Vande Bharath
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Our institute provides easy-to-follow data science training, covering machine learning, data analysis, and statistics. With practical projects and expert guidance, we help you gain real skills to kickstart your career in data science. Join us for hands-on learning and support every step of the way!
In 2005, Syed Mahmood obtained a degree in electronics engineering.He is almost ten years experienced in Excel, Robotics, and Gen AI. He works as a data science trainer.
Mr. Subramanya Swamy H N has been implementing his passion towards teaching from the past 10+ years. He teaches any complex topic in a very simpler manner by giving real time examples such that even naive can easily understands the subject in a crystal clear manner. He is handling Java Full Stack Development, Python Full Stack Development, Devops with AWS, Dotnet Full Stack Development.
• List and Tuples
• Sets and Dictionaries
• Associated Methods
• If -elif -else
• Nested Conditions
• AND , OR Operations
• For and while loops
• Loop Counters
• Loops with conditions
• Nested loops
• Functions with parameters
• Functions with return
• Closures
• Recursive functions
• Object and Methods
• Polymorphism
• Abstract Methods
• Compositions
• Indexing/ Slicing
• Broadcasting
• Appending/ Inserting on Axis
• Mathematical and Statistical operations
• Sort/ Conditions
• Transpose operations
• Joining/ Splitting
• Linear Algebra
• Data Extraction
• Series/ DataFrame Creations
• Indexing and Slicing
• Conditions/ Grouping/ Imputation
• Append/ Concat/ Merge/ Join
• DateTime Functionalities and Resampling
• Window Functions
• Excel functions
• Customization of Matplotlib/ Seaborn
• Scatterplots/ Barplots/ Histograms/ Density Plots
• 3D plots
• Boxplotting and Outlier Detection
• Visualizing Linear Relationships
• Plotting with Pandas
• Probability
• Basics( Mutually Exclusive/ Joint Probability)
• Conditional Probability
• Dependent/ Independent
• Logs/ Odds
• Bayes Theorem
• Descriptive/ Inferential
• Mean, Median, Mode
• Variance/ Standard Deviation
• Co-variance/ Correlation (Pearson/ Spearman)
• PDF/ PMF/ CDF
• Uniform/ Normal/ Skewed Distributions
• Binomial/ Bernoulli Distribution
• Poisson/ Exponential Distributions
• Central Limit Theorem
• Null/ Alternative Hypothesis
• Z-test/ T-test/ Chi2-test
• p-value
• F-test/ Anova
• Scipy.Stats/ Statsmodels
• Difference Between AI, ML and DL
• Applications of Machine Learning
• Categorization of Machine Learning
• Supervised / Unsupervised / Semi Supervised
• Parametric vs Non Parametric
• Geometric/ Rule Based/ Gaussian
• Flow Operation (Pipelining)
• Sklearn Usage
• Null Values Imputation
• Outlier Detection
• Encoding
• Label Encoder
• Ordinal Encoding
• One Hot Encoding
• Scaling
• Binarizer
• MinMaxScaling
• Normalizer (L1 and L2)
• StandardScaler
• Imbalance Dataset
• Univariate/ Bivariate/ Multivariate Analysis
• Filter Methods
• Wrapper Methods
• Embedding
• Assumptions
• Introduction to Linear Regression
• Understanding the real meaning of Linear Regression
• Multiple Linear Regression
• Cost Function (Sum of Square Error)
• Loss Function
• Derivative of Loss
• Gradient Descent for Multiple Features
• Introduction to Polynomial Regression
• When to use Polynomial regression
• Evaluation based on RMSE/ R2
• Problems with Large Features
• Why penalty is inducted
• Difference between L1 and L2
• Cost Function
• Introduction to KNN algorithm
• KNN Classifier vs Regressor
• How to select the best K
• Logistic regression vs Linear Regression
• Log Odds / Logit / Sigmoid Function
• Optimization and Log Loss
• Maximum Likelihood Estimation
• Margin of SVM’s
• SVM optimization
• SVM for Data which is not linear separable
• Kernel Trick
• SVM Parameter Tuning
• Hinge Loss
• Introduction to Decision tree
• Decision Tree Classification / Regression
• Types of Decision Tree techniques (ID3 / CART)
• Pruning
• Conditional Probability and Bayes Theorem
• Naive Bayes
• Burnoulli / Multinomial and Gaussian Implementation
• Holdout Validation
• K-fold cross Validation
• Stratified Kfold
• Cross_val_score
• GridSearchCV
• RandomizedSearchCV
• MSE/ MAE
• R2/Adjusted R2
• Accuracy measurement
• Confusion Matrix
• Precision/ Sensitivity/ Specificity/ F1 Score
• AUC/ ROC
• AIC and BIC
• Voting/ Averaging
• Bagging / Boosting / Stacking
• Random Forest
• AdaBoost
• Gradient Boosting
• XGBoost
K Means
• Applications of Clustering
• Understanding Euclidean Distance
• Basics of Clustering
• Elbow Method and Silhouette score
Hierarchical Clustering
• Agglomerative
• Divisive
DBSCAN
• Reachability
• Connectivity
• epsilon and r
GMM
• Gaussian surface
• Relation with EM
• Difference between GM and other Clusters
• What is PCA?
