| 1 -Day 1–5 Python basics (variables, loops, functions, OOP).mp4 | 141.3 MB | ||
| 1 -Day 21–25 Data cleaning (missing values, duplicates, outliers).mp4 | 100.9 MB | ||
| 1 -Day 36–40 Intro to ML, traintest split, evaluation metrics.mp4 | 101.6 MB | ||
| 1 -Day 56–60 Case study — Predict customer churn (classification).mp4 | 104.2 MB | ||
| 1 -Day 71–75 Neural networks basics (perceptrons, forwardbackpropagation).mp4 | 101.6 MB | ||
| 1 -Day 86–88 Introduction to LLMs (GPT, BERT, transformers).mp4 | 101.5 MB | ||
| 1 -Day 96–98 Capstone project — End-to-end pipeline (data → ML model → dashboardAPI.mp4 | 82 MB | ||
| 2 -Day 26–30 Feature engineering (encoding, scaling, transformations).mp4 | 93.2 MB | ||
| 2 -Day 41–45 Regression models (Linear, Logistic, Ridge, Lasso).mp4 | 115.7 MB | ||
| 2 -Day 61–65 Case study — Sales forecasting (time-series).mp4 | 111.2 MB | ||
| 2 -Day 6–10 Data handling with NumPy & Pandas.mp4 | 102.6 MB | ||
| 2 -Day 76–80 Deep learning with TensorFlowKeras.mp4 | 101.6 MB | ||
| 2 -Day 89–91 Prompt engineering & fine-tuning basics.mp4 | 84.3 MB | ||
| 2 -Day 99 Portfolio & resume building for Data ScienceAI roles.mp4 | 67.7 MB | ||
| 3 -Day 100 Presentation + “Next Steps” (MLOps, advanced DL, specialized domains).mp4 | 77.5 MB | ||
| 3 -Day 11–15 Data visualization (Matplotlib, Seaborn).mp4 | 112 MB | ||
| 3 -Day 31–35 Exploratory Data Analysis (EDA) with case studies.mp4 | 89.4 MB | ||
| 3 -Day 46–50 Classification models (Decision Trees, Random Forest, SVM, KNN).mp4 | 103.4 MB | ||
| 3 -Day 66–70 Case study — Recommendation systems (collaborative filtering).mp4 | 84.6 MB | ||
| 3 -Day 81–85 Applications — Image classification (CNN), text processing (RNNLSTM).mp4 | 108.7 MB | ||
| 3 -Day 92–93 AI for business (automation, NLP, chatbots).mp4 | 98.8 MB | ||
| 4 - Hands on Lab 2.html | 102.4 B | ||
| 4 - Hands on Lab 4.html | 102.4 B | ||
| 4 - Hands on Lab 5.html | 204.8 B | ||
| 4 - Hands on Lab 7.html | 102.4 B | ||
| 4 -Day 16–20 Statistics & probability (mean, variance, distributions, hypothesis).mp4 | 115.6 MB | ||
| 4 -Day 51–55 Unsupervised learning (K-Means, PCA, clustering use cases).mp4 | 103.9 MB | ||
| 4 -Day 94–95 AI in industries (aviation, healthcare, finance).mp4 | 86.2 MB | ||
| 4 -Hands on lab 2.pdf | 109 KB | ||
| 4 -Hands on lab 4.pdf | 78.9 KB | ||
| 4 -Hands on lab 5.pdf | 88.4 KB | ||
| 4 -Hands on lab 7.pdf | 146.6 KB | ||
| 5 - Hands on Lab 1.html | 102.4 B | ||
| 5 - Hands on Lab 3.html | 102.4 B | ||
| 5 - Hands on Lab 6.html | 204.8 B | ||
| 5 -Hands on lab 1.pdf | 106 KB | ||
| 5 -Hands on lab 3.pdf | 90.9 KB | ||
| 5 -Hands on lab 6.pdf | 85.8 KB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 40 total files | |||
Data Science & AI Mastery: 100 Days to Career Success
https://WebToolTip.com
Published 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 18m | Size: 2.33 GB
Master Data Science & AI in 100 Days with Hands-On Projects, Real Case Studies, and Career-Ready Skills
What you'll learn
Master Python programming, statistics, and data handling as the foundation for Data Science & AI
Perform data cleaning, feature engineering, and exploratory data analysis (EDA) with real-world datasets
Build and evaluate machine learning models for regression, classification, clustering, and forecasting
Apply deep learning with Neural Networks, CNNs, RNNs, and LSTMs using TensorFlow/Keras.
Work with Large Language Models (LLMs), practice prompt engineering, and explore generative AI use cases
Solve industry-level case studies such as churn prediction, sales forecasting, and recommendation systems.
Develop an end-to-end capstone project with data pipeline, model, dashboard, and business insights
Build a portfolio and resume that showcase your skills and prepare you for career opportunities in Data Science & AI.
Requirements
No prior experience in Data Science or AI is required — the course starts from the basics
A basic understanding of high-school level math (algebra, probability, and statistics) will be helpful
Familiarity with Python programming is a plus, but not mandatory — core concepts are covered early on
A computer with internet access and the ability to install software such as Python, Jupyter Notebook, and required libraries.
Most importantly: a growth mindset, curiosity, and commitment to completing the 100 days of structured learning.
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