The Complete Data Science Journey: Analyze, Model, Predict — and Deploy AI
- Description
- Curriculum
- FAQ
- Reviews
The Ultimate Path to AI Mastery
Welcome to the most comprehensive Data Science program designed for the modern era. The Complete Data Science Journey is a meticulously crafted roadmap that takes you from absolute ground zero to building advanced Neural Networks and Transformer models.
Whether you are a student, an engineer, or a business professional, this 8-stage journey bridges the gap between theoretical intuition and production-ready AI deployment. This isn’t just a course; it’s your professional transformation.
Why This Is the Only Course You Need
| Program Pillar | Your Career Outcome |
|---|---|
| Full-Stack Curriculum | Master everything from Python automation to Deep Learning architectures. |
| The MasterPlaybook | Access interactive sandboxes to visualize complex math before you code. |
| Real-World Deployment | Don’t just build models; learn to deploy AI into live environments. |
| Industry Mentorship | Learn directly from a Lead Mentor with 12+ years of global analytical experience. |
The 8-Stage Curriculum Roadmap
Module 0: The Python Engine
Foundational Python, automation scripts, and data structures for analytics.
Foundational Python, automation scripts, and data structures for analytics.
Module 1: Statistical Intuition
Master probability and distribution to validate business hypotheses and spot patterns.
Master probability and distribution to validate business hypotheses and spot patterns.
Module 2: Predictive Relationships
Build regression models to forecast sales, pricing trends, and business growth.
Build regression models to forecast sales, pricing trends, and business growth.
Module 3: Classification Logic
The science of “Yes/No” outcomes. Master risk assessment and customer behavior prediction.
The science of “Yes/No” outcomes. Master risk assessment and customer behavior prediction.
Module 4: Tree-Based Decisions
Map complex processes through “Split Points” to optimize workflows and strategic planning.
Map complex processes through “Split Points” to optimize workflows and strategic planning.
Module 5: Pattern Discovery
Learn Clustering to group data by similarities—perfect for market segmentation.
Learn Clustering to group data by similarities—perfect for market segmentation.
Module 6: Neural Networks
Deep Learning foundations, Perceptrons, and implementation using PyTorch.
Deep Learning foundations, Perceptrons, and implementation using PyTorch.
Module 7: Modern AI & Transformers
The world of Large Language Models (LLMs), attention mechanisms, and Generative AI.
The world of Large Language Models (LLMs), attention mechanisms, and Generative AI.
Beyond the Curriculum
Graduate with a portfolio of 10+ industry projects, a professional certification, and the confidence to crack interviews at top-tier global firms.
“Don’t just learn Data Science. Master the journey.”
Module 0 : The Python Builder Track (Foundations & Data Wrangling)
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1
Introduction - Data-Driven Leadership: The New Industry Standard -
2
Introduction - Why Top Firms Choose Python over Excel
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3
Introduction - Overview of Python Ecosystem: pandas, NumPy, matplotlib, Jupyter
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4
Introduction - Setting Up Your Environment (Anaconda, IDEs, Jupyter Notebook)
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5
Essentials - Python Basics: Variables, Data Types, Loops, Functions
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6
Essentials - Working with Python Scripts and Jupyter Notebooks
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7
Essentials - Common Python Libraries for Data Science
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8
Data Structures - Lists, Tuples, Dictionaries, Sets
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9
Data Structures - List and Dictionary Comprehensions
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10
Data Structures - Functions and Lambda Expressions
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11
Data Structures - Reading from and Writing to Files
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12
NumPy - Arrays and Operations
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13
NumPy - Indexing, Slicing, and Reshaping Arrays
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14
NumPy - Mathematical and Statistical Operations
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15
NumPy - Random Number Generation and Simulations
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16
Pandas - Introduction to pandas: Series and DataFrames
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17
Pandas - Indexing, Filtering, and Selection
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18
Pandas - Data Alignment, Sorting, and Ranking
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19
Pandas - Applying Functions and Handling Missing Data
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20
Import-Export: Reading Data from CSV, Excel, JSON
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21
Import-Export: Saving Cleaned Data
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22
Import-Export: Web Data: APIs, HTML, and XML
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23
Import-Export: Working with Databases (Intro to SQL with pandas)
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24
Data Wrangling - Handling Missing Values and Duplicates
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25
Data Wrangling - Data Transformation and Mapping
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26
Data Wrangling - Binning, Outlier Detection, and Dummy Variables
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27
Data Wrangling - Working with Text and Regular Expressions
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28
Data Wrangling - Combining Datasets: Merge, Join, and Concatenate
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29
Data Wrangling - Pivot Tables and Reshaping DataFrames
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30
Data Wrangling - Hierarchical Indexing and Multi-Level Aggregation
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31
Data Visualizaton - Plotting with matplotlib: Line, Bar, Scatter, Histogram
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32
Data Visualizaton - Advanced Plotting with seaborn: Heatmaps, Pairplots, and Categorical Plots
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33
Data Visualizaton - Customizing Charts: Legends, Labels, Styles
