Python Programming, Django/Flask, RestfulAPIs, Statistics, Tree, Ensemble Model, Random Forest, Kmeans, Deep Learning, Confusion matrix, Recall, Precision, F1-Score, Micro F1-Score, Macro F1- Score, Accuracy, ROC-AUC, R-Squared, Adjusted R-Squared, Dimensionality Reduction, Hyperparameter Tuning, NLP, Case Study, Numpy, Pandas, SkLearn, NLTK, sPacy, Keras, Recursion, Dynamic Programming, Graph Theory, Every night coding contest, HackathonsClick Here To Apply
How this bootcamp works ?
Weekdays we will be having 3-4 Hrs of Algorithm classes & 3-4 Hrs of Development Classes. Every night coding contest on Hackerrank.
Also will be working on multiple assignments and problems given in the classes.
On weekends we will be having Hackathons, where you all will be implementing some product in a team of 3-4 members supprted by TAs and Mentors.
Stay, Food, Mentorship, Hackathon, Classes, Coding Contest all at same place which is included in the complete camp with a surprise trip !
Can code in C, C++.Enthusiastic to build Software Platforms and enquisitive enough to code it.
Someone who is not looking for the exact way of building the things but ready to learn through debugging, hit & trial, collaborative learning and hard working.
Python Programming (loops, functions, list, dictionaries, tupple, set, comprehension list, generators, iterators, lambda, map, reduce, zip, filter)
OOPs Python, Flask/Django, Restful APIs using Python & Flask/Django
Some array-based problems, recursion based problems.
Linear Algebra Concept – Scalars, Vectors, Matrices, Tensors, Determinant, Dot Product, Hyperplane
tatistics & Probability – PDF, CDF, Bayes’ Rule, Expectations, Variance, Covariance, Kernel Density Estimation, Kernel Density Classification, Central Limit Theorem, Chebyshev’s Inequality, Continuous Probability Distribution, and its Significance, Correlation, Confidence Interval, Hypothesis Testing
Calculus - Differentiation, Gradient Descent, Stochastic Gradient Descent
Numpy, Pandas & SkLearn
Basic Machine Learning – What is ML, History of ML, Types of ML, Challenges in ML, Essence of Data Analysis, Data Preparation for ML Model,
Machine Learning Models :
Supervised Machine Learning Algorithm :
a) Linear Regression – Assumption of Linear Regression, Geometric Intuition, Mathematical Formulation, Shrinkage Method (Subset Selection, Lasso, Ridge), Model Interpretability, Multiple Linear Regression, Principal Component Regression, Interaction Terms, Complexity, Code and Hyperparameter tuning
b) KNN- Geometric Intuition, Invariant Metrics and Tangent Distance, Euclidean Distance, Hamming Distance, Cosine Similarity, Overfitting and underfitting case, Complexity, Model Interpretability, Code and Hyperparameter tuning
c) Naïve Bayes – Probabilistic Intuition, Naïve Bayes Algorithm with proof, Workflow of Naive Bayes with Example, Zero Probability Problem, Overfitting and UnderFitting Case, Model Interpretability, Gaussian Naïve Bayes, Complexity, Code and Hyperparameter tuning
d) Logistic Regression – Geometric Intuition, Estimating Regression Coefficients, Loss Minimisation Interpretation, Mathematical Formulation, Regularisation, Multiple Logistic Regression, Making Sense of Result Parameters( Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test), Model Interpretability, Train and Run Time Complexity, Sklearn Code and Hyperparameter tuning.
