on supervised learning algorithms.
Week 10: Unsupervised Learning
1. Logistic Regression, Artificial Neural Networks: Perceptron and Multilayer Perceptron
2. Parameters, Hyper parameters, Underfitting, Overfitting, Regularization etc.
3. Unsupervised Learning: Clustering, K-Means Clustering and Hierarchical Clustering
4. Exercises on Logistic Regression, Artificial Neural Networks and Clustering
Week 11: Introduction to Deep Learning
1. Deep Learning: Introduction, Difference and Similarity between Machine Learning and Deep Learning
2. Image Classification and Convolutional Neural Networks.
3. Text Classification and Recurrent Neural Networks
4. Exercises on image and text classification using deep learning models.
Week 12: End to end project
1. Steps for end to end machine learning project.
2. End-to-end implementation of various real-life projects
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