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34
Data Visualizaton - Saving and Exporting Visualizations
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35
Data Operations - Using group by for Data Aggregation
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36
Data Operations - Applying Multiple Functions
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37
Data Operations - Pivot Tables and Cross-Tabulations
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38
Data Operations - Real-World Examples: Weighted Averages, Correlations, and More
Module 1 : Statistical Intuition & Decision Logic
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39
Basic Statistical Concepts : Histograms - Preview Lesson -
40
Basic Statistical Concepts : Bar Charts vs. Pie Charts - Preview Lesson
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41
Basic Statistical Concepts : Boxplot - Preview Lesson
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42
Probability & Distributions : The Normal Distribution
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43
Probability & Distributions : Mean, Median, Mode for Normal Distribution
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44
Probability & Distributions : Standard Deviation vs Standard Error
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45
Probability & Distributions : Estimating Population Parameter
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46
Probability & Distributions : p-values: Interpretation
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47
Probability & Distributions : p-values Calculation: Part 1 (Discrete Variables)
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48
Probability & Distributions : p-values Calculation: Part 2 (Continuous Variables)
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49
Statistical Testing : Hypothesis Testing - Null Hypothesis
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50
Statistical Testing : Alternative Hypothesis
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51
Statistical Testing : ANOVA
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52
Statistics Quiz Time! – Understanding Statistics Concepts -
53
🕵️♂️ The Data Detective’s Guide: Stats Made Simple
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54
Advance Stat Concepts : Central Limit Theorem
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55
Advance Stat Concepts : Bootstrapping
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56
Advance Stat Concepts : Confidence Intervals
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57
Advance Stat Concepts : QQ Plots & Quantile Normalization
Module 2 : Supervised Learning: Linear Regression
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58
Relationship in Data : Covariance - Part 1
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59
Relationship in Data : Covariance - Part 2
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60
Relationship in Data : Pearson’s Correlation - Part 1
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61
Relationship in Data : Pearson’s Correlation - Part 2
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62
Relationship in Data : R-squared
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63
Linear Regression : Least Square Method
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64
Linear Regression : Simple Linear Regression
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65
Linear Regression : Multiple Regression
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66
Linear Regression : General Linear Models (GLM) Unlocked (t-test and ANOVA)
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67
Linear Regression Case Study : Simple Linear Regression
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68
Linear Regression Case Study : t tests
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69
Linear Regression Model : Assumptions
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70
Linear Regression Model : Model Selection using Mallow’ Cp
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71
Linear Regression Model : Backward and Forward Selection
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72
Linear Regression Model : Residual Analysis
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73
Linear Regression Model : Influential Observations Analysis
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74
Linear Regression Model : Collinearity Diagnostics
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75
Linear Regression Case Study 2 : Multiple_Linear_Regression.ipynb
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76
Interview & CV - Guidance
Module 3 : Supervised Learning: Logistic Regression
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77
Logistics Regression : Expected Values - Discrete
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78
Logistics Regression : Expected Values - Continuous
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79
Logistics Regression : Test of Association
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80
Logistics Regression : Fisher's Exact Test
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81
Logistics Regression : The Mantel-Haenszel Test (Controlling for Confounders)
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82
Logistics Regression : Logs
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83
Logistics Regression : Odds and Log(Odds)
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84
Logistics Regression : Odds Ratios and Log(Odds Ratios)
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85
Logistics Regression : Probability vs Likelihood
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86
Logistics Regression : Maximum Likelihood
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87
Logistics Regression : Maximum Likelihood - Binomial Distribution
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88
Logistics Regression : Concept
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89
Logistics Regression : Coefficients - Continuous
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90
Logistics Regression : Coefficients - Discrete
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91
Logistics Regression : Reference Cell and Effect Cell Coding
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92
Logistics Regression : R Squared and p-value
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93
Logistics Regression : Concordant and Discordant Pairs
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94
Logistics Regression : Null Deviance and Residual deviance
