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Study Material
Artificial Intelligence With Generative AI Material
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-1(Day 1 to Day 10)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-2(Day 11 to Day 20)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-3(Day 21 to Day 30)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-4(Day 31 to Day 40)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-5(Day 41 to Day 49)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-6(Day 50 to Day 59)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-7(Day 60 to Day 69)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-8(Day 70 to Day 79)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-9(Day 80 to Day 89)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-10(Day 90 to Day 97)
100 Days of Artificial Intelligence GENERATIVE AI – From Beginner to Pro Part-11(Day 98 to Day 100)
Ai With GenAi Video Sessions
SESSION 1 - Introduction to AI, ML, DL, GenAI
SESSION 2 - Life Cycle of AI, _Data Analysis_Data Analytics
SESSION 3_Data Wrangling_Data Cleansing_DataMining.mp4
SESSION 4 - Project Discussion- LLM
SESSION 5 - Python Introduction- Variable_DataTypes
SESSION 6 - Type Casting, Integer, Float, Complex, Boolean
SESSION 7 - Strings, Indexing, Slicing
SESSION 8 - Input Function, If condition, inline if
SESSION 9 - WHILE LOOP, FOR LOOP, RANGE, LIST
SESSION 10 - LIST2, LIST CRUD, LIST COMPREHENSION
SESSION 11 - FUNCITON, LAMBDA, MAP
SESSION 12 - LAMBDA FILTER, LAMBDA REDUCE, REGULAR EXPRESSION
SESSION 13 - REGULAR EXPRESSION - 2
SESSION 14 - REGULAR EXPRESSION - 2
SESSION 15 - NUMPY 1
SESSION 16 - NUMPY 2
SESSION 17 - NUMPY 3 -Indexing, Slicing, Boolean Index
SESSION 18 - NUMPY 4 - NUMPY FUNCTIONS
SESSION 19 - PANDAS 1- Series
SESSION 20 - PANDAS 2 - DataFrame, Columns -
SESSION 21 - PANDAS 3 - Dataframe rows, Slicing
SESSION 22 - PANDAS 4 - Read_CSV, NAN, Fillna, Dropna, Replace
SESSION 23 - PANDAS 5 = Concat, Merge, pivot
SESSION 24 - PANDAS 5 - Pivot Table, Cross Tab, Groupby1
SESSION 25 -PANDAS 6 - Groupby
SESSION 26 - Feature Engineering-1, Statistics - 1
SESSION 27 - Statistics - 2, Descriptive Statistics, IQR Method, Mean-std Method
SESSION 28 - Feature Engineering - Error Detection Implementation
SESSION 29 - Feature Engineering 2 - Error Detection 2
SESSION 30 - FE 3 - Automate Error Detection - IQR - Mean Std
SESSION 31 - FE4 - Encoding-Theory
SESSION 32 - Encoding Implementation, OneHotEncoder, Ordinal Encoder
SESSION 33 - Data Separation, Independent Columns, Dependent Columns
SESSION 34 - Data Splitting, ML End-End Implementation
SESSION 35 - Deep Learning, Perceptron, ANN
SESSION 36 - Deep Learning 2, Internal Mechanism, ML Drawbacks, DL Dependencies,History
SESSION 37 - Perceptron Internal Mechanism, Weights, Activation Function, Step AF, Signum AF, Linear AF
SESSION 38 - Relu AF, Leaky AF, Sigmoid AF, Tanh AF, Softmax
SESSION 39 - Tensorflow - 1
SESSION 40 - Tensorflow 2, Tensorflow Implementation, Keras
SESSION 41 -Keras, Keras models, Sequential model
SESSION 42 - Keras 2, Models, ANN MNIST
SESSION 43 - ANN MNIST Implementation
SESSION 44 - Functional Model, ANN Fashion MNIST
SESSION 45 - ANN Implementation - Cifar 10 Dataset
SESSION 46 - CNN, Filter, Strides, Kernel
SESSION 47 - CNN, Filter, Strides, Kernel
SESSION 48 - CNN, Internal Mechanism, Filter Map
SESSION 49 - CNN, Padding, Pooling Layer, CNN Implementation
SESSION 50 - CNN Implementation - CIfar 10
SESSION 51 - CNN Layers, Dropout Layer, Batch Normalization
SESSION 52 - CNN Implementation - Gender Detection
SESSION 53 - CNN Implementation - Gender Detection 2
SESSION 54 - CNN Implementation - Gender Detection - 3 part
SESSION 55 - NLP Introduction,, Internal Mechanism,
SESSION 56 - NLP NLU, NLG, Different Analysis, NLP Challenges
