Prime : Complete AI/ML Job Preparation!

Course Content

19. Data Visualization (Part II)

0.TOPIC LIST.jpg

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1-matplotlib_tutorial.ipynb

00:00
1. Histograms

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10. Introduction to Seaborn

00:00
11. Creating Plots with Seaborn

00:00
12. Relational Plots in Seaborn

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13. Categorical Plots in Seaborn

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14. Distribution Plots in Seaborn

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15. Relational Plots in Seaborn – Heatmaps

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16. Best Practices for Data Visualization

00:00
2. Multiple datasets on Histogram

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3. axvline in Histogram

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4. Box Plots

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5. Operations on Box Plot

00:00
6. Stack Plots

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7. Subplots

00:00
8. Modern Matplotlib

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9. Practice Task (Weekly Temperature)

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DATA VISUALISATION – SEABORN .pdf

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Data Visualisation Assignment.pdf

00:00
DATA VISUALISATION MATPLOTLIB .pdf

00:00
seaborn_tutorial.ipynb

00:00

20. Maths for AI ( probability )

3 Probability Examples

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4 Solving Practice Problems

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5 Types of Events

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6 Complementary Rule

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7 Addition Rule

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8 Multiplication Rule

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9 Solving Practice Problems

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probab_dis_codet.ipynb

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1 Math for AI

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10 Conditional Probability

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11 Law of Total Probability

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12 Bayes Theorem

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13 Solving Practice Problem

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14 Random Variables

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15 Mean, Median & Mode

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16 Variance & Standard Deviation

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17 Probability Distributions & its Types

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18 Binomial Distribution

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19 Uniform Distribution

00:00
2 Understanding Probability

00:00
20 Normal Distribution

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21. Math For AI (Linear Algebra)

1 Introduction to Linear Algebra

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10 Dot Product

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11 Cross Product

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12 Matrix

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13 Operations on Matrices

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14 Determinants

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15 Eigen Vectors & Eigen Values

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2 Straight Lines

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3 Distance between 2 Points

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4 Parallel & Perpendicular Lines

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5 Distance between Parallel Lines

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6 Practice Problem TEAMWORK

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7 Vectors

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8 Vector Addition

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9 Scalar Multiplication

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22. Math For AI (Calculas)

1 Calculus Introduction

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2 Functions

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3 Composite Functions

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4 Functions Scalar Multiplication & Addition

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5 Input Scalar Multiplication & Addition

00:00
6 Differentiation

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7 Differentiation Rules

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8 Finding Minima Maxima

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9 Practice Problem

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calculus_code.ipynb

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23. Machine Learning Part I

0.TOPIC LIST.jpg

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1-linear_regression.ipynb

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1. Introduction to ML

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10. Understanding Cost Function Curve

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11. What is Gradient Descent

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12. Summary

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13. Linear Regression (Code)

00:00
14. Evaluation Metrics

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2. Types of ML (Supervised)

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3. Types of ML (Unsupervised & RL)

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4. Supervised Machine Learning

00:00
5. Regression & Classification

00:00
6. Introduction to sklearn

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7. Starting with Linear Regression

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8. What is Best Fit Line

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9. What is Cost Function

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insurance.csv

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24. Machine Learning Part II

0.TOPIC LIST.jpg

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1. Feature Engineering – Encoding

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10. Lasso Regression (Implementation)

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11. Using LassoCV

00:00
12. ElasticNet Overview

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12. Logistic Regression (Intuition)

00:00
13. Logistic Regression (Cost Fnx)

00:00
14. Logistic Regression (Code)

00:00
2-linear_regression.ipynb

00:00
2. Dummy Variable Trap

00:00
3. Other FE Techniques

00:00
4. Overfitting

00:00
5. Underfitting

00:00
6. How to fix Underfit & Overfit

00:00
7. Is Model underfitting or overfitting

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8. Regularization (Lasso Regression)

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9. Regularization (Ridge Regression)

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heart.csv

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lasso_regression.ipynb

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logistic_regression.ipynb

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25. Machine Learning Part III

