Prime 2.0 : Complete AI/ML Job Preparation!

Course Content

01. Orientataion Session

Prime Orientation Session

00:00
GSOC 2025-12-27 (Recorded)

00:00

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

00:00
04. Keywords & Comments

00:00
05. Style Guide

00:00
06. Arithmetic Operators

00:00
07. Relational Operators

00:00
08. Assignment Operators

00:00
09. Logical Operators

00:00
10. Operator Precedence

00:00
11. Type Conversion & Casting

00:00
12. Taking User Input

00:00
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)

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

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

00:00
03. Attributes & Methods

00:00
04. Constructor – init( ) Method

00:00
05. Types of Constructors

00:00
06. Attributes – class & instance

00:00
07. Instance Methods

00:00
08. Class Methods

00:00
09. Static Methods

00:00
10. Practice Problem

00:00
11. Encapsulation in OOPs

00:00
12. Inheritance in OOPs

00:00
13. Types of Inheritance

00:00
14. Abstraction

00:00
15. Polymorphism (Function Overriding)

00:00
16. Polymorphism (Duck Typing)

00:00
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

00:00
03. Modes in File Operations

00:00
04. _with_ Keyword

00:00
05. Delete Files

00:00
06. Practice Problem

00:00
07. Exception Handling

00:00
08. _finally_ Keyword

00:00
09. List Comprehensions

00:00
10. Working with JSON Module

00:00
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

00:00
03._Python_List_vs_NumPy_Array

00:00
04._Creating_Arrays_from_Lists

00:00
05_Creating_Arrays_using_built_in_methods

00:00
06._Array_Properties

00:00
07) Reshaping array

00:00
08._Indexing_on_Arrays

00:00
09._Slicing_Arrays

00:00
10._Copy_v_s_View_in_Slice

00:00
11._Common_NumPy_Data_Types

00:00
12._Multi-dimensional_Arrays_&_Axes

00:00
13._3D_Arrays

00:00
14._Vectorization_&_Broadcasting

00:00
15._Vector_Normalization

00:00
16._Math_-_Mean_&_Standard_Deviation

00:00
17._Mathematical_Functions_(Aggregate)

00:00
18._Other_Math_functions

00:00
19.numpy_tutorial.ipynb

00:00
20._NumPy.pdf

00:00

11. Pandas (Part I)

01._Data_Science_Process

00:00
02._What_is_EDA

00:00
02.5_Introduction_to_Pandas

00:00
03._Series_in_Pandas

00:00
04._Series_Properties

00:00
05._DataFrame_in_Pandas

00:00
06_Pandas_with_csv_&_json_data_give_file

00:00
07._DataFrame_Methods

00:00
08._Using_Kaggle_DataSet

00:00
09._Indexing_&_Selecting_Data

00:00
10._Filtering_Data

00:00
11._Filtering_Data_using_Query

00:00
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)

