Data Science Mastery - Complete Data Science Bootcamp 2025
Categories:Data EngineeringData Science & AI
$49.99$9.99Save 80%
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Certificate of completion
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What Will We CoverFree Preview
1:58
Module 1 Data Collection The Foundation of Data ScienceFree Preview
6:51
Mod 2 Data Cleaning and Preprocessing Turning Raw Data into Usable InsightsFree Preview
5:09
Module 3 Data Exploration and Analysis EDAFree Preview
5:10
Module 4 Feature Engineering Transforming Data into InsightsFree Preview
5:14
Module 5 Data Visualization Communicating Insights Effectively
5:29
Module 6 Machine Learning and Modeling Building Intelligent Systems
6:52
Module 7 Model Evaluation and Validation Ensuring Reliable Predictions
6:25
Module 8 Model Deployment Bringing Machine Learning Models to Life
5:22
Module 9 Big Data Technologies Managing and Analyzing Massive Datasets
6:05
Module 10 Data Ethics and Governance Responsible AI and Data Practices
7:32
Module 11 Business Understanding and Domain Expertise
6:54
Mod 12 Communication and Storytelling Turning Data into Impactful Narratives
7:22
Whats Next Bootcamp Deep Dive
00:48
Introduction to Week 1 Python Programming Basics
00:39
Day 1 Introduction to Python and Development Setup
20:37
Day 2 Control Flow in Python
32:46
Day 3 Functions and Modules
23:22
Day 4 Data Structures Lists Tuples Dictionaries Sets
30:33
Day 5 Working with Strings
23:53
Day 6 File Handling
22:48
Day 7 Pythonic Code and Project Work
39:28
Introduction to Week 2 Data Science Essentials
00:44
Day 1 Introduction to NumPy for Numerical Computing
22:49
Day 2 Advanced NumPy Operations
21:33
Day 3 Introduction to Pandas for Data Manipulation
19:44
Day 4 Data Cleaning and Preparation with Pandas
24:28
Day 5 Data Aggregation and Grouping in Pandas
15:09
Day 6 Data Visualization with Matplotlib and Seaborn
27:01
Day 7 Exploratory Data Analysis EDA Project
23:08
Introduction to Week 3 Mathematics for Machine Learning
00:42
Day 1 Linear Algebra Fundamentals
21:23
Day 2 Advanced Linear Algebra Concepts
19:43
Day 3 Calculus for Machine Learning Derivatives
18:10
Day 4 Calculus for Machine Learning Integrals and Optimization
16:29
Day 5 Probability Theory and Distributions
25:07
Day 6 Statistics Fundamentals
19:08
Day 7 MathDriven Mini Project Linear Regression from Scratch
15:03
Introduction to Week 4 Probability and Statistics for Machine Learning
00:45
Day 1 Probability Theory and Random Variables
18:34
Day 2 Probability Distributions in Machine Learning
17:10
Day 3 Statistical Inference Estimation and Confidence Intervals
15:40
Day 4 Hypothesis Testing and PValues
11:44
Day 5 Types of Hypothesis Tests
18:41
Day 6 Correlation and Regression Analysis
17:28
Day 7 Statistical Analysis Project Analyzing RealWorld Data
24:52
Introduction to Week 5 Introduction to Machine Learning
00:46
Day 1 Machine Learning Basics and Terminology
15:35
Day 2 Introduction to Supervised Learning and Regression Models
15:48
Day 3 Advanced Regression Models Polynomial Regression and Regularization
35:19
Day 4 Introduction to Classification and Logistic Regression
24:19
Day 5 Model Evaluation and CrossValidation
16:00
Day 6 kNearest Neighbors kNN Algorithm
17:22
Day 7 Supervised Learning Mini Project
25:10
Introduction to Week 6 Feature Engineering and Model Evaluation
00:42
Day 1 Introduction to Feature Engineering
14:30
Day 2 Data Scaling and Normalization
16:21
Day 3 Encoding Categorical Variables
16:48
Day 4 Feature Selection Techniques
16:22
Day 5 Creating and Transforming Features
18:09
Day 6 Model Evaluation Techniques
15:49
Day 7 CrossValidation and Hyperparameter Tuning
20:03
Introduction to Week 7 Advanced Machine Learning Algorithms
00:40
Day 1 Introduction to Ensemble Learning
15:04
Day 2 Bagging and Random Forests
14:22
Day 3 Boosting and Gradient Boosting
16:07
Day 4 Introduction to XGBoost
19:39
Day 5 LightGBM and CatBoost
20:21
Day 6 Handling Imbalanced Data
16:42
Day 7 Ensemble Learning Project Comparing Models on a Real Dataset
22:36
Introduction to Week 8 Model Tuning and Optimization
00:52
Day 1 Introduction to Hyperparameter Tuning
13:46
Day 2 Grid Search and Random Search