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95
Logistics Regression : FAIQs
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96
Case Study 3 - Logistics Regression : Real World Case Study
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97
Model Validation : Cross Validation
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98
Model Performance : Confusion Matrix
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99
Model Metrics : Sensitivity, Specificity, Precision and Accuracy
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100
Model Diagnostics : Bias and Variance
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101
Model Evaluation : ROC and AUC
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102
Information Logic : Entropy
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103
Information Logic : Mutual Information
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104
Regularization : Ridge Regression
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105
Regularization : Lasso Regression
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106
Regularization : Elastic-Net Regression
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107
Regularization : Case Study 4
Module 4 : Unsupervised Machine Learning (Clustering & Dimension Reduction)
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108
Dimension Reduction : PCA (Principal Component Analysis)
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109
Dimension Reduction : MDS (Multidimensional Scaling) and PCoA (Principal Coordinate Analysis)
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110
Dimension Reduction : t-SNE
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111
Dimension Reduction : UMAP Dimension Reduction
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112
Dimension Reduction : Heatmap
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113
Case Study 5
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114
Clustering : K-Means
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115
Clustering : DBSCAN
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116
Clustering : Hierarchical
Module 5 : Supervised Machine Learning Algorithms
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117
Gradient Descent
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118
Stochastic Gradient Descent
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119
K - Nearest Neighbors
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120
Naive Bayes
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121
Gaussian Naive Bayes
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122
Decision Tree : One-Hot, Label, Target and K-Fold Target Encoding
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123
Decision Tree : Classification Tree
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124
Decision Tree : Regression Trees
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125
Decision Tree : Pruning
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126
Case Study 6
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127
Random Forest : Build, use and validate
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128
Random Forest : Missing Data and Clustering
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129
Case Study 7
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130
Boosting : AdaBoost
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131
Boosting : Gradient Boost
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132
Boosting : XGBoost
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133
Boosting : Cosine Similarity
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134
Boosting : CatBoost
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135
Case Study 8
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136
Support Vector Machine : Understanding
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137
Support Vector Machine : The Polynomial Kernel
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138
Support Vector Machine : The Radial Kernel (RBF)
Module 6: Neural Networks & Deep Learning (Advanced AI)
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139
Neural Networks: Demystifying Complexity
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140
Neural Networks: Building Blocks of Neural Architectures
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141
Backpropagation : Foundations
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142
Backpropagation : Advanced Backpropagation Techniques
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143
Activation Functions : ReLU and Its Applications
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144
Activation Functions : SoftMax and ArgMax for Classification
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145
Feedforward Networks : Handling Multiple Inputs and Outputs
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146
Feedforward Networks : Cross-Entropy Loss and Optimization
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147
Feedforward Networks : Derivative Calculations for Loss Functions
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148
Convolutional Neural Networks: ConImage Classification with CNNs
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149
Recurrent Neural Networks (RNNs) : Fundamentals
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150
Recurrent Neural Networks (RNNs) : Long Short-Term Memory (LSTM) Networks
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151
Advanced Architectures : Encoder-Decoder Frameworks
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152
Advanced Architectures : Attention Mechanisms
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153
Transformer Networks : Architecture
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154
Transformer Networks : Decoder-Only Models (e.g., GPT-style
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155
Transformer Networks : Decoder-Only Models (e.g., GPT-style
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156
Linear Algebra Essentials : Tensor Operations in Neural Networks
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157
Linear Algebra Essentials : Matrix Algebra for Deep Learning
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158
Linear Algebra Essentials : Matrix-Based Transformer Implementation
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159
Neural Networks in Reinforcement Learning
Module 7: Deep Learning in Action (The PyTorch Lab)
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160
Start with PyTorch – Learn the basics with hands-on, beginner-friendly notebooks.
