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₹15000
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Duration: 4 months
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Delivery mode: Online
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Group size: Individual
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Instruction language:
English,
Hindi,
Bengali
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Certificate provided:
Yes
These are the modules I teach
Module 1: Beginning Your Data Science Journey
Setting Up Your Python Environment
Installing Jupyter Notebook
The Importance of Learning Data Science
Understanding the Field of Data Science
Essential Tools for Data Science
Module 2: Harnessing Python and Jupyter's Power
Working with Python Data Types and Operators
Getting Familiar with Jupyter Notebook
Exploring Basic Data Types
Understanding Comparison and Logical Operators
Module 3: Exploring Lists, Tuples, and Dictionaries
Lists and How to Use Indexing
Advanced Indexing Techniques
Modifying Data Within Lists
Introduction to Tuples
Getting to Know Python Dictionaries
Module 4: Crafting Python Functions
Writing Functions in Python
Working with Function Arguments
Understanding Methods
Creating User-Defined Functions
Exploring Nested and Lambda Functions
Module 5: Working with Loops and Conditionals
Using Conditional Statements (If Statements)
Implementing While Loops
Getting Comfortable with For Loops
Looping Through Dictionary Items
Module 6: Navigating Data with NumPy and Pandas
Introduction to 2D NumPy Arrays
Iterating Over NumPy Arrays
Creating DataFrames with Pandas
Slicing and Filtering DataFrames with Pandas
Utilizing NumPy and Pandas for Statistical Analysis
Module 7: Manipulating Data with Pandas
Importing and Exporting Data
Understanding Pandas Objects: Series and DataFrames
Common Functionality with Pandas Objects
Selecting and Modifying Data with Pandas
Combining and Reshaping DataFrames
Module 8: Data Visualization with Matplotlib
Introduction to Data Visualization
Exploring Matplotlib for Plotting
Creating Line Plots
Generating Bar Plots
Crafting Scatter Plots
Understanding Histograms
Customizing Graphs
Exploring Line of Best Fit
Delving into Box Plots
Analyzing Data with Pair Plots
Visualizing Time Series Data
Introduction to 3D Plotting
Exporting Figures for Sharing
Module 9: Exploring Statistics and Probability
Quiz on Statistics, Probability, and Linear Algebra
Understanding Probability and Statistics
Differentiating Probability vs. Statistics in Python
Sampling Techniques in Python
Exploring Random Variables and Probability Distributions
Analyzing Probability Mass and Density Functions
Module 10: Statistical Distributions and Hypothesis Testing
Overview of Statistical Distributions
Exploring the Uniform Distribution
Understanding Bernoulli and Binomial Distributions
Unveiling the Normal Distribution
Investigating Exponential, Poisson, and T Distributions
Confidence Intervals and Hypothesis Testing
The Data Cleaning Process and Strategies
Handling Missing or Duplicate Data
Concluding Data Cleaning Tasks
Module 11: Introduction to Exploratory Data Analysis
Getting Started with Exploratory Data Analysis (EDA)
Analyzing Descriptive Statistics, Frequencies, and Averages
Understanding Correlation in Data
Visualizing Data in EDA
Data Preprocessing for Analysis
Summary of Exploratory Data Analysis
Module 12: Introduction to Linear Algebra
Foundations of Linear Algebra
Matrices and Vectors
Operations with Matrices
Dot Product and Cross Product
Matrix Multiplication and Division
Transposing Matrices
Determinants, Inverses, and More
Linear Independence and Eigenvalues
Singular Value Decomposition (SVD)
Principal Component Analysis (PCA)
Maximum Likelihood Estimation (MLE)
Module 13: Supervised and Unsupervised Machine Learning
Overview of Supervised Machine Learning
Introduction to Unsupervised Machine Learning
The Basics of Data Modeling
Multivariate Data Analysis with Gaussian Distributions
Understanding Probabilistic Models
Linear Regression: A Foundational Technique
Practical Example of Linear Regression
Insight into Least Squares
Expanding the Scope of Linear Regression
Module 14: Understanding Regression Techniques
Geometry Behind Least Squares Regression
Essential Concepts of Linear Regression
Probabilistic Perspective on Linear Regression
An Exploration of Probability in Regression
Introducing Ridge Regression
Unveiling the Role of Regularization
The Balance Between Bias and Variance
Cross-Validation: A Model Evaluation Technique
Bayesian Inference: A Probabilistic Approach