16:09
Day 3 Advanced Hyperparameter Tuning with Bayesian Optimization
26:57
Day 4 Regularization Techniques for Model Optimization
13:17
Day 5 CrossValidation and Model Evaluation Techniques
13:00
Day 6 Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
19:28
Day 7 Optimization Project Building and Tuning a Final Model
22:45
Introduction to Week 9 Neural Networks and Deep Learning Fundamentals
00:47
Day 1 Introduction to Deep Learning and Neural Networks
16:13
Day 2 Forward Propagation and Activation Functions
14:43
Day 3 Loss Functions and Backpropagation
15:31
Day 4 Gradient Descent and Optimization Techniques
21:50
Day 5 Building Neural Networks with TensorFlow and Keras
19:20
Day 6 Building Neural Networks with PyTorch
26:28
Day 7 Neural Network Project Image Classification on CIFAR10
22:09
Introduction to Week 10 Convolutional Neural Networks CNNs
00:48
Day 1 Introduction to Convolutional Neural Networks
26:16
Day 2 Convolutional Layers and Filters
23:48
Day 3 Pooling Layers and Dimensionality Reduction
23:58
Day 4 Building CNN Architectures with Keras and TensorFlow
17:46
Day 5 Building CNN Architectures with PyTorch
22:26
Day 6 Regularization and Data Augmentation for CNNs
18:40
Day 7 CNN Project Image Classification on Fashion MNIST or CIFAR10
27:34
Introduction to Week 11 Recurrent Neural Networks RNNs and Sequence Modeling
00:49
Day 1 Introduction to Sequence Modeling and RNNs
33:32
Day 2 Understanding RNN Architecture and Backpropagation Through Time BPTT
24:31
Day 3 Long ShortTerm Memory LSTM Networks
15:03
Day 4 Gated Recurrent Units GRUs
7:07
Day 5 Text Preprocessing and Word Embeddings for RNNs
24:02
Day 6 SequencetoSequence Models and Applications
43:09
Day 7 RNN Project Text Generation or Sentiment Analysis
17:55
Introduction to Week 12 Transformers and Attention Mechanisms
00:48
Day 1 Introduction to Attention Mechanisms
15:17
Day 2 Introduction to Transformers Architecture
18:19
Day 3 SelfAttention and MultiHead Attention in Transformers
21:00
Day 4 Positional Encoding and FeedForward Networks
20:22
Day 5 HandsOn with PreTrained Transformers BERT and GPT
19:37
Day 6 Advanced Transformers BERT Variants and GPT3
20:38
Day 7 Transformer Project Text Summarization or Translation
18:33
Introduction to Week 13 Transfer Learning and FineTuning
00:45
Day 1 Introduction to Transfer Learning
14:52
Day 2 Transfer Learning in Computer Vision
26:26
Day 3 FineTuning Techniques in Computer Vision
21:46
Day 4 Transfer Learning in NLP
17:00
Day 5 FineTuning Techniques in NLP
26:04
Day 6 Domain Adaptation and Transfer Learning Challenges
14:52
Day 7 Transfer Learning Project FineTuning for a Custom Task
18:22
Introduction to Machine Learning Algorithms
3:43
1 Linear Regression Implementation in Python
6:23
2 Ridge and Lasso Regression Implementation in Python
7:49
3 Polynomial Regression Implementation in Python
7:17
4 Logistic Regression Implementation in Python
5:55
5 KNearest Neighbors KNN Implementation in Python
6:11
6 Support Vector Machines SVM Implementation in Python
6:26
7 Decision Trees Implementation in Python
6:16
8 Random Forests Implementation in Python
5:57
9 Gradient Boosting Implementation in Python
6:11
10 Naive Bayes Implementation in Python
5:51
11 KMeans Clustering Implementation in Python
4:22
12 Hierarchical Clustering Implementation in Python
5:16
13 DBSCAN DensityBased Spatial Clustering of Applications w Noise
4:59
14 Gaussian Mixture ModelsGMM Implementation in Python
4:54
15 Principal Component Analysis PCA Implementation in Python
4:43
16 tDistributed Stochastic Neighbor Embedding tSNE Implementation in Python
5:14
17 Autoencoders Implementation in Python
8:22
18 SelfTraining Implementation in Python
6:41
19 QLearning Implementation in Python
8:36
20 Deep QNetworks DQN Implementation in Python
13:48
21 Policy Gradient Methods Implementation in Python
10:08
22 OneClass SVM Implementation in Python
4:44
23 Isolation Forest Implementation in Python
5:06
24 Convolutional Neural Networks CNNs Implementation in Python
8:27
25 Recurrent Neural Networks RNNs Implementation in Python
7:37
26 Long ShortTerm Memory LSTM Implementation in Python
7:16
27 Transformers Implementation in Python
11:05
