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Raghu is a B. E. in Computer Science and Engineering and has teaching experience of over 4.5 years. His expertise comprises Full Stack Java Development, Cyber security, A.W.S., Networking, Python Development and many more. He also possesses various certifications, including JAVA HackerRank, Oracle Certificatified Java Professional-8, Network Security Professional, and Azure - Az900.
With a year-long experience in I.T. and two years of experience training candidates for JAVA and related frameworks, HTML5, CSS3, Bootstrap, Javascript, MySQL, Manohar Yadav N is a B.E.B.E. (E.C.E.). He is highly passionate and devoted to Teaching and has trained up to 1600+ students.
With a year-long experience in I.T. and two years of experience training candidates for JAVA and related frameworks, HTML5, CSS3, Bootstrap, Javascript, MySQL, Manohar Yadav N is a B.E.B.E. (E.C.E.). He is highly passionate and devoted to Teaching and has trained up to 1600+ students.
• 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 cloud computing, storage, networking, and security from basics to advanced.
Work on real-world scenarios to gain practical experience in AWS services.
Get trained by industry professionals with in-depth cloud expertise.
Prepare for AWS certification and boost your career in cloud computing
AI (Artificial Intelligence) is the broader concept of machines being able to carry out tasks smartly, while ML (Machine Learning) is a subset of AI focused on systems that learn from data.
Basic programming knowledge (especially in Python) is helpful, but many beginner-friendly courses also teach coding along the way.
You’ll learn about algorithms, data handling, model training, neural networks, and real-world AI applications.
It takes around 4 months
You can pursue roles like Data Scientist, Machine Learning Engineer, AI Developer, or Research Analyst across various industries.