e) Support Vector Machine – Geometric Intuition, Mathematical Formulation, Maximal Margin Classifier, Non-Separable Case, Function Estimation and Reproducing Kernels, Support Vector Concept, Overfitting and Underfitting case, complexity, Model Interpretability, Curse of Dimensionality, Code and Hyperparameter tuning
f) Decision Tree – Intuition, Entropy, Information Gain, Gini Impurity, Classification Tree, Regression Tree, Overfitting and UnderFitting Case, Complexity, Model Interpretability, Code and Hyperparameter tuning
g) Ensemble Model – Bagging, Boosting, Random Forest, Complexity, Model Interpretability
h) Random Forest - Definition, OOB Concept, Variable Importance, Proximity Plots, Overfitting, Analysis of Random Forests, Code and Hyperparameter tuning
Unsupervised Machine Learning Algorithm :
i) Cluster Analysis - Kmeans, K-means++, K-medoids, Hierarchical, DBSCAN
j) Association Rule - Market Basket Analysis, Generalized Association Rules, Measures of Association Rule Mining (Support, Confidence, Lift, Apriori algorithm)
Performance Measurements Techniques - Confusion matrix, Recall, Precision, F1-Score, Micro F1-Score, Macro F1- Score, Accuracy, ROC-AUC, R-Squared, Adjusted R-Squared
Dimensionality Reduction: Curse of Dimensionality, PCA, Intuition, Mathematical Formulation, EigenVector, and EigenValue Significance, code and Hyperparameter.
NLTK, sPacy, Keras
Deep Learning - Introduction to Deep Learning, History of Deep Learning, Multi-Layered Perceptron, Forward Propagation, Backward Propagation, Activation Functions, Vanishing Gradient Problem, Bias-Variance Tradeoff, Dropout, Weight Initialization, Batch Normalization, Optimization Technique
Natural Language Processing - Preprocessing the text data, BOW, TF-IDF, Word2Vec, N-Gram, Cosine Similarity, Jaccard Similarity, Prediction, Named Entity Recognition, Part of Speech, Hidden Markov Model, Conditional Random Field
Dynamic Programming and Graph based Algorithms
Week Long Hackathon.
Event Details & Cost
When is this bootcamp starting & how long is it?
Next Batch Starts From 7th Dec, 15th Dec And 4th Jan ( 28 days coding bootcamp )
Where will this bootcamp be conducted?
Your classes will be taking place in Bangalore.
What is the fees for the bootcamp?
30,000 + 18% GST - that includes cost for your food, stay, mentorship, hackathons, late night coding & classes.
Who will be attending this bootcamp along with me?
College Students & Software Developers who can come and stay for the complete Bootcamp.
What kind of mentors will be there in the bootcamp?
Passionate coders and Developers who are well-versed and hold expertise over specific domains. Above all, we choose mentors who are passionate towards teaching, helping and guiding our students through all stages of learning. As the bootcamp will proceed, you will be interacting with around 10 - 15 mentors from various top tech companies.
What will be the takeaways that students would have from the bootcamp?
After the course, you will be skilled at individual technologies taught in the course and also uniquely skilled at integrating all those technologies to build an enterprise solution. If you chose to appear for tech interviews, it will give you a great edge over the other candidates, as you will be knowing most skills mentioned in any job description and what’s more exceptional, all of your knowledge will be hands-on.
Will there be any placement assistance for job search?
Due to CodeAsylums strong alumni network, we can refer students in various top tech companies like Microsoft, Amazon, D.E. Shaw, Goldman, Oracle etc.
Python Programming, Django/Flask, RestfulAPIs, Statistics, Tree, Ensemble Model, Random Forest, Kmeans, Deep Learning, Confusion matrix, Recall, Precision, F1-Score, Micro F1-Score, Macro F1- Score, Accuracy, ROC-AUC, R-Squared, Adjusted R-Squared, Dimensionality Reduction, Hyperparameter Tuning, NLP, Case Study, Numpy, Pandas, SkLearn, NLTK, sPacy, Keras, Recursion, Dynamic Programming, Graph Theory, Every night coding contest, HackathonsAPPLY
Linux, Python and Networking, Text Manipulation tools, Process Monitoring, Socket Programming, Operating System, Distributed Systems, CAP Theorem, Replications, Partitioning, Sharding, Load Balancer, Reverse Proxy, Forward proxy, Caching Server, Web Server, Hadoop, HDFS, MapReduce, Zookeeper, Kafka, Cassandra, Redis, Ancible, Beam, Elastic Search, Jenkins, Logstash, kibana, Hystrix, Chukwa, Containers, Dockers, Kubernetes, AWS Services, System Design, HackathonsAPPLY