Applying Bayesian Concepts to a Coin Toss Example
Module 15: Exploring Bayesian Methods
The Essence of Bayesian Methods
Instructions for Applying Bayesian Principles
Bayesian Linear Regression
Applications of Posterior Distribution
The Concept of Active Learning
Analytical Tools for Bayesian Analysis
Using Lagrange Multipliers for Optimization
Sparse Regression Techniques
Insights into Lp Regression
Module 16: Introduction to Classification Techniques
Understanding Classification
Optical Character Recognition with NN Classifier
Exploring the K-Nearest Neighbors Classifier
Statistical Foundations of Classification
Unveiling Optimal Classification Strategies
Embracing the Bayes Classifier
Gaussian Class Conditional Densities
Multivariate Gaussian Classification
Plug-In Classifiers: The Practical Approach
Linear Classification and Hyperplanes
Generalizing Classification to Polynomial Forms
Least Squares in Classification Tasks
Module 17: Delving into Logistic Regression
An In-Depth Look at Logistic Regression
The Likelihood in Logistic Regression
Unraveling the Logistic Regression Algorithm
Laplace Approximation: A Probabilistic Technique
Kernel Methods and the World of Gaussian Processes
Expanding Features with Kernel Techniques
A Comprehensive Study of Kernels
Kernelized Perceptron: A Deep Dive
Regression with Kernel Functions
The Magical World of Gaussian Processes
Module 18: The Art of Support Vector Machines
Maximum Margin Classifiers: The Foundation
Support Vector Machines: A Crucial Tool
Primal and Dual Problems in SVM
Soft-Margin SVM: Balancing Act
An Introduction to Decision Trees
Basics of Decision Tree Learning Algorithm
The Power of Bootstrapping
Bagging and Random Forest: A Closer Look
Two Exciting Projects Await
Module 19: Boosting and the World of Clustering
Boosting: A Technique to Boost Decision Stumps
Application Spotlight: Face Detection
A Detailed Analysis of Boosting
Unsupervised Learning: The Exploration Begins
Clustering: Grouping Similar Data Points
Understanding the Convergence of K-Means
Real-World Applications of K-Means Clustering
Module 20: Expectation-Maximization and Beyond
Maximum Likelihood: Laying the Groundwork
The Expectation-Maximization (EM) Algorithm
Navigating the EM Algorithm
EM for Handling Missing Data
Soft Clustering vs. Hard Clustering
Unveiling Gaussian Mixture Models
A Closer Look at the M-Step
An Example Run with Gaussian Mixtures
EM for Generic Mixture Models
Two Intriguing Projects Await
Module 21: Collaborative Filtering and Topic Modeling
Collaborative Filtering: A Puzzle to Solve
Matrix Factorization: The Key to Model Inference
Probabilistic Matrix Factorization
A Dive into Topic Modeling
Latent Dirichlet Allocation: Unveiling the Technique
Nonnegative Matrix Factorization: Exploring the Concept
Dual Objective Functions in NMF and Topic Modeling
Module 22: Principal Component Analysis (PCA)
Principal Component Analysis: A Dimension Reduction Technique
The Fundamentals of PCA
The Probabilistic Aspect of PCA
The Intricacies of Kernel PCA
Personalization through Dimension Reduction
Crafting Recommender Systems for Travelers
Module 23: Hidden Insights with Markov Models
Exploring Markov Models
Sequences and Their Significance
The Dynamics of Markov Chains
The First Order Markov Chain
Delving into State and Stationary Distributions
A Glimpse into Ranking Algorithms
Classification: A Continuing Journey
Module 24: Unveiling Hidden Patterns with HMMs and Kalman Filter
Understanding Hidden Markov Models
Learning the Art of HMM
Kalman Filtering: An Algorithmic Marvel
The Revisiting of Markov Models
Mastering Kalman Filtering Techniques
Module 25: Discovering Patterns with Association Analysis
The World of Association Analysis and Rules
Basket Processing: A Crucial Step
Dependencies in Frequency
Unearthing Association Rules
Selecting Models and Parameters
BIC: An Essential Derivative
Automated Tracking of Basketball Statistics
Module 26: Deep Learning and Text Analysis
The Deep Learning Abyss: A Journey Commences
The Intricate World of Deep Neural Networks
Activation Functions: The Heart of Deep Learning
Loss Functions: Understanding Model Errors
Gradients and Optimization Techniques
The Mathematical Formulations of Deep Neural Networks
A Glimpse into Convolutional Neural Networks
Handling Pixels, Edges, and Sharpening Images
Module 27: Language Secrets: Ciphers, Models, and Analysis
The Enigma of Ciphers
Language Models: Decoding Text Patterns
Sentiment Analysis: Extracting Emotions from Text
The Power of Trigrams
Building an Article Spinner in Python
Thanks and Regards,
Dr. Souptik Mukherjee