01. Standardization & Normalization

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02. Using StandardScaler

00:00
03. Understanding Confusion Matrix

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04. Classification Evaluation Metrics

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05. Naive Bayes (Intuition)

00:00
06. Naive Bayes (Example)

00:00
07. Types of Naive Bayes

00:00
08. Naive Bayes (Code)

00:00
09. kNN – K Nearest Neighbours (Intuition)

00:00
10. kNN (Code)

00:00
11. Limitations of kNN

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12. What is Validation Data_

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13. What is Cross Validation_

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14. CV for Hyperparameter Tuning

00:00
15. Pipeline in sklearn

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knn.ipynb

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naive_bayes.ipynb

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26. Scratch Implementations (Add-on)

0) Day-29 contents.jpg

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1) Section Introduction

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1) Section Introduction.pdf

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10) Logistic Regression Implementation

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11) KNN Classifier (steps)

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12) KNN Classifier Implementation

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13) knn.ipynb

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14) knn.ipynb

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2) Linear Regression with Gradient Descent

00:00
3) LR with GD (Step1)

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4) LR with GD (Step2)

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5) LR with GD (Step3)

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6) LR with GD (Step4)

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7) Linear Regression (with OLS)

00:00
8) LR with Gradient Descent vs OLS

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9) Logistic Regression (steps)

00:00

27. CreditWise Loan System (Minor Project)

01.0)Problem Statement.jpg

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01.1)Problem Statement continued.jpg

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10) Feature Engineering to Improve Model

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11 Important Pointers

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2) loan approval data.csv

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3) Project Introduction

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4) Handle Missing Data

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5) Exploratory Data Analysis(EDA)

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6) Feature Encoding

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7) Correlation Heatmap

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8) Feature Scaling

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9) Model Training & Evolution

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topics.jpg

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28. Supervised Machine Learning Part IV

00) Day-31 Contents.jpeg

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01) Introduction to Decision Trees

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02) Decision Tree Classifier

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03) Entropy (Impurity Metric)

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04) Gini Impurity

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05) Entropy vs Gini Impurity

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06) Information Gain

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07) Pruning Decision Trees

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08) Common Pruning Rules

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09) Decision Tree Classifier (Code)

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10) Pre-Pruning Implementation

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11) Post-Pruning Implementation

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12) Decision Tree Regressor

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13) Variance Reduction

00:00
14) Decision Tree Regressor (Code)

00:00
15) Day-31 Assignment(1).jpeg

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16) Day-31 Assignment(2).jpeg

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17) shop_smart_ecommerce.csv

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17) shop_smart.ipynb

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29. Supervised Machine Learning Part V

00) Day-32 Contents.jpeg

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01) Introduction to SVM

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02) SVM Classifier (Intuition)

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03) Classification Hyperparameters

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04) Kernel in SVM

00:00
05) Common Kernel Types

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06) SVM Classifier (Code)

00:00
07) SVM Regressor (Intuition)

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08) SVM Regressor (Code)

00:00
09) GridSearchCV for Hyperparameters

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10)LinearSVR vs SVR

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11)svr_implementation.ipynb

00:00
12) Ensemble Learning

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13) What is Bagging

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14) What is Boosting

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15) Random Forest

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16) Out Of Bag (OOB)

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17) Random Forest (Implementation)

00:00
18) Decision Tree vs Random Forest

00:00
19) Bagging Classifier & Regressor

00:00
20) code oob.ipynb

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30. Supervised Machine Learning Part VI

05) AdaBoost (Intuition)

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06) AdaBoost (Code)

00:00
07) Other Boosting Algorithms

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08) XGBoost Code

00:00
09) Homogenous vs Heterogenous Ensemble

00:00
1 Gradiant boosting regression

00:00
10) Voting (Logic)

00:00
11) Voting (Code)

00:00
12) Stacking (Logic)

00:00
13) Stacking (Code)

00:00
14) What is Blending

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2 GB Classifier (Intuition)

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3 GB Regressor (Code)

00:00
4 GB Classifier (Code)