00:00
employee_data.csv

00:00
pandas_tutorial.ipynb

00:00
raw_data.csv

00:00

12. Pandas (Part II)

01._Data_Transformation

00:00
02_Data_Transformation_other_Methods

00:00
03._Practice_Task

00:00
04_Writing_data_to_csv_&_json_files

00:00
05._Group_by_&_Aggregation

00:00
06_Melt_&_Pivot_for_Reshaping

00:00
07_Basic_Visualization_with_Pandas

00:00
08._Merge_&_Join_Data

00:00
09_Data_Concatenation_with_Pandas

00:00
10._Pandas_Notes.pdf

00:00
11._Pandas_Assignment_Problems.pdf

00:00

13. Data Collection

14. SQL (Part I)

01)What_is_a_Database

00:00
02)SQL_v_s_NoSQL

00:00
03)What_is_SQL

00:00
04)What_is_a_Table

00:00
05)(For_Windows)_Installation

00:00
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

00:00
11)What_are_Constraints

00:00
12)Key_Constraints

00:00
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

19. Data Visualization (Part II)

0.TOPIC LIST.jpg

00:00
1-matplotlib_tutorial.ipynb

00:00
1. Histograms

00:00
10. Introduction to Seaborn

00:00
11. Creating Plots with Seaborn

00:00
12. Relational Plots in Seaborn

00:00
13. Categorical Plots in Seaborn

00:00
14. Distribution Plots in Seaborn

00:00
15. Relational Plots in Seaborn – Heatmaps

00:00
16. Best Practices for Data Visualization

00:00
2. Multiple datasets on Histogram

00:00
3. axvline in Histogram

00:00
4. Box Plots

00:00
5. Operations on Box Plot

00:00
6. Stack Plots

00:00
7. Subplots

00:00
8. Modern Matplotlib

00:00
9. Practice Task (Weekly Temperature)

00:00
DATA VISUALISATION – SEABORN .pdf

00:00
Data Visualisation Assignment.pdf

00:00
DATA VISUALISATION MATPLOTLIB .pdf

00:00
seaborn_tutorial.ipynb

00:00

20. Maths for AI ( probability )

1 Math for AI

00:00
10 Conditional Probability

00:00
11 Law of Total Probability

00:00
12 Bayes Theorem

00:00
13 Solving Practice Problem

00:00
14 Random Variables

00:00
15 Mean, Median & Mode

00:00
16 Variance & Standard Deviation

00:00
17 Probability Distributions & its Types

00:00
18 Binomial Distribution

00:00
19 Uniform Distribution

00:00
2 Understanding Probability

00:00
20 Normal Distribution

00:00
3 Probability Examples

00:00
4 Solving Practice Problems

00:00
5 Types of Events

00:00
6 Complementary Rule

00:00
7 Addition Rule

00:00
8 Multiplication Rule

00:00
9 Solving Practice Problems

00:00
probab_dis_codet.ipynb

00:00

21. Math For AI (Linear Algebra)

1 Introduction to Linear Algebra

00:00
10 Dot Product

00:00
11 Cross Product

00:00
12 Matrix

00:00
13 Operations on Matrices

00:00
14 Determinants

00:00
15 Eigen Vectors & Eigen Values

00:00
2 Straight Lines

00:00
3 Distance between 2 Points

00:00
4 Parallel & Perpendicular Lines

00:00
5 Distance between Parallel Lines

00:00
6 Practice Problem TEAMWORK

00:00
7 Vectors

00:00
8 Vector Addition

00:00
9 Scalar Multiplication

00:00

22. Math For AI (Calculas)

1 Calculus Introduction

00:00
2 Functions

00:00
3 Composite Functions

00:00
4 Functions Scalar Multiplication & Addition

00:00
5 Input Scalar Multiplication & Addition

00:00
6 Differentiation

00:00
7 Differentiation Rules

00:00
8 Finding Minima Maxima

00:00
9 Practice Problem

00:00
calculus_code.ipynb

00:00

23. Machine Learning Part I

0.TOPIC LIST.jpg

00:00
1-linear_regression.ipynb

00:00
1. Introduction to ML

00:00
10. Understanding Cost Function Curve

00:00
11. What is Gradient Descent

00:00
12. Summary

00:00
13. Linear Regression (Code)

00:00
14. Evaluation Metrics

00:00
2. Types of ML (Supervised)

00:00
3. Types of ML (Unsupervised & RL)

00:00
4. Supervised Machine Learning

00:00
5. Regression & Classification

00:00
6. Introduction to sklearn

00:00
7. Starting with Linear Regression

00:00
8. What is Best Fit Line

00:00
9. What is Cost Function

00:00
insurance.csv

00:00

24. Machine Learning Part II

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

00:00
8. Regularization (Lasso Regression)

00:00
9. Regularization (Ridge Regression)

00:00
heart.csv

00:00
lasso_regression.ipynb

00:00
logistic_regression.ipynb

00:00
0.TOPIC LIST.jpg

00:00
1. Feature Engineering – Encoding

00:00
10. Lasso Regression (Implementation)

