1. Statastics (4 Days)
1.1. Descriptive
1.1.1. Measure of Central Tendency
1.1.1.1. Mean
1.1.1.2. Mode
1.1.1.3. Median
1.1.2. Measure of Variation
1.1.2.1. Range
1.1.2.2. Variance
1.1.2.3. Standard Deviation
1.2. Inferential
1.2.1. Hypothesis Testing
1.2.1.1. Types
1.2.1.1.1. One -Tail Test
1.2.1.1.2. Two Tail Test
1.2.1.2. Confidence Intervals
1.2.1.2.1. Alpha Value
1.2.1.2.2. P Value
1.2.1.3. Error Types
1.2.1.3.1. Type 1 Error
1.2.1.3.2. Type 2 Error
1.2.1.4. Test Statastic
1.2.1.4.1. Z - Test
1.2.1.4.2. T- Test
1.2.1.4.3. Chi-sq Test
1.2.1.4.4. Anova
1.2.2. Co Relation
1.2.2.1. Multi Corelation
1.2.2.2. Auto Corelation
1.2.3. Co Variance
1.2.4. Regression Analysis
2. Probaility (4 Days)
2.1. Random Variables
2.1.1. Types
2.1.1.1. Numerical
2.1.1.1.1. Discrete
2.1.1.1.2. Continuous
2.1.1.2. Categorical
2.1.2. Levels of measurement
2.1.2.1. Qualitative
2.1.2.1.1. Nominal
2.1.2.1.2. Ordinal
2.1.2.2. Quantitative
2.1.2.2.1. Interval
2.1.2.2.2. Ratio
2.2. PDF
2.2.1. Discrete
2.2.1.1. Binomial
2.2.1.2. Bernouli
2.2.1.3. Poisions
2.2.2. Continuous
2.2.2.1. Data Distributions
2.2.2.1.1. Left Skewed
2.2.2.1.2. Normal Distribution
2.2.2.1.3. Right Skewed
2.2.2.2. Central Limit Theorem
2.2.2.3. Chi-sq Test
2.2.2.4. T Test
2.3. Types of Probabilities
2.3.1. Joint probability
2.3.2. Conditional Probability
2.4. Bayes Theorem
3. Python (15 Days)
3.1. Basics
3.1.1. Data Structures
3.1.2. Conditional Statements
3.1.3. Loops
3.1.4. Functions
3.1.5. Oops
3.1.6. Regular Expressions
3.2. Libraries
3.2.1. Numpy
3.2.2. Pandas
3.2.3. Beautiful Soup
3.2.4. Scikit Learn
3.3. IDE
3.3.1. Anaconda
3.3.1.1. Jupyter
3.3.1.2. Spyder
3.3.2. Google Colab
3.3.3. Pycharm
3.3.4. Visual Stuido
4. Data Preprocessing (10 Days)
4.1. Data Preparation
4.1.1. Data Cleaning
4.1.1.1. Handling Missing Values
4.1.1.2. Encoding
4.1.1.3. Handling Outliers
4.1.1.4. Binning
4.1.1.5. Data Deduplication
4.1.2. Feature Engineering
4.1.2.1. Feature Selection
4.1.2.1.1. Sampling
4.1.2.2. Feature Scaling
4.1.2.2.1. Normalization
4.1.2.2.2. Standardization
4.1.2.2.3. Robust Scaler
4.1.2.3. Feature Transformation
4.1.2.3.1. Box-Cox
4.1.2.3.2. log / square / cube
4.1.2.4. Feature Spilt
4.1.2.5. Feature Extraction
4.1.2.6. Feature Generation
4.2. Exploratory Data Analysis
4.2.1. Methods
4.2.1.1. Quantitative
4.2.1.2. Graphical
4.2.1.2.1. Python
4.2.1.2.2. Power BI
4.2.1.2.3. Tabuleau
4.2.2. Types
4.2.2.1. Uni Variant Analysis
4.2.2.2. Bi Variant Analysis
4.2.2.3. Multi Variante Analysis
5. Machine Learning (20 Days)
5.1. Model Building
5.1.1. Supervised Learning
5.1.1.1. Regression
5.1.1.1.1. Linear
5.1.1.1.2. Multi Linear
5.1.1.1.3. Polynomial
5.1.1.1.4. Regularization
5.1.1.2. Classification
5.1.1.2.1. Logistic Regression
5.1.1.2.2. KNN
5.1.1.2.3. Navie Bayes
5.1.1.2.4. SVM
5.1.1.2.5. Decision Tress
5.1.1.2.6. Types
5.1.2. Unsupervised Learning