00:00
photo_6055445395766185611_y.jpg

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31. Unsuperwised ML Part I

1. Introduction to Unsupervised ML

00:00
10. Hierarchical Clustering

00:00
11. What is Dendrogram_

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12. Hierarchical Clustering (Code)

00:00
13. K-Means v_s Hierarchical Clustering

00:00
14. DBSCAN Clustering

00:00
15. DBSCAN (Code)

00:00
16. K-Means v_s DBSCAN (non-linear)

00:00
2. What is Clustering_

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3. K-Means Clustering

00:00
4. Elbow Method for K

00:00
5. Silhouette Score for K

00:00
6. Random Initialization Trap

00:00
7. K-Means (Code)

00:00
8. Choosing K (Code)

00:00
9. K-Means for Iris Dataset

00:00
DBSCAN.ipynb

00:00
hierarchical_clustering.ipynb

00:00
KMeans.ipynb

00:00
KMeansForIris.ipynb

00:00

32. Unsuperwised ML Part II

1. Dimensionality Reduction

00:00
10. LOF for Anomalies

00:00
11. LOF for Anomalies (Code)

00:00
2. What is PCA_

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3. PCA (Step by Step)

00:00
4. PCA (Code)

00:00
5. What is Anomaly Detection_

00:00
6. Isolation Forest for Anomalies

00:00
7. Isolation Forest for Anomalies (Code)

00:00
8. DBSCAN for Anomalies

00:00
9. DBSCAN for Anomalies (Code)

00:00
clustering_DBSCAN.ipynb

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isolation_forest.ipynb

00:00
PCA.ipynb

00:00
thyroid_dataset.csv

00:00

33. SmartCart Clustering System (Minor Project)

1. Project Introduction

00:00
10. Clustering or Segmentation

00:00
11. Characterization of Clusters

00:00
11

00:00
2. Handle Missing Data

00:00
3. Feature Engineering

00:00
4. Drop Columns

00:00
5. Handle Outliers

00:00
6. HeatMap of Features

00:00
7. Feature Encoding

00:00
8. Visualizing our Data

00:00
9. Choose K

00:00
SmartCart Clustering System.pdf

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smartcart_customers.csv

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smartcart.ipynb

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34.Terminal

0 topics.jpg

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05) Basic Commands

00:00
1. What will we learn

00:00
10. Touch Command

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11) Deleting Files and Folders

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2. What is the terminal

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3. Different Terms related to terminal

00:00
4. Installing git bash

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6. Navigation commands

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7. Paths in navigation

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8. Making Directories

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9. What are flags

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35. Git & GitHub

0 topics.jpg

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03.1) Using Git

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04) Configuring Git

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05) Git with VSCode

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06) Clone command

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07) Status command

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08) Add & Commit Commands

00:00
09) Push command

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1. What is Git and GitHub

00:00
10) Init Command

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11) Pushing Local repo

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12) Workflow

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13) Git branches

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14) Branch Commands

00:00
15) Merging Branches

00:00
16) Pull command

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17) Merge Conflicts

00:00
18) Fixing Mistakes

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19) What is Forking

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2. Creating a Github Account

00:00
20) Git Assignment.png

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3.2 Using GitHub

00:00

36. Deep Learning Part-1

02) Machine Learning vs Deep Learning

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03) What are Neural Networks

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04) Perceptron (in depth)

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05) Multi-layered Neural Networks

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06) PyTorch vs TensorFlow vs Keras

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07) Google Colab (Alternative)

00:00
08) Building a Neuron (Code)

00:00
Day-36 Contents.jpeg

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01) What is Deep Learning

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37. Deep Learning Part-2

01) Forward & Backward Propagation

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02) Loss Functions (Regression)

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03) Loss Functions (Classification)

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04) Weight & Bias Updation

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05) Chain Rule of Derivatives in NN

00:00
06) Vanishing Gradient Problem

00:00
07) ReLU & its variants

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08) Batch vs Iteration vs Epoch

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09) Optimizers

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10) Modern Variants of GD

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Day 37 Contents.jpeg

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38. Deep Learning Part-3

01) ANN for Regression

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02) Loading Dataset

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03) TensorDataset & DataLoader