00:00
11. Using LassoCV

00:00
12. ElasticNet Overview

00:00
12. Logistic Regression (Intuition)

00:00
13. Logistic Regression (Cost Fnx)

00:00
14. Logistic Regression (Code)

00:00

25. Machine Learning Part III

01. Standardization & Normalization

00:00
02. Using StandardScaler

00:00
03. Understanding Confusion Matrix

00:00
04. Classification Evaluation Metrics

00:00
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

00:00
12. What is Validation Data_

00:00
13. What is Cross Validation_

00:00
14. CV for Hyperparameter Tuning

00:00
15. Pipeline in sklearn

00:00
knn.ipynb

00:00
naive_bayes.ipynb

00:00

26. Scratch Implementations (Add-on)

0) Day-29 contents.jpg

00:00
1) Section Introduction

00:00
1) Section Introduction.pdf

00:00
10) Logistic Regression Implementation

00:00
11) KNN Classifier (steps)

00:00
12) KNN Classifier Implementation

00:00
13) knn.ipynb

00:00
14) knn.ipynb

00:00
2) Linear Regression with Gradient Descent

00:00
3) LR with GD (Step1)

00:00
4) LR with GD (Step2)

00:00
5) LR with GD (Step3)

00:00
6) LR with GD (Step4)

00:00
7) Linear Regression (with OLS)

00:00
8) LR with Gradient Descent vs OLS

00:00
9) Logistic Regression (steps)

00:00

27. CreditWise Loan System (Minor Project)

01.0)Problem Statement.jpg

00:00
01.1)Problem Statement continued.jpg

00:00
10) Feature Engineering to Improve Model

00:00
11 Important Pointers

00:00
2) loan approval data.csv

00:00
3) Project Introduction

00:00
4) Handle Missing Data

00:00
5) Exploratory Data Analysis(EDA)

00:00
6) Feature Encoding

00:00
7) Correlation Heatmap

00:00
8) Feature Scaling

00:00
9) Model Training & Evolution

00:00
topics.jpg

00:00

28. Supervised Machine Learning Part IV

00) Day-31 Contents.jpeg

00:00
01) Introduction to Decision Trees

00:00
02) Decision Tree Classifier

00:00
03) Entropy (Impurity Metric)

00:00
04) Gini Impurity

00:00
05) Entropy vs Gini Impurity

00:00
06) Information Gain

00:00
07) Pruning Decision Trees

00:00
08) Common Pruning Rules

00:00
09) Decision Tree Classifier (Code)

00:00
10) Pre-Pruning Implementation

00:00
11) Post-Pruning Implementation

00:00
12) Decision Tree Regressor

00:00
13) Variance Reduction

00:00
14) Decision Tree Regressor (Code)

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

00:00
16) Day-31 Assignment(2).jpeg

00:00
17) shop_smart_ecommerce.csv

00:00
17) shop_smart.ipynb

00:00

29. Supervised Machine Learning Part V

00) Day-32 Contents.jpeg

00:00
01) Introduction to SVM

00:00
02) SVM Classifier (Intuition)

00:00
03) Classification Hyperparameters

00:00
04) Kernel in SVM

00:00
05) Common Kernel Types

00:00
06) SVM Classifier (Code)

00:00
07) SVM Regressor (Intuition)

00:00
08) SVM Regressor (Code)

00:00
09) GridSearchCV for Hyperparameters

00:00
10)LinearSVR vs SVR

00:00
11)svr_implementation.ipynb

00:00
12) Ensemble Learning

00:00
13) What is Bagging

00:00
14) What is Boosting

00:00
15) Random Forest

00:00
16) Out Of Bag (OOB)

00:00
17) Random Forest (Implementation)