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161
Build Your First Neural Net – Code real models using PyTorch + Lightning.
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162
Handle Complex Data – Work with networks that take multiple inputs and outputs.
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163
Master CNNs – Create image-based models from scratch.
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164
Implement LSTMs – Tackle sequential data like time series and text.
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165
Create Word Embeddings – Learn how machines understand language.
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166
Explore Autoencoders – Compress and reconstruct data with unsupervised learning.
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167
Code a Transformer – Build a simplified ChatGPT-style model step by step.
Is this course suitable for complete beginners?
Absolutely! We start from scratch – no math or coding experience needed. Just bring curiosity
Can I pay in installments?
A: Yes! Choose:
• Full payment (5% discount)
• 2-month interest-free installments
• Full payment (5% discount)
• 2-month interest-free installments
How do I get my certificate?
Automatically downloadable from your student dashboard after completing all projects or will be sent to your email ID.
How flexible is the schedule?
Weekend Batch: Sat - Sun (1.5 hr/day as per your convenience)
Is this course online or offline?
This is primarily an online learning program – no need to visit any physical center! Engage, Ask, Learn - Live! However, if you're based in Vadodara and prefer in-person training, we’re happy to arrange offline batches. Just WhatsApp us at +91 92743 73121 to discuss options.
Will I get access to course material after finishing the course?
Yes! You get lifetime access to our premium digital learning vault, which includes:
🎬 AI-Powered Video Library: High-definition video lessons with crystal-clear AI narration for easy, anytime learning.
📊 Interactive Visual Sandboxes: Exclusive access to our proprietary "MasterPlaybook" tools to visualize complex math and data logic in real-time.
🐙 GitHub Repository Access: Lifetime access to our private GitHub repo containing all Python/R code templates and curated industry-grade datasets.
🚀 Project Documentation: Step-by-step guides and "Solution Notebooks" for your 10+ portfolio-ready projects.
📝 Expert Cheat Sheets: Rapid-reference digital guides for statistical formulas and core Machine Learning concepts.
🎬 AI-Powered Video Library: High-definition video lessons with crystal-clear AI narration for easy, anytime learning.
📊 Interactive Visual Sandboxes: Exclusive access to our proprietary "MasterPlaybook" tools to visualize complex math and data logic in real-time.
🐙 GitHub Repository Access: Lifetime access to our private GitHub repo containing all Python/R code templates and curated industry-grade datasets.
🚀 Project Documentation: Step-by-step guides and "Solution Notebooks" for your 10+ portfolio-ready projects.
📝 Expert Cheat Sheets: Rapid-reference digital guides for statistical formulas and core Machine Learning concepts.
How do I enroll and get access to the course?
Purchase: Create an account (or log in), complete the payment, and gain access to the course materials.
Live Sessions: After payment, our team will contact you within 24–48 hours to schedule your live training batches.
Live Sessions: After payment, our team will contact you within 24–48 hours to schedule your live training batches.
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