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04) Building ANN Model

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05) Training the ANN

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06) Saving & Loading the Best Model

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07) Evaluation

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08) ANN for Classification

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09) Model & Evaluation

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1.1) ANN_Regression.ipynb

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10) DateFruit Dataset.csv

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11) Powerplant dataset.csv

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8.1) ANN_Classification.ipynb

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Day-38 Contents.jpeg

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39. Deep Learning Part-4

40. Deep Learning Part-5

00) Day-43 Contents.jpeg

00:00
01) Minor Project [CNN for Image Classification]

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03) Training the CNN

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04) CNN code.ipynb

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05) Popular CNN Architectures.pdf

00:00
06) Natural Language Processing (NLP)

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07) Text Processing Techniques

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08) Text Processing Techniques (Adv.)

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09) Word2Vec (Reading Material).pdf

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10) What is TF-IDF

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11) RNN Architecture

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12) Types of RNN Architectures

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13) Backpropagation in RNN

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14) LSTM (Long Short-Term Memory) Network

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15) LSTM Architecture.pdf

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batches.meta

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data_batch_1

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data_batch_2

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data_batch_3

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data_batch_4

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test_batch

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._batches.meta

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._data_batch_1

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._data_batch_2

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._data_batch_3

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._data_batch_4

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._test_batch

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41. Deep Learning Part-6

00) Contents.jpeg

00:00
01) Minor Project [RNN for Sentiment Analysis]

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02) IMDB Dataset.csv

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03) Pre-processing with Regex

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04) Remove StopWords with NLTK

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05) Stemming & TF-IDF Vectorization

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08) Codes.ipynb

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06) Use DataLoaders & Build RNN

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07) Training & Evaluation

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42. Reinforcement Learning Part-1

00) Contents.jpeg

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01) Reinforcement Learning

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02) Components in RL

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03) Components in Detail

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04) Markov Decision Process (MDP)

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05) Understanding with Grid Example

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06) Policy Optimization in Grid

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07) Importance of Q-Functions

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08) Exploration-Exploitation TradeOff

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43. Reinforcement Learning Part-2

00) Contents.jpeg

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01) Diverse RL Methods

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02) Dynamic Programming

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03) Monte Carlo Methods

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04) Temporal Difference (TD) Learning

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05) SARSA Algorithm

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06) Q-learning Algorithm

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07) Cliff-Walking Problem & Environment

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08) Small Update

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09) SARSA Implementation

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10) What Agent learnt with SARSA

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11) Cilff-walking (SARSA) code.ipynb

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12) Q-learning Implementation

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13) What Agent learnt with Q-learning

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14) Cliff-walking (Q_Learning) Code.ipynb

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44. Reinforcement Learning Part-3

01. Orientataion Session

02. Course Intoduction

03. Python Fundamentals (Part I)

01. Our First Program

00:00
02. Variables in Python

00:00
03. Data Types in Python

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04. Keywords & Comments

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05. Style Guide

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06. Arithmetic Operators

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07. Relational Operators

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08. Assignment Operators

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09. Logical Operators

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10. Operator Precedence

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11. Type Conversion & Casting

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12. Taking User Input

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13. Average of 2 Nums

00:00
14. Python Fundamentals _Part1

00:00
15. Python Fundamentals _Assignment1

00:00

04. Python Fundamentals (Part II)

01. Conditional Statements in Python

00:00
02. Practice Examples (Conditionals)

00:00
03. Odd or Even

00:00
04. Nesting

00:00
05. Match case in Python

00:00
06. Loops using while

00:00
07. Practice Examples (Loops)

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08. Multiplication Table of N

00:00
09. Break & Continue

00:00
10. Loops using for

00:00
11. Vowel Count

00:00
12. range( ) Function

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13. Sum of N numbers

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14. Functions in Python

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15. Practice Examples (Functions)

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16. Types of Functions

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17. Lambda Functions

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18. Factorial of N

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19. Python Fundamentals _Part2

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20. Python Fundamentals _Assignment2

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05. Python Fundamentals (Part III)