00:00
18) Decision Tree vs Random Forest

00:00
19) Bagging Classifier & Regressor

00:00
20) code oob.ipynb

00:00

30. Supervised Machine Learning Part VI

05) AdaBoost (Intuition)

00:00
06) AdaBoost (Code)

00:00
07) Other Boosting Algorithms

00:00
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

00:00
2 GB Classifier (Intuition)

00:00
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_

00:00
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_

00:00
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_

00:00
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

00:00
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

00:00
11. Characterization of Clusters

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

00:00
smartcart_customers.csv

00:00
smartcart.ipynb

00:00

34.Terminal

0 topics.jpg

00:00
05) Basic Commands

00:00
1. What will we learn

00:00
10. Touch Command

00:00
11) Deleting Files and Folders

00:00
2. What is the terminal

00:00
3. Different Terms related to terminal

00:00
4. Installing git bash

00:00
6. Navigation commands

00:00
7. Paths in navigation

00:00
8. Making Directories

00:00
9. What are flags

00:00

35. Git & GitHub

0 topics.jpg

00:00
03.1) Using Git

00:00
04) Configuring Git

00:00
05) Git with VSCode

00:00
06) Clone command

00:00
07) Status command

00:00
08) Add & Commit Commands

00:00
09) Push command

00:00
1. What is Git and GitHub

00:00
10) Init Command

00:00
11) Pushing Local repo

00:00
12) Workflow

00:00
13) Git branches

00:00
14) Branch Commands

00:00
15) Merging Branches

00:00
16) Pull command

00:00
17) Merge Conflicts

00:00
18) Fixing Mistakes

00:00
19) What is Forking

00:00
2. Creating a Github Account

00:00
20) Git Assignment.png

00:00
3.2 Using GitHub

00:00

36. Deep Learning Part-1

01) What is Deep Learning

00:00
02) Machine Learning vs Deep Learning

00:00
03) What are Neural Networks

00:00
04) Perceptron (in depth)

00:00
05) Multi-layered Neural Networks

00:00
06) PyTorch vs TensorFlow vs Keras

00:00
07) Google Colab (Alternative)

00:00
08) Building a Neuron (Code)

00:00
Day-36 Contents.jpeg

00:00

37. Deep Learning Part-2

01) Forward & Backward Propagation

00:00
02) Loss Functions (Regression)

00:00
03) Loss Functions (Classification)

00:00
04) Weight & Bias Updation

00:00
05) Chain Rule of Derivatives in NN

00:00
06) Vanishing Gradient Problem

00:00
07) ReLU & its variants

00:00
08) Batch vs Iteration vs Epoch

00:00
09) Optimizers

00:00
10) Modern Variants of GD

00:00
Day 37 Contents.jpeg

00:00

38. Deep Learning Part-3

01) ANN for Regression

00:00
02) Loading Dataset

00:00
03) TensorDataset & DataLoader

00:00
04) Building ANN Model

00:00
05) Training the ANN

00:00
06) Saving & Loading the Best Model

00:00
07) Evaluation

00:00
08) ANN for Classification

00:00
09) Model & Evaluation

00:00
1.1) ANN_Regression.ipynb

00:00
10) DateFruit Dataset.csv

00:00
11) Powerplant dataset.csv

00:00
8.1) ANN_Classification.ipynb

00:00
Day-38 Contents.jpeg

00:00

39. Deep Learning Part-4

40. Deep Learning Part-5

06) Natural Language Processing (NLP)

00:00
07) Text Processing Techniques

00:00
08) Text Processing Techniques (Adv.)