What is Machine Learning in the context of TensorFlow
10:59
Introduction to TensorFlow
7:53
TensorFlow vs Other Machine Learning frameworks
15:11
Installing TensorFlow
11:46
Setting up your Development Environment
9:40
Verifying the Installation
13:34
Introduction to Tensors
2:18
Tensor Operations
4:28
Constants Variables and Placeholders
3:41
TensorFlow Computational Graph
4:28
Creating and Running a TensorFlow Session
3:09
Managing Graphs and Sessions
4:37
Building a Simple Feedforward Neural Network
5:31
Activation Functions
4:31
Loss Functions and Optimizers
6:20
Introduction to Keras API
5:17
Building Complex Models with Keras
4:47
Training and Evaluating Models
5:20
Introduction to CNNs
5:00
Building and Training CNNs with TensorFlow
3:32
Transfer Learning with Pretrained CNNs
5:26
Introduction to RNNs
5:17
Building and Training RNNs with TensorFlow
3:29
Applications of RNNs Language Modeling Time Series Prediction
3:38
Saving and Loading Models
4:32
TensorFlow Serving for Model Deployment
4:29
TensorFlow Lite for Mobile and Embedded Devices
5:28
Introduction to Distributed Computing with TensorFlow
5:38
TensorFlows Distributed Execution Framework
5:34
Scaling TensorFlow with TensorFlow Serving and Kubernetes
5:52
Introduction to TFX
6:08
Building EndtoEnd ML Pipelines with TFX
4:13
Model Validation Transform and Serving with TFX
5:32
Image Classification
5:56
Natural Language Processing
5:37
Recommender Systems
5:53
Object Detection
5:07
Building a Sentiment Analysis Model
6:03
Creating an Image Recognition System
5:03
Developing a Time Series Prediction Model
4:04
Implementing a Chatbot
5:54
Generative Adversarial Networks GANs
5:06
Reinforcement Learning with TensorFlow
5:41
Quantum Machine Learning with TensorFlow Quantum
5:27
TensorFlow Documentation and Tutorials
4:37
Online Courses and Books
3:07
TensorFlow Community and Forums
4:16
Summary of Key Concepts
5:21
Next Steps in Your TensorFlow Journey
4:26
What will we cover
1:23
Introduction to PyTorch
8:57
Getting Started with PyTorch
8:22
Working with Tensors
10:28
Autograd and Dynamic Computation Graphs
7:29
Building Simple Neural Networks
10:15
Loading and Preprocessing Data
9:39
Model Evaluation and Validation
11:16
Advanced Neural Network Architectures
11:11
Transfer Learning and FineTuning
8:11
Handling Complex Data
8:32
Model Deployment and Production
9:14
Debugging and Troubleshooting
10:15
Distributed Training and Performance Optimization
9:50
Custom Layers and Loss Functions
10:02
Researchoriented Techniques
10:22
Integration with Other Libraries
9:06
Contributing to PyTorch and Community Engagement
8:17
Basic Calculator using Python
13:14
Image Classifier using Keras and TensorFlow
22:32
Simple Chatbot using predefined responses
8:21
Spam Email Detector using Scikitlearn
16:17
Handwritten Digit Recognition with MNIST dataset
11:52
Sentiment Analysis on text data using NLTK
17:54
Movie Recommendation System using cosine similarity
13:32
Predict House Prices with Linear Regression
16:25
Weather Forecasting using historical data
11:28
Basic Neural Network from scratch
21:19
Stock Price Prediction using historical data w simple Linear Regression
13:01
Predict Diabetes using logistic regression
11:35
Dog vs Cat Classifier with CNN
14:30
TicTacToe AI using Minimax Algorithm
10:17
Credit Card Fraud Detection using Scikitlearn
7:20
Iris Flower Classification using decision trees
6:42
Simple Personal Assistant using Python speech libraries
10:09
Text Summarizer using Gensim
6:41
Fake Product Review Detection using NLP techniques
6:48
Detect Emotion in Text using Natural Language Toolkit NLTK
6:28
Book Recommendation System using collaborative filtering
7:06
Predict Car Prices using Random Forest
7:12
Identify Fake News using Naive Bayes
7:52
Create a Resume Scanner using keyword extraction
7:31
Customer Churn Prediction using classification algorithms
7:51
Named Entity Recognition NER using spaCy
8:09
Predict Employee Attrition using XGBoost
8:07
Disease Prediction eg Heart Disease using ML algorithms
12:25
Movie Rating Prediction using Collaborative Filtering
9:18
Automatic Essay Grading using BERT
16:57
$49.99$9.99Save 80%
What's included
Certificate of completion
Create an account to start learning