01. Conditional Statements in Python

00:00
02. Practice Examples (Conditionals)

00:00
03. Odd or Even

00:00
04. Nesting

00:00
05. Match case in Python

00:00
06. Loops using while

00:00
07. Practice Examples (Loops)

00:00
08. Multiplication Table of N

00:00
09. Break & Continue

00:00
10. Loops using for

00:00
11. Vowel Count

00:00
12. range( ) Function

00:00
13. Sum of N numbers

00:00
14. Functions in Python

00:00
15. Practice Examples (Functions)

00:00
16. Types of Functions

00:00
17. Lambda Functions

00:00
18. Factorial of N

00:00
19. Python Fundamentals _Part2

00:00
20. Python Fundamentals _Assignment2

00:00

06. Python Fundamentals (Part IV)

01. What is Object Oriented Programming

00:00
02. Classes & Objects

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03. Attributes & Methods

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04. Constructor – init( ) Method

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05. Types of Constructors

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06. Attributes – class & instance

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07. Instance Methods

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08. Class Methods

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09. Static Methods

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10. Practice Problem

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11. Encapsulation in OOPs

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12. Inheritance in OOPs

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13. Types of Inheritance

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14. Abstraction

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15. Polymorphism (Function Overriding)

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16. Polymorphism (Duck Typing)

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17. Python Fundamentals Part4

00:00
18. Python Fundamentals _Assignment4

00:00
19. Chat_system_code

00:00

07. Python Fundamentals (Part V)

01. File I_O

00:00
02. Operations on Files

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03. Modes in File Operations

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04. _with_ Keyword

00:00
05. Delete Files

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06. Practice Problem

00:00
07. Exception Handling

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08. _finally_ Keyword

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09. List Comprehensions

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10. Working with JSON Module

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11. Python Fundamentals Part5

00:00
12. Python Fundamentals _Assignment5

00:00

08. Required Installation

09. Phase 2 : Data

10. NumPy

01._Introduction_to_NumPy

00:00
02._Installation_&_Usage

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03._Python_List_vs_NumPy_Array

00:00
04._Creating_Arrays_from_Lists

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05_Creating_Arrays_using_built_in_methods

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06._Array_Properties

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07) Reshaping array

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08._Indexing_on_Arrays

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09._Slicing_Arrays

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10._Copy_v_s_View_in_Slice

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11._Common_NumPy_Data_Types

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12._Multi-dimensional_Arrays_&_Axes

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13._3D_Arrays

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14._Vectorization_&_Broadcasting

00:00
15._Vector_Normalization

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16._Math_-_Mean_&_Standard_Deviation

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17._Mathematical_Functions_(Aggregate)

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18._Other_Math_functions

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19.numpy_tutorial.ipynb

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20._NumPy.pdf

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11. Pandas (Part I)

01._Data_Science_Process

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02._What_is_EDA

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02.5_Introduction_to_Pandas

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03._Series_in_Pandas

00:00
04._Series_Properties

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05._DataFrame_in_Pandas

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06_Pandas_with_csv_&_json_data_give_file

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07._DataFrame_Methods

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08._Using_Kaggle_DataSet

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09._Indexing_&_Selecting_Data

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10._Filtering_Data

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11._Filtering_Data_using_Query

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12_Data_Cleaning_Handle_Missing_Values_give_file

00:00
13._Data_Cleaning_(Handle_Duplicates)

00:00
14._Data_Cleaning_(Handle_Data_Types)

00:00
15._Data_Cleaning_(Handle_Strings)

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employee_data.csv

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pandas_tutorial.ipynb

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raw_data.csv

00:00

12. Pandas (Part II)

01._Data_Transformation

00:00
02_Data_Transformation_other_Methods

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03._Practice_Task

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04_Writing_data_to_csv_&_json_files

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05._Group_by_&_Aggregation

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06_Melt_&_Pivot_for_Reshaping

00:00
07_Basic_Visualization_with_Pandas

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08._Merge_&_Join_Data

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09_Data_Concatenation_with_Pandas

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10._Pandas_Notes.pdf

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11._Pandas_Assignment_Problems.pdf