5.1.2.1. Clustering
5.1.2.1.1. K-Means
5.1.2.1.2. Hierarchical Clustering
5.1.2.1.3. Mean Shift
5.1.2.1.4. DB Scan
5.1.2.1.5. Fuzzy C Means
5.1.2.2. Dimensionality Reduction
5.1.2.2.1. PCA
5.1.2.2.2. SVD
5.1.2.2.3. LDA
5.1.3. Ensemble Learning
5.1.3.1. Stacking
5.1.3.2. Boosting
5.1.3.2.1. Adaboost
5.1.3.2.2. Gradient Boosting
5.1.3.2.3. XG Boost
5.1.3.2.4. Cat , Light
5.1.3.3. Bagging
5.1.3.3.1. Random Forest
5.1.3.3.2. Decision Trees
5.1.4. Reinforcement Learning
5.2. Model Evaluation
5.2.1. Bias - Variance Tradeoff
5.2.2. Performance Metrices
5.2.2.1. Regression
5.2.2.1.1. MAE
5.2.2.1.2. MSE
5.2.2.1.3. RMSE
5.2.2.1.4. R - Square
5.2.2.1.5. Adjusted R Square
5.2.2.2. Classification(Confusion Matrix)
5.2.2.2.1. Precision
5.2.2.2.2. Accuracy
5.2.2.2.3. Re Call
5.2.2.2.4. F Score
5.2.2.2.5. Specificity
5.2.3. Model Fit
5.2.3.1. Under Fitting
5.2.3.2. Over Fitting
5.2.3.3. Best Fit
5.2.4. Hyperparameter Tuning
5.2.4.1. Grid Search
5.2.4.2. Random Search
5.2.5. Cross Validation
5.2.5.1. K - Fold
5.2.5.2. Stratified K - Fold
6. Deep Learning (10 Days)
6.1. Terminologies
6.1.1. Perceptrons
6.1.2. Layers
6.1.2.1. Input
6.1.2.2. Output
6.1.2.3. Hidden
6.1.3. Weight Matrix
6.1.3.1. Weights
6.1.3.2. Bias
6.1.4. Learning Rate
6.1.5. Epoch
6.1.6. Local - Global Minima
6.1.7. Early Stooping
6.1.8. Dropout Layer
6.2. ANN
6.2.1. Forward Propagation
6.2.1.1. Activation Functions
6.2.1.1.1. Sigmoid
6.2.1.1.2. Tanh
6.2.1.1.3. RELU
6.2.1.1.4. Leaky RELU
6.2.1.1.5. MaxOut
6.2.1.1.6. ELu
6.2.1.1.7. Soft Max
6.2.1.1.8. Swish
6.2.2. Back Propagation
6.2.2.1. Chain Rule
6.2.2.2. Loss Functions
6.2.2.2.1. Regression
6.2.2.2.2. Binary Classification
6.2.2.2.3. Multi Class Classification-
6.2.2.3. Optimizers
6.2.2.3.1. Gradient Descent
6.2.2.3.2. SGD With Momentum
6.2.2.3.3. Ada Grad
6.2.2.3.4. NAG
6.2.2.3.5. Ada delta
6.2.2.3.6. Adam
6.2.2.3.7. RMSprop
6.3. Frame Works
6.3.1. Tensor Flow
6.3.2. PyTorch
6.3.3. Keras
6.4. Unsupervised DL
6.4.1. Auto Encoders
6.4.1.1. Sparse Auto Encoders
6.4.1.2. Denoising
6.4.1.3. Contractive
6.4.1.4. Generative Models
6.4.1.4.1. Variationally Auto Encoders
6.4.1.4.2. GAN's
6.4.2. Boltzman Machines
6.4.3. Data Augmentation
7. Time Series Analysis (7 Days)
7.1. Components of Time Series
7.1.1. Trend
7.1.2. Seasonality
7.1.3. Cycle
7.1.4. Stationary / Non-Stationary
7.2. Smoothing Techniques
7.2.1. AR
7.2.2. MA
7.3. Steps in Time Series
7.3.1. Check stationary
7.3.1.1. Dicky Fuller Test
7.3.2. Stationarize
7.3.2.1. De-Trending
7.3.2.2. Differencing
7.3.2.3. Decomposition
7.3.2.4. Moving Average
7.3.2.5. De Seasonality
7.3.2.6. Log Transformation
7.3.3. ACF/ PACF Plots
7.3.4. Build ML Models
7.3.4.1. AR-MA
7.3.4.2. ARIMA
7.3.4.3. SARIMA
7.3.4.4. SARIMAX
7.4. Error Measures
8. Recommendation Systems (4 days)
8.1. Types
8.1.1. Content Based Filtering
