| title | date | author |
|---|---|---|
120-days of job Ready Data Science Course |
July 24, 2023 |
Dr. Aammar Tufail |
This course is a paid course, if you want to register and join our zoom meeting and online live classes you can register here
- 120-days of job Ready Data Science Course
- Table of Content
- 120-days of job Ready Data Science Course
- Introduction to the course
- Resources
- Lecture No. 0: Pre-requisite of this course:
- Lecture No. 1: Course plan | Data is Everywhere:
- Lecture No. 2: Introduction to Data Science and importance of Statistics:
- Lecture No. 3: Statistics to Data Science:
- Lecture No. 4: Stat ka zero se start (Complete Workshop):
- Lecture No. 5: Introduction to Python and Data Science Revision:
- Lecture No. 6: Statistics for Data Science (Scales of Measurements and details about Data Types):
- Lecture No. 7: Descriptive vs. Inferential Statistics (Choosing a right statistical Method):
- Lecture No. 8: Descriptive and Inferential Statistics:
- Lecture No. 9: Statistics:
- Lecture No. 10: Statistics:
- Lecture No. 11: Data Analytics Skills and Job plans:
- Lecture No. 12: (Revise the Lecture 11) Data Analytics Skills and Job plans:
- Lecture No. 13: Spreadsheets in Data Science
- Lecture No. 14: Spreadsheets in Data Science
- Lecture No. 15: Excel Statistics and SQL
- Lecture No. 16: SQL
- Lecture No. 17: SQL intermediate
- Lecture No. 18: SQL intermediate
- Lecture No. 19: SQL intermediate
- Lecture No. 20: SQL Practice
- Lecture No. 21: R and R studio
- Lecture No. 22: R and R studio
- Lecture No. 23: R and R studio
- Lecture 23a: Installation of Software and Introduction to R
- Lecture 23b: First Line of Code in R and learning to work with R
- Lecture 23c: Data Visualization and plotting in R using ggplot2
- Lecture 23d: Data Visualization and Data Transformation in R using tidyverse packages
- Publication Ready Graphs in R
- Lecture No. 24: R and R studio
- Lecture 24a: Data Transformation in R using tidyverse packages
- Lecture 24b: Statistics in R
- Lecture 24c: Choosing a right Statistical Method for Data Analysis in R
- Lecture 24d: t-test (all Types), ANOVA, One-way ANOVA, Two-way ANOVA, lettering and plotting in R
- Lecture 24e: Publication Ready Plots in R
- Lecture 25: EDA in R
- Lecture 26: EDA in R
- Lecture 27: EDA in R a project
- Lecture 28: EDA in R a project
- Lecture 29: EDA in R a project
- Lecture 30: EDA in R a project
- Lecture 31: EDA in R a project
- Lecture 32: EDA in R a project (Data Science Night)
- Lecture 33: Practice Python for Data Science pre-lecture given at the start of this course
- Lecture 34: Start of Python for Data Science
- Lecture 35: Python for Data Science Markdown Language in 72 minutes
- Lecture 36: Python for Data Science basics
- Lecture 37: Data Night in Ramzan
- Lecture 38: Doing EDA in Python
- Lecture 39: EDA in Python on Big Dataset (1/4)
- Lecture 40: EDA in Python on Big Dataset (2/4)
- Lecture 41: EDA in Python on Big Dataset (3/4)
- Lecture 42: EDA in Python on Big Dataset (4/4)
- Lecture 43: EDA in Python (Practice Session)
- Lecture 44: EDA in Python (Practice Session)
- Machine Learning
- Lecture 01: Introduction to Machine learning
- Lecture 02: Regression vs. Classification
- Lecture 03: Multilinear and Polynomial Regression
- Lecture 04: Logistic Regression Theory
- Lecture 05: Logistic Regression in Python
- Lecture 06: Classification Metrics to evaluate models
- Lecture 07: Support VEctor Machines (SVMs)
- Lecture 08: Naive Bayes
- Lecture 09: Cross Validation Methdos in Machine Learning
- Lecture 10: K nearest Neighbours (KNN)
- Lecture 11: Mathematics behind K-nearest Neighbours (KNN)
- Lecture 12: Missing or Null values Imputation
- Lecture 13: Pipeline and Hyperparameter Tuning in ML
- Bonous Lecture: Data Science Portfolio with ChatGPT
- Lecture 14: Decision Tree Algorithms
- Lecture 15: Decision Tree Algorithm practice in Python with sk-learn
- Lecture 16: ADA-booost
- Lecture 17: Random Forest
- Lecture 18: XGBoost, CATBoost, lightGBM
- Lecture 19: Regularization Lasso (L1) and Ridge (L2) | A beginner guide
- Lecture 20: Ensemble Algorithms in python
- Lecture 21: Selecting a Best Model with Hyperparameter tuning
- Lecture 22: Unsupervised Machine Learning Algorithms
- Lecture 23: Clustering Algorithms
- Lecture 24: K-means Clustering
- Lecture 25: kmeans vs. kmeans++ Clustering
- Lecture 26: Hierarchical Clustering Theory