00:00

13. Data Collection

14. SQL (Part I)

01)What_is_a_Database

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02)SQL_v_s_NoSQL

00:00
03)What_is_SQL

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04)What_is_a_Table

00:00
05)(For_Windows)_Installation

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06)(For_Mac)_Installation

00:00
07)Our_First_Database_RATNA

00:00
08)Our_First_Table

00:00
09)Database_Queries

00:00
10)CREATE_Table

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11)What_are_Constraints

00:00
12)Key_Constraints

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13)Primary_&_Foreign_Keys

00:00
14)INSERT_into_Table

00:00
15)SELECT_Command

00:00
16)Where_Clause

00:00

15. SQL (Part II)

01. Transactions & ACID properties

00:00
02. Commit in Transactions

00:00
03. Rollbacks & Savepoints

00:00
04. JOINs in SQL (inner join)

00:00
05. Left join & Right join

00:00
06. Outer join & Cross join

00:00
07. Self join

00:00
08. Practice problems – Exclusive joins

00:00
09. Sub-Queries in SQL

00:00
10. Views in SQL

00:00
11. Index in SQL

00:00
12. Composite Index

00:00
13. Stored Procedures

00:00
14. Call & Drop procedures

00:00

16.Data Collection (Continuation)

01. Population Techniques (Recap)

00:00
02.Working with APIs

00:00
03.Homework Problem

00:00
04.Starting with Web Scraping

00:00
05.HTML Overview

00:00
06.Important Tags in HTML

00:00
07.Attributes in HTML (1)

00:00
08.Web Scraping (requests Library)

00:00
09.Additional Techniques

00:00
10.Web Scraping (using BeautifulSoup)

00:00
11.BeautifulSoup Methods & Attributes

00:00
12.Storing Collected Data

00:00
13.Web Scraping.pdf

00:00
14.data_collection.ipynb

00:00

17. DAY-16+17 opp html

0.1)Installation_Guide_RATNA.pdf

00:00
02)HTML_Elements_&_Tags

00:00
03)Hello_World

00:00
04)Paragraph_Element_RATNA

00:00
06)Practice_Qs_(1)_RATNA

00:00
07)Boilerplate_Code

00:00
08)Lists_in_HTML

00:00
09)Attributes_in_HTML

00:00
1)Introduction_to_HTML

00:00
10)Anchor_Element

00:00
11)Image_Element

00:00
12)Practice_Qs_RATNA

00:00
13)More_HTML_Tags

00:00
14)Comments_in_HTML

00:00
15)Is_HTML_Case_Sensitive

00:00
16)Practice_Qs_(2)