8.1.2. Collaborative Filtering
8.1.2.1. Item - Item
8.1.2.2. User - User
8.2. Cold Start Problem
8.3. Error Measures
9. NLP ( 8 Days)
9.1. Types
9.1.1. NLU
9.1.2. NLG
9.2. Components of NLP
9.2.1. Morphological & Lexical Analysis
9.2.2. Syntactic Analysis
9.2.3. Semantic Analysis
9.2.4. Discourse Integration
9.2.5. Pragmatic Analysis
9.3. Terminology
9.3.1. Corpus
9.3.2. Parsing
9.3.3. Tokens
9.3.4. Tokenization
9.3.5. Lexicon
9.4. Text Cleaning
9.4.1. Tokenization
9.4.2. Noise Entities Removal
9.4.3. Removal of Stop Words
9.4.4. POS Tagging
9.4.5. Normalization
9.4.5.1. Stemming
9.4.5.2. Lemmatization
9.5. Text Representation in Vector Space
9.5.1. Bag of Words
9.5.2. Word Embeddings
9.5.2.1. Word2Vec
9.5.2.1.1. CBOW
9.5.2.1.2. Skip Gram
9.5.2.2. Glove
9.5.3. SVD
9.5.4. TF-IDF
9.5.5. Count Vectorizer
9.6. Topic Modelling
9.6.1. LDA
9.7. Sequential Modeling
9.7.1. RNN
9.7.1.1. One To Many
9.7.1.2. Many To Many
9.7.1.3. Many To One
9.7.2. LSTM
9.7.3. GRU
9.8. Transfer Learning
9.8.1. BERT
9.8.2. GTP 2
9.9. Libraries
9.9.1. NLTK
9.9.2. Spacy
9.9.3. Genism
9.9.4. Core NLP
9.9.5. Text Blob
9.9.6. Hugging Face
9.10. Conversational AI
9.10.1. Text (Chat Bots)
9.10.1.1. Azure Bot Framework
9.10.1.2. Amazon lex
9.10.1.3. Google Dialogflow
9.10.1.4. Rasa
9.10.1.5. kore.ai
9.10.2. Audio
10. Computer Vision (8 Days)
10.1. OpenCV
10.1.1. Reading / Storing / Writing Images
10.1.2. Resizing / Rotating / Cropping the Image
10.1.3. Drawing Functions
10.1.4. Changing Image Colors / Channels
10.1.5. Spilting / Merging Images
10.1.6. Accessing / Modifying Pixel Values
10.1.7. Accessing / Modifying Image Properties
10.1.8. Reading Edges
10.1.9. Image Filter Functions
10.1.10. Thresholding
10.1.11. Transformation
10.1.12. Extracting the Region of Interest (ROI)
10.1.13. HOG
10.2. Essentials
10.2.1. Bounding Boxes
10.2.2. IOU
10.2.3. Anchor Boxes
10.2.4. Regional Proposals
10.2.5. Non - Max supression
10.3. Functions
10.3.1. Classification
10.3.2. Segmentation
10.3.3. Localization
10.3.4. Object Detection
10.4. CNN
10.4.1. Terminologies
10.4.1.1. Padding
10.4.1.1.1. Valid
10.4.1.1.2. Same
10.4.1.2. Stride
10.4.1.3. Pooling layer
10.4.1.3.1. Max
10.4.1.3.2. Average
10.4.1.3.3. Sum
10.4.1.4. Convolution Layer
10.4.2. Architectures
10.4.2.1. VGG - 16 , 19
10.4.2.2. RCNN : Fast , Faster , Mask RCNN
10.4.2.3. YOLO
10.4.2.4. Lenet 5
10.4.2.5. AlexNet
10.4.2.6. Resnet
10.4.2.7. Inception
11. Deployment (5 Days)
11.1. Frame Works
11.1.1. Flask
11.1.2. Django
11.2. Clouds
11.2.1. AWS Sage Maker
11.2.2. Azure
11.2.3. GCP
11.2.4. Heroku
12. Resources
12.1. Websites
12.1.1. Towards Data Science
12.1.2. Math is Fun
12.1.3. Data camp
12.1.4. Analytics Vidya
12.1.5. Medium
12.1.6. Kd Nuggets
12.1.7. Machine Learning Mastery
12.2. You Tube
12.2.1. Krish Naik
12.2.2. Codebasics (Computer Vision)
12.2.3. FreeCodeCamp
12.2.4. statquest (Stats)