00:00
09) Word2Vec (Reading Material).pdf

00:00
10) What is TF-IDF

00:00
11) RNN Architecture

00:00
12) Types of RNN Architectures

00:00
13) Backpropagation in RNN

00:00
14) LSTM (Long Short-Term Memory) Network

00:00
15) LSTM Architecture.pdf

00:00
00) Day-43 Contents.jpeg

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

00:00
03) Training the CNN

00:00
04) CNN code.ipynb

00:00
05) Popular CNN Architectures.pdf

00:00

41. Deep Learning Part-6

00) Contents.jpeg

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

00:00
02) IMDB Dataset.csv

00:00
03) Pre-processing with Regex

00:00
04) Remove StopWords with NLTK

00:00
05) Stemming & TF-IDF Vectorization

00:00
06) Use DataLoaders & Build RNN

00:00
07) Training & Evaluation

00:00
08) Codes.ipynb

00:00

42. Reinforcement Learning Part-1

00) Contents.jpeg

00:00
01) Reinforcement Learning

00:00
02) Components in RL

00:00
03) Components in Detail

00:00
04) Markov Decision Process (MDP)

00:00
05) Understanding with Grid Example

00:00
06) Policy Optimization in Grid

00:00
07) Importance of Q-Functions

00:00
08) Exploration-Exploitation TradeOff

00:00

43. Reinforcement Learning Part-2

00) Contents.jpeg

00:00
01) Diverse RL Methods

00:00
02) Dynamic Programming

00:00
03) Monte Carlo Methods

00:00
04) Temporal Difference (TD) Learning

00:00
05) SARSA Algorithm

00:00
06) Q-learning Algorithm

00:00
07) Cliff-Walking Problem & Environment

00:00
08) Small Update

00:00
09) SARSA Implementation

00:00
10) What Agent learnt with SARSA

00:00
11) Cilff-walking (SARSA) code.ipynb

00:00
12) Q-learning Implementation

00:00
13) What Agent learnt with Q-learning

00:00
14) Cliff-walking (Q_Learning) Code.ipynb

00:00

44. Reinforcement Learning Part-3

45. Reinforcement Learning Part-4

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Course Content

01. Orientataion Session

Prime Orientation Session

GSOC 2025-12-27 (Recorded)

02. Course Intoduction

01. Course Objective

02. What will we Learn

03. Schedule

04. Why did we choose Python

05. Tools to Install

06. Installation Guide Prime

07. Using Visual Studio Code

03. Python Fundamentals (Part I)

01. Our First Program

02. Variables in Python

03. Data Types in Python

04. Keywords & Comments

05. Style Guide

06. Arithmetic Operators

07. Relational Operators

08. Assignment Operators

09. Logical Operators

10. Operator Precedence

11. Type Conversion & Casting

12. Taking User Input

13. Average of 2 Nums

14. Python Fundamentals _Part1

15. Python Fundamentals _Assignment1

04. Python Fundamentals (Part II)

01. Conditional Statements in Python

02. Practice Examples (Conditionals)

03. Odd or Even

04. Nesting

05. Match case in Python

06. Loops using while

07. Practice Examples (Loops)

08. Multiplication Table of N

09. Break & Continue

10. Loops using for

11. Vowel Count

12. range( ) Function

13. Sum of N numbers

14. Functions in Python

15. Practice Examples (Functions)

16. Types of Functions

17. Lambda Functions

18. Factorial of N

19. Python Fundamentals _Part2

20. Python Fundamentals _Assignment2

05. Python Fundamentals (Part III)

01. Conditional Statements in Python

02. Practice Examples (Conditionals)

03. Odd or Even

04. Nesting

05. Match case in Python

06. Loops using while

07. Practice Examples (Loops)

08. Multiplication Table of N

09. Break & Continue

10. Loops using for

11. Vowel Count

12. range( ) Function

13. Sum of N numbers

14. Functions in Python

15. Practice Examples (Functions)

16. Types of Functions

17. Lambda Functions

18. Factorial of N

19. Python Fundamentals _Part2

20. Python Fundamentals _Assignment2

06. Python Fundamentals (Part IV)

01. What is Object Oriented Programming

02. Classes & Objects

03. Attributes & Methods

04. Constructor – init( ) Method

05. Types of Constructors

06. Attributes – class & instance

07. Instance Methods

08. Class Methods

09. Static Methods