- Lecture 27: Hierarchical Clustering Practice in Python
- Lecture 28: DBSCAN (Density-based spatial clustering of applications with noise)
- Lecture 29: DBSCAN vs. OPTICS
- Lecture 30: Gaussian Mixture Models (GMM)
- Lecture 31: Evaluation metrics for GMM
- Lecture 32: Feature Engineering
- Lecture 33: Feature Selection
- Lecture 34: Principal Component Analysis (PCA)
- Lecture 35: SVD (Singular Value Decomposition)
- Lecture 36: tSNE (t-distributed Stochastic Neighbor Embedding)
- Google Colab use, tips and tricks
- Miconda and Conda Environments for Effiecint use of ML models
- Deep Learning
-
- Lecture 01: Introduction to Deep Learning (DL-101)
- Lecture 02: What is Neural Network? and How we can construct it
- Lecture 03: Number of neurons in Each Layer
- Lecture 04: Computer Vision (Basics)
- Lecture 05: Deep Learning and Computer Vision
- Lecture 06: Activation Functions
- Lecture 07: Activation Functions
- Lecture 08: Activation Functions
- Lecture 09: How to choose an activation functions
- Lecture 10: Regression with Artificial Neural Network (ANN)
- Lecture 11: Test, training, validation loss in Artificial Neural Network (ANN)
- Bonus Lecture: gitHUB copilotX
- Bonus Lecture: How to find big Datasets for you Data science, Machine Learning or Deep Learning portfolio?
- Lecture 12: Convolutional Neural Network (CNN) Theory
- Lecture 13: Convolutional Neural Network (CNN) Practice in Python | Tensorflow framework
- Lecture 14: Master Convolutional Neural Network (CNN)Concepts to make projects
- Lecture 15: Recurrent Neural Network (RNN) Theory
- Lecture 16: Recurrent Neural Network (RNN) Practice in Python | Tensorflow framework
- Lecture 17: NLP with RNN
- Lecture 18: Key Terms to be used in NLP
- Lecture 19: Sentiment Anlysis using RNN
- Lecture 20: LSTM (Long Short Term Memory) Theory for NLP in Python
- Lecture 21: Fastest way to make notebooks for your portfolio using gitHub copilotX
- Lecture 22: Key DIfferences: ANN vs. CNN vs. RNN vs. LSTM vs. GRU
- Lecture 23: LSTM vs. GRU
- Bonus Lecture: Job Search and Interview Preparation on LinkedIn using chatGPT
- Bonus Lecture: New AI courses on Google by Goolge
-
- Time Series
-
- Lecture 01: Introduction to Time Series
- Lecture 02: Types of Data Analysis in Time Series
- Lecture 03: Data Types in Time Series
- Lecture 04: Time Series Data Analysis in Python
- Lecture 05: Time Series Project Formation
- Lecture 06: Weather Forecasting Project (Part-1)
- Lecture 07: Weather Forecasting Project (Part-2)
- Lecture 08: Weather Forecasting Project (Part-3)
- Lecture 09: Facebook Model - prophet Model (Part-1)
- Lecture 10: Facebook Model - prophet Model (Part-2)
- Lecture 11: Webscrapping vs. Webcrawling
- Lecture 12: Webscrapping with Python on wikipedia
- Lecture 13: Stock Market Data Scrapping in Python
- Lecture 14: ARIMA model in Time Series-Introduction (Part-1)
- Lecture 15: ARIMA model python (Part-2)
- Lecture 16: ARIMA model Explained Further (Part-3)
- Lecture 17: Stock Market Forecasting
- Lecture 18: Webapplication development using Streamlit
- Lecture 19: Stock Market Data Forecast - A complete Project
-
- openCV and pyTorch
- Lecture 01: Introduction to openCV
- Lecture 02: Edge Detection in openCV with Python
- Lecture 03: Face Detection in openCV with Python
- Lecture 04: streamlit webapp using openCV-python- A complete project
- Lecture 05: Computer Vision Learning Resources
- Lecture 06: Introduction to pyTorch
- Lecture 07: pyTorch Binary Class Classification using ANN
- Lecture 08: pyTorch Multi Class Classification using ANN
- Lecture 09: pyTorch and DeepLabv3
- langChain and API integration and Generative Models
- Lecture 01: Reinforcement Learning for Langchain and Robotics
- Lecture 02: langChain A-Z explained
- Lecture 03: langChain app in 14 lines of code
- Lecture 04: API integration to make NLP apps
- Lecture 05: chatGPT code interpreter
- Lecture 06: chatPDF application - A complete project
- Lecture 07: COVID-19 Data Science project Idea - A complete project
- Lecture 08: Generative Models in Python (Stable Diffusion for AI art in Tensorflow
- Ai News Updates
- YourFeedback and Certificate of Completion:
- Join our Facebook Group for more updates:
- Information about the instructor:
This course is a paid course, if you want to register and join our zoom meeting and online live classes you can register here