12.2.5. Telusko (Python)
12.3. Datasets
12.3.1. Scikit-learn datasets
12.3.2. Kaggle
12.3.3. UCI Machine Learning Repository
12.3.4. Government Datasets
12.3.5. Google's Dataset Search Engine
12.3.6. Registry of Open Data on AWS
12.3.7. Microsoft Datasets
12.3.8. Awesome Public Datasets Collection
12.3.9. Visualdata
12.4. Increase Coding Skills
12.4.1. Hackeerank
12.4.2. LeetCode
12.4.3. HackerEarth
12.4.4. CodeChef
12.4.5. Geeks for Geeks
13. Profile Building (3 Days)
13.1. Linkedin
13.2. Github
13.3. Writing Blogs
13.4. Portfolio
13.5. Resume Making
13.5.1. Jobscan's
13.5.2. CakeResume
13.5.3. Resume Genius
13.5.4. Zety
13.5.5. Overleaf
13.5.6. My Perfect Resume
13.5.7. NovoResume
13.5.8. KickResume
13.6. Apply for Jobs
14. Further
14.1. Pipelines
14.2. MLOps
14.3. AIOps
14.4. Big Data
14.5. No Code ML
14.5.1. PyCaret
14.5.2. BigML
14.5.3. Create ML
14.5.4. Google Cloud AutoML
14.5.5. RunwayML
15. Tips
15.1. Never Underestimate role of datapreprocessing
15.2. Just use one and stick to it
15.3. Focus on one course
15.4. Practice more
15.5. Don’t spend too much time on theory
15.6. Get engaged in Data Science communities
15.7. Narrow down your expertise
15.8. You don’t have to know everything beforeapplying to jobs
15.9. Study Research Papers
15.10. Keep up to date with trends
15.11. Participate in Hackathons
15.12. Case Studies
16. Jobs
16.1. Roles
16.1.1. Data Analyst
16.1.2. Data Engineer
16.1.3. Data Scientist
16.1.4. ML Engineer /Developer
16.1.5. NLP Engineer
16.1.6. CV Engineer
16.1.7. AI Engineer / Developer
16.1.8. Business Analyst
16.1.9. BI Developers
16.1.10. Researchers
16.1.11. MLops Engineers
16.2. Places To Hunt
16.2.1. Naukri
16.2.2. Linkedin
16.2.3. AngelList
16.2.4. Cut Short
16.2.5. Hirist
16.2.6. Indeed
16.3. Companies
16.3.1. The Math Company
16.3.2. Genpact
16.3.3. Tredence Analytics
16.3.4. Tiger Analytics
16.3.5. Ugam
16.3.6. Fractel Analytics
16.3.7. GE Health Care
16.3.8. Bridgei2i
16.3.9. Latentview
17. Introduction (2 Days)
17.1. Why
17.1.1. High Demand
17.1.2. High Pay
17.1.3. Respected Role
17.1.4. Versatile Career
17.2. Required
17.2.1. Domain Knowledge
17.2.2. Programming
17.2.2.1. Python/R
17.2.2.2. SQL
17.2.2.3. Web Scrapping
17.2.2.4. Version Control
17.2.3. Mathematics
17.2.3.1. Linear Algebra
17.2.3.2. Statistics & Probability
17.2.3.3. Matrix Manipulation
17.2.3.4. Calculus
17.2.4. Communication Skills (Story Telling)
17.2.5. Analytical Mindset
17.3. Life Cycle
17.4. Basic Terminology
17.4.1. Data
17.4.1.1. Types
17.4.1.1.1. Structured
17.4.1.1.2. Semi Structured
17.4.1.1.3. Un Structured
17.4.1.2. Analysis
17.4.1.2.1. Descriptive Analysis
17.4.1.2.2. Diagnostic Analysis
17.4.1.2.3. Predictive Analysis
17.4.1.2.4. Prescriptive Analysis
17.4.2. Population - Sample
17.4.3. Dependent - Independent Variables
17.4.4. DS vs ML vs Dl vs AI