SESSION 57 - NLP Approachs, ML Approach, DL Approach, NLP Models
SESSION 58 - NLP Pipeline, Data Acquisition
SESSION 59 - NLP Preprocessing, Data Cleansing, Data Normalization,
SESSION 60 - NLP Pipeline - Data Cleansing Implementation
SESSION 61 - NLP Normalization Implementations, Contractions, Abbrevations, Spelling Correction
SESSION 62 - NLP Normalization - TextBlob, PySpellChecker, Tokenization
SESSION 63 - Stop Words Removal NLP
SESSION 64- Automate Entire NLP Preprocessing
SESSION 65 - NLP Stemming, Porter, Lemmatization
SESSION 66 - Count Vectorization , N- Grams
SESSION 67 - NLP POS tagging, NER
SESSION 68 - NLP Bag of Words
SESSION 69 - NLP TF IDF Vectorization
SESSION 70 - NLP Embedding, Types of Embedding
SESSION 71 - NLP Word Embedding - Word2Vec CBOW,
SESSION 72 - NLP Pre-Trained Word2Vec - Google news
SESSION 73 - NLP Word2Vec External Dataset -game of thrones
SESSION 74 - Glove, Co-occurence Matrix
SESSION 75 - Glove Implementation - Wiki - Gigaword
SESSION 76 - Word Embedding - Fast Text
SESSION 77 - Sequence Data, Types of Sequence Data, Key Features
SESSION 78 - Seq2Seq Models, RNN, Internal Mechanism
SESSION 79-.Types of RNN, RNN Weigts Assign, forward propagation
SESSION 80 - RNN Implementation - Twitter Dataset - Part 1
SESSION 81 - RNN Implementation - 2 - Twitter Dataset
SESSION 82 - LSTM - Architecture - Cell State, Hidden State
SESSION 83 - LSTM 2 - Long Term and Short Term Memory, Forget Gate
SESSION 84 - LSTM - 3, Forget Gate, Input Gate, Output Gate
SESSION 85 - LSMT - Mathematical intuition, LSTM Implementation - 1
SESSION 86 - LSTM Project part 1 - Next word Prediction
SESSION 87 - LSTM Prediction, Encoder - Decoder Architecture
SESSION 89 - Drawback of Encoder - Decoder, Attention Mechanism
SESSION 90 - Transformerc, Encoder Architecture, Input Embedding, Positional Encoding
SESSION 91 - Transformer - Geometrical Intuition, Self_Attention - Contextual Embedding,
SESSION 92 - Self Attention Intuition, Query Vecttor, Key Vector, Value Vector
SESSION 93-Multi-Head, Masked Multi Head, Cross Head Self Attention
SESSION 94 -Language Model, Small LM, Medium LM, LLM, Types of LLM
SESSION 95 - Large Language Model, Core Components of LLM, Draw Backs of LLM, OpenAI
SESSION 96 - BERT, Architecture, Types of BERT Model, MLM, NSP
SESSION 97 - BERT Model Fine Tuning - 1
SESSION 98- BERT Model Fine Tuning - 2
SESSION 99 - BERT Model Prediction - Part 1 - IMDB
SESSION 100 - BERT Model Prediction - Part 2 - IMDB
SESSION 101- GPT, OPEN AI
SESSION 102 - Different Type of GPT, RLHF, Reward Model, OPENAI API Key
SESSION 103 - Prompt Engineering, Core Elements of PE
SESSION 104 - Prompt Engineering Capability,Skills Required , Types of Prompts
SESSION 105 - Prompt Engineering, Zero Shot, One Shot, Few Shot
SESSION 106 - Prompt Engineering - Batch Prompt - Chain of thoughts
SESSION 107 - Prompt Engineering - Tree of Thoughts, Meta Prompts
SESSION 108 - LangChain, Key Components of LangChain
SESSION 109 - LangChain Models, Prompts, Indexes, Chain, Memory
SESSION 110 -Agents, Output parser, Tools, VS Code Configuration
SESSION 111 - OPEN AI API Key, Model LLM, Groq
SESSION 112 - LLM, ChatModel OpenAI, HuggingFace
SESSION 113 - LangChain PromptTemplate, PromptTemplate Implementation
SESSION 114 - LangChain ChatPromptTemplate, ChatPrompt Implementation
SESSION 115 - LangChain FewShotPromptTemplate, Multiple Examples
SESSION 116 - LangChain Output Parser, StrOutputParser
SESSION 117 - LangChain Output Parser, JsonOutputParser
SESSION 118 - LangChain Output Parser, JsonOutput2, Structured Output Parser
SESSION 119 - LangChain Output Parse - Pydantic Output Parser -1
SESSION 120 - LangChain Chain - Simple Chain, Structured Chain
SESSION 121 - Sequential Chain, Parallel Chain, RunnableParallel
SESSION 122 - LangChain - Parallel Chain - 2
SESSION 123 - LangChain - Conditional Chain