00:00
17)_Inline_vs_Block

00:00
18)_Div_Element_RATNA

00:00
19)._Span_Element_RATNA

00:00
20)_Hr_Tag_RATNA

00:00
21)._Sup_&_Sub_Tags_RATNA

00:00
22)_Practice_Qs_RATNA

00:00
23)_Semantic_Markup_RATNA

00:00
24)_Semantic_Tags_RATNA

00:00
25)_Practice_Qs_RATNA

00:00
26)_HTML_Entities_RATNA

00:00
27)._Practice_Qs_RATNA

00:00
28)_Emmets_RATNA

00:00
29)Further_Understanding_HTML_RATNA

00:00
30)Assignment_Level_2_(Qs)_RATNA.pdf

00:00
31)Assignment_Level_2_(Ans)_RATNA.pdf

00:00
34)._Tables_in_HTML_RATNA

00:00
35)_Semantics_in_Tables_RATNA

00:00
36)_Colspan_&_Rowspan_Attributes_RATNA

00:00
37_Practice_Qs_RATNA

00:00
38)._Forms_in_HTML_RATNA

00:00
39)._Input_-_Form_Element_RATNA

00:00
40._Placeholders_&_Labels_RATNA

00:00
41)_Button_Element_RATNA

00:00
42)._Name_Attribute_RATNA

00:00
43)._Practice_Qs_RATNA

00:00
44)._Checkbox_-_Input_Element_RATNA

00:00
45)_Radio_-_Input_Element_RATNA

00:00
46)._Select_-_Input_Element_RATNA

00:00
47)_Range_-_Input_Element_RATNA

00:00
48)_Text_Area_RATNA

00:00
49)._Practice_Qs_RATNA

00:00
50)_HTML_Level_3_(Qs)_RATNA.pdf

00:00
51)._HTML_Level_3_(Ans)_RATNA.pdf

00:00
HTML_(Level_1)_Qs_RATNA.pdf

00:00
HTML_Level1_(Ans)_RATNA.pdf

00:00
Prerequisites_RATNA

00:00
Welcome_to_Sigma!_RATNA

00:00
What_is_the_Internet_RATNA

00:00
What_is_Web_Development_RATNA

00:00
What_will_we_learn_RATNA

00:00

18. Data Visualization

1 What is Data Visualization

00:00
2 How to Plot data

00:00
10 Add Bar labels

00:00
11 Multiple datasets on Bar Chart

00:00
12 Bar Charts (Horizontal)

00:00
13 Scatter Plots

00:00
14 Customizations on Scatter Plots

00:00
15 Add Annotations

00:00
16 Multiple datasets on Scatter Plots

00:00
17 Pie Charts

00:00
18 Customizations on Pie Charts

00:00
3 Introduction to Matplotlib

00:00
4 Important Plot Methods

00:00
5 Multiple datasets on Line Plot

00:00
6 Format Strings

00:00
7 Styling & Saving Plots

00:00
8 Common Plots & Charts

00:00
9 Bar Charts (Vertical)

00:00
matplotlib_tutorial_code.ipynb

00:00

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Course Content

19. Data Visualization (Part II)

0.TOPIC LIST.jpg

1-matplotlib_tutorial.ipynb

1. Histograms

10. Introduction to Seaborn

11. Creating Plots with Seaborn

12. Relational Plots in Seaborn

13. Categorical Plots in Seaborn

14. Distribution Plots in Seaborn

15. Relational Plots in Seaborn – Heatmaps

16. Best Practices for Data Visualization

2. Multiple datasets on Histogram

3. axvline in Histogram

4. Box Plots

5. Operations on Box Plot

6. Stack Plots

7. Subplots

8. Modern Matplotlib

9. Practice Task (Weekly Temperature)

DATA VISUALISATION – SEABORN .pdf

Data Visualisation Assignment.pdf

DATA VISUALISATION MATPLOTLIB .pdf

seaborn_tutorial.ipynb

20. Maths for AI ( probability )

3 Probability Examples

4 Solving Practice Problems

5 Types of Events

6 Complementary Rule

7 Addition Rule

8 Multiplication Rule

9 Solving Practice Problems

probab_dis_codet.ipynb

1 Math for AI

10 Conditional Probability

11 Law of Total Probability

12 Bayes Theorem

13 Solving Practice Problem

14 Random Variables

15 Mean, Median & Mode

16 Variance & Standard Deviation

17 Probability Distributions & its Types

18 Binomial Distribution

19 Uniform Distribution

2 Understanding Probability

20 Normal Distribution

21. Math For AI (Linear Algebra)

1 Introduction to Linear Algebra

10 Dot Product

11 Cross Product

12 Matrix

13 Operations on Matrices

14 Determinants

15 Eigen Vectors & Eigen Values

2 Straight Lines

3 Distance between 2 Points

4 Parallel & Perpendicular Lines

5 Distance between Parallel Lines

6 Practice Problem TEAMWORK

7 Vectors

8 Vector Addition

9 Scalar Multiplication

22. Math For AI (Calculas)

1 Calculus Introduction

2 Functions

3 Composite Functions

4 Functions Scalar Multiplication & Addition

5 Input Scalar Multiplication & Addition

6 Differentiation

7 Differentiation Rules

8 Finding Minima Maxima

9 Practice Problem

calculus_code.ipynb

23. Machine Learning Part I

0.TOPIC LIST.jpg

1-linear_regression.ipynb

1. Introduction to ML

10. Understanding Cost Function Curve

11. What is Gradient Descent

12. Summary