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Course Curriculum
| Python Basics | |||
| Submit all your assignments through github link | |||
| Introduction to Statistics | 00:00:00 | ||
| Jupyter Notebook and Basic Syntax of Python | 00:00:00 | ||
| Conditionals and Loops | 00:00:00 | ||
| Lists and Tuples | 00:00:00 | ||
| Strings in Python | 00:00:00 | ||
| Dictionary in Python | 00:00:00 | ||
| MINI PROJECT: ROCK-PAPER-SCISSORS GAME | 00:00:00 | ||
| Functions in Python Part-1 | 00:00:00 | ||
| Functions in Python Part-2 | 00:00:00 | ||
| MINI PROJECT:SUDOKU SOLVER | 00:00:00 | ||
| Object Oriented Programming | |||
| OOPs Intro,Class and Objects | 00:00:00 | ||
| Access Modifiers and Inheritance | 00:00:00 | ||
| Method overriding,Encapsulation,Polymorphism and imports | 00:00:00 | ||
| Files and Exception Handling | |||
| Files and Exception Handling | 00:00:00 | ||
| Python Full Project | |||
| Chess with GUI part-1 | 00:00:00 | ||
| Chess with GUI part-2 | 00:00:00 | ||
| Chess with GUI part-3 | 00:00:00 | ||
| Chess with GUI part-4 | 00:00:00 | ||
| Data Analysis | |||
| File reading in Pandas | 00:00:00 | ||
| File writing in Pandas | 00:00:00 | ||
| Getting into data manipulation Part-1 | 00:00:00 | ||
| Getting into data manipulation Part-2 | 00:00:00 | ||
| Getting into data manipulation Part���3 | 00:00:00 | ||
| Getting into data manipulation Part-4 | 00:00:00 | ||
| Advanced data manipulation Part-1 | 00:00:00 | ||
| Advanced data manipulation Part-2 | 00:00:00 | ||
| Numpy | |||
| Numpy array manipulation Part-1 | 00:00:00 | ||
| Numpy array manipulation Part-2 | 00:00:00 | ||
| Numpy array manipulation Part-3 | 00:00:00 | ||
| Numpy array manipulation Part-4 | 00:00:00 | ||
| Data Visualization | |||
| Data Visualization with pandas Part-1 | 00:00:00 | ||
| Data Visualization with pandas Part-2 | 00:00:00 | ||
| Plotly and Cufflinks part-1 | 00:00:00 | ||
| Plotly and Cufflinks part-2 | 00:00:00 | ||
| Visualization with Seaborn Part-1 | 00:00:00 | ||
| Visualization with Seaborn Part-2 | 00:00:00 | ||
| DATABASE | |||
| MySQL | 00:00:00 | ||
| SQLite | 00:00:00 | ||
| MongoDB | 00:00:00 | ||
| API and Framework | |||
| API AND EXECUTION FLOW | 00:00:00 | ||
| UNDERSTANDING API BY CREATING CALCULATOR | 00:00:00 | ||
| EDA AND DATA PREPROCESSING | |||
| SPORTS | |||
| Virat Kohli ODI Career Analysis | 00:00:00 | ||
| Virat Kohli Test Career Analysis | 00:00:00 | ||
| CRISTIANO RONALDO GOALS DATA | 00:00:00 | ||
| FIFA DATA EXPLORATION | 00:00:00 | ||
| When Would Virat Ovetake Sachin in ODI | 00:00:00 | ||
| MESSI VS RONALDO LEAGUE PERFORMANCE | 00:00:00 | ||
| MOVIES AND SERIES | |||
| MoneyHeist Data Exploration | 00:00:00 | ||
| Breaking Bad EDA and a Glimpse of NLP | 00:00:00 | ||
| Disney Movies | 00:00:00 | ||
| IMDB 1000 MOVIES | 00:00:00 | ||
| Netflix Shows | 00:00:00 | ||
| Banking and Finance | |||
| Finance Courses in Udemy | 00:00:00 | ||
| IBM PAYMENT HISTORY | 00:00:00 | ||
| State aggregate expenditure and personal finance data | 00:00:00 | ||
| Loan Approval Data | 00:00:00 | ||
| Statistics With Python | |||
| Introduction to Statistics | 00:00:00 | ||
| Population and Sampling Techniques | 00:00:00 | ||
| Variables and Measurements | 00:00:00 | ||
| Measures of Central Tendency and Dispersion | 00:00:00 | ||
| 5 Number Summary | 00:00:00 | ||
| Gaussian(Normal) Distribution and Z score | 00:00:00 | ||
| Log-Normal,Bernauli,Binomial and More Distributions | 00:00:00 | ||
| Standardization and Normalization | 00:00:00 | ||
| Probability | 00:00:00 | ||
| Permutations and Combinations | 00:00:00 | ||
| Hypothesis Testing | 00:00:00 | ||
| Z-test,t-test and Chi-Square test | 00:00:00 | ||
| Covariance ,Pearson���s Correlation and Spearman���s Correlation | 00:00:00 | ||
| MACHINE LEARNING | |||
| Section 1 | |||
| Introduction to Machine Learning | 00:00:00 | ||
| END TO END ML PROJECT | |||
| PROJECT_INTRO | 00:00:00 | ||
| Sampling and Data Exploration | 00:00:00 | ||
| Data Preperation and Pipeline Setting | 00:00:00 | ||
| Model Building ,Evaluation and Presentation | 00:00:00 | ||
| Machine Learning Algorithms | |||
| Linear Regression | 00:00:00 | ||
| R square and Adjusted R Square | 00:00:00 | ||
| Linear Regression and R2_Score Practical | 00:00:00 | ||
| Ridge and Lasso Regression | 00:00:00 | ||
| Ridge and Lasso Regresson Practicals | 00:00:00 | ||
| Logistic Regression | 00:00:00 | ||
| Confusion Matrix,Precision,Recall and F-Score | 00:00:00 | ||
| Logistic Regression,Confusion Matrix practicals | 00:00:00 | ||
| Naive Bayes Indepth Intuition | 00:00:00 | ||
| ROC AND AUC | 00:00:00 | ||
| Naive Bayes ,ROC and AUC Practicals | 00:00:00 | ||
| K Means Clustering with Practical | 00:00:00 | ||
| MACHINE LEARNING FULL PROJECTS | |||
| 1. Salary Prediction(Using Flask and Django) | 00:00:00 | ||
| Using Flask Framework | 00:00:00 | ||
| Using Django Framework | 00:00:00 | ||
| 2. Admission Prediction | 00:00:00 | ||
| 3.Drug Classification | 00:00:00 | ||
| 4.Diabetes Prediction | 00:00:00 | ||
| 9.Bank Note Authenticity Prediction | 00:00:00 | ||
| Analysing More Data | |||
| Netflix_Shows | 00:00:00 | ||
| Breast Cancer | 00:00:00 | ||
| EDA_TOOLS_AND_LIBRARIES | 00:00:00 | ||
| UBER_EATS | 00:00:00 | ||