• Understanding Matrix Transformations
• Eigen Values and Eigen Vectors
• tSNE and Umap
• Lag Values
• AutoRegression/ AutoCorrelation
• Stationarity
• Dicky Fuller Test
• Time Series Decomposition
• Modelling and Forecasting
• ARIMA/ SARIMA
• Support, Confidence, Lift
• Jacard Matrix
• Cosine Similarity
• Perceptron and relate it with Logistic Regression
• Multiple layer Neural network
• Similiarities and Differences with Baisc ML
• Forward Propogation
• Back Propagation Algorithm
• Vanishing Gradient and Exploding Gradient
• Non Linearity
• Sigmoid / Tanh Function
• Relu /Leaky Relu /Gelu
• Softmax Function
• Gradient Descent
• Stochastic Gradient Descent
• Momentum
• AdaGrad
• RMSProp
• NAG
• Adam/ Nadam
• Tensors
• Session, Placeholders and Variables
• Hands on with Tensorflow
• Sequential vs Functional
• Model Creation
• Difference between Tensorflow and Pytorch
• Autograd
• Graphs
• Data Loaders
• Feed Forward Networks
• Fully Connected Networks
• Recurrent Neural Networks
• Convolutional Networks
• Convolution/ Filters/ Pooling
• Back Propogation in CNN
• Image Recognition vs Object Localization
• Types Of CNN
• FastRCNN, YOLO
• LeNet/ Alexnet
• VGG 16/ 19
• ResNet
• MobileNet
• ImageNet
• Need of Transfer Learning
• Freezing of Layers
• Reusing of Structure
• Encoder vs Decoder
• Difference with PCA
• KL Divergence
• Variable Auto Encoders
• Generators
• Discriminator
• Structure
• Word Embedding
• Frequency vs Prediction based embedding
• Count Vector/ TFIDF/ Co Occurrence
• Bag of Words / Skip Gram
• Word2Vec /GloVe
• Classical RNN
• LSTM/GRU
• Vanishing Gradient
• Exploding Gradient
• Bidirectional RNN
• Encoder and Decoder
• Encoder only Transformers
• Decoder only Transformers
• Prompt Engineering
• Chain of Thought (CoT)
• Components of Langchain
• Open AI and Hugging Face
• How to query database
• Multi Instruction Fine Tuning
• Parameter Efficient (PEFT)
• LoRA
• Quantization
• QLoRA
• Difference between Retrieval and Augmentation
• Limitations of RAG
• ReAct
• Knowledge Graphs
• Vector Database
• PAL model
• MDP
• Value Functions
• State Values vs Action Value (Q)
• Exploitation vs Exploration
• TD Learning (Q Learning vs Sarsa)
• Hands on RL
• Tensorflow Agents Library
• OpenAI Gym environment
• Deep Q Networks
• Policy Gradient Methods
• Actor-Critic Architectures
• Gradient Based Techniques
• Proximal Policy Optimization (PPO)
• RLHF
Learn data science basics like machine learning and data analysis.
Work on real projects to practice what you learn.
Get help and guidance from experienced instructors.
Build skills that prepare you for a job in data science.
