Mastering DeepSeekAI: Build from Scratch with Advance Techniques
Unlock the power of Deep Seek AI by building it from scratch
Updated on Jun, 2025
Language - English
Development ,Data Science,Generative AI
Lectures -67
Duration -9 hours
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Course Description
Unlock the power of Deep Seek AI by building it from scratch! This comprehensive course takes you through the fundamentals and advanced techniques of Deep Seek AI, covering deep learning architectures, transformer models, and real-world applications. Whether you're a beginner or an experienced AI practitioner, this course will equip you with hands-on coding skills, optimization strategies, and best practices for deploying Deep Seek AI solutions.
You'll gain expertise in model development, fine-tuning, and deployment, preparing you for cutting-edge AI innovation.
Goals
Video 1: Logistic Regression: From Zero to Hero
Video 2: Demystifying Logistic Regression Math
Video 3: Logistic Regression: Real-World Examples You Can't Ignore
Video 4: Data Cleaning: The Unsung Hero of ML
Video 5: Feature Engineering Magic: Transform Your Data
Video 6: Know Your Model: Essential Evaluation Metrics
Video 7: NLP for Beginners: Start with Logistic Regression
Video 8: Supercharge Your NLP with Advanced Techniques
Video 9: Transfer Learning: The NLP Shortcut You Need
Video 10: Taming COVID-19 Data: A Data Scientist's Guide
Video 11: Unmasking COVID-19 Trends: Data-Driven Insights
Video 12: The Machine Learning Lifecycle: From Data to Deployment
Video 13: Text Preprocessing: Clean Up Your Act
Video 14: Advanced Text Preprocessing: Unlock Hidden Patterns
Video 15: Telling Stories with Text Data: EDA Mastery
Video 16: Feature Engineering: The Secret to NLP Success
Video 17: Optimize Your Model: Hyperparameter Tuning Tips
Video 18: Finding the Perfect Hyperparameters: A Practical Guide
Video 19: Regularization: Prevent Overfitting Like a Pro
Video 20: Which Model Wins? A Showdown
Video 21: Linear Regression: The Building Block of ML
Video 22: Linear Regression: Simple Models, Big Impact
Video 23: Boost Your Linear Regression Game
Video 24: Decision Trees: Easy to Understand, Powerful to Use
Video 25: Decision Trees: The Building Blocks
Video 26: Mastering Entropy and Information Gain
Video 27: Avoid Overfitting: Deep Dive into Decision Trees
Video 28: Handling Categorical Data: Decision Tree Style
Video 29: Train and Conquer: Decision Tree Mastery
Video 30: Data-Driven Insights: Univariate Analysis
Video 31: Data Visualization: Tell Your Story Visually
Video 32: Spotting Trends: Outliers and Correlations
Video 33: Advanced Visualization: Uncover Hidden Insights
Video 34: Bivariate Analysis: Uncover Relationships
Video 35: Multivariate Analysis: Mastering Complexity
Video 36: Time Series Analysis: Forecasting the Future
Video 37: K-means Clustering: Find Your People
Video 38: Mastering K-means: Tips and Tricks
Video 39: K-means in Action: Real-World Examples
Video 40: Beyond K-means: Advanced Clustering Techniques
Video 41: Evaluating Your Clusters: Does It Make Sense?
Video 1: The History of Deep Learning and Inspired by Neuroscience
Video 2: Understanding Neural Networks: Weights, Multi-Neuron Networks,
Video 3: Dive Deep into Backpropagation
Video 4: Introduction to RNNs: The Intuition Behind RNNs and Different Cells
Video 5: Building RNNs with TensorFlow: Hands-on Multiple Neural Networks
Video 6: Training RNNs in TensorFlow: Model Fit, Compile, and Execute
Video 7: Optimizing Model Training: Model Training with Number of Epochs
Video 8: Sequence-to-Sequence Models: Encoder and Decoder Models
Video 9: LSTM Networks and Applications: Random Initialization and LSTM Intuition
Video 10: Implementing LSTMs with TensorFlow: Custom Implementation
Video 11: Introduction to Computer Vision: Pixel Idea and Conversion into Arrays
Video 12: Basics of Convolutional Neural Networks: Padding and Kernel
Video 13: Understanding Kernels in CNNs: Different Kernels
Video 14: Padding, Strides, and Pooling in CNNs
Video 15: Data Augmentation and Optimization in CNNs: Hands-on TensorFlow
Video 16: Building and Training CNN Models
Video 17: Implementing LSTMs with TensorFlow: Preprocessing of Data
Video 18: New! Building Generative Models with LSTMs: Train Models with Hyperparameter Tuning
Video 19: Introduction to Computer Vision with Deep Learning: Preprocessing and Training with Mini-Batch Size
Video 20: Training Deep Learning Models for Image Data: 1500 Images on Training and Test Data
Video 21: Efficiently Handling Large Image Data: Training Samples
Video 22: Advanced Image Processing Techniques: Cleaning and Preprocessing Data
Video 23: Classification with Deep Learning: 10 Classification Tasks
Video 24: Model Evaluation and Transfer Learning: Evaluating Models and Transformers
Video 25: Interpreting Deep Learning Models: Geometric Intuition of VGG16 Models
Video 26: Optimizing Deep Learning Models: Gradient Descent and Stochastic Gradient Descent
Video 27: Advanced Optimization Techniques
Video 28: Practical Deployment of Deep Learning Models: Mathematical Equations
Video 29: Deploying Models with Flask: Understanding the Internals
Video 30: Handling Requests with Keras and Flask: Keras Models and Get/Post Methods
Video 31: Scaling Deep Learning Models: Image CNN Animal in Action
Video 32: Ensuring Low Latency in Model Deployment: Getting Logs Flask Application
Prerequisites
None
Curriculum
Check out the detailed breakdown of what’s inside the course
Logistic Regression Fundamentals
3 Lectures
-
Logistic Regression: From Zero to Hero
11:00
11:00
-
Demystifying Logistic Regression Math
09:37
09:37
-
Logistic Regression: Real-World Examples You Can't Ignore
10:18
10:18
Data Preparation and Evaluation
3 Lectures
Logistic Regression for NLP
3 Lectures
Logistic Regression in Action: COVID-19 Case Study
3 Lectures
Text Preprocessing and EDA
3 Lectures
Feature Engineering for NLP
3 Lectures
Regularization and Model Comparison
3 Lectures
Linear Regression and Decision Trees
3 Lectures
Decision Tree Algorithms
3 Lectures
Decision Trees with Categorical Data
3 Lectures
Data Visualization and Analysis
3 Lectures
Advanced Data Analysis
2 Lectures
Introduction to Deep Learning Concepts
3 Lectures
Recurrent Neural Networks (RNNs)
3 Lectures
Advanced Training Techniques
3 Lectures
Convolutional Neural Networks (CNNs)
3 Lectures
Advanced CNN Techniques
3 Lectures
Implementing CNNs
3 Lectures
Deep Learning for Computer Vision
3 Lectures
Advanced Techniques in Image Processing
3 Lectures
Model Interpretation and Optimization
3 Lectures
Deployment and Maintenance of Deep Learning Models
3 Lectures
Advanced Deployment Techniques
2 Lectures
Instructor Details
AKHIL VYDYULA
Data Scientist | Data & Analytics Specialist | EntrepreneurHello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!
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