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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

The Ascend Pro Program is Back! Analytics Vidhya and KPMG’s Popular Data Science Program

Abhiraj Suresh
Abhiraj Suresh Last Updated : 12 Jul, 2021
3 min read

ascendpro

Ascend Pro – Time to Get Industry Ready for Data Science!

Are you looking to land your dream role in data science?

Do you feel you’ve put in the hours but can’t quite clear that dreaded interview round?

You’re at the right place (and the right time!).

Machine Learning Market is growing at a CAGR of 44%, with around 99,000 job listings that post machine learning as a required skill.

That’s a mind-blowing number. We are literally living in the era of data and witnessing how harnessing the power of data can transform your organizations. With close to $100 billion dollars being invested into AI, it is clear that a career as a data scientist is meant to stay for long.

But we have constantly heard people not getting their dream data science job and the reason is the gap between the skill you possess and the skill that the industry requires.

And that gap is precisely what the Ascend Pro Program helps you bridge.

Note – Ascend Pro is a 7 month/30-week program and the next batch goes live on 14th August.

 

Key Features of Ascend Pro

Ascend Pro is a program created by Analytics Vidhya in collaboration with KPMG. Yes!!! You read it right.

The multi-billion company, through its intense research, realized the need and dearth of data scientists and decided to build a program focussing on the right amount of theory, practice, and industry insight.

The features of 30 weeks Ascend Pro include-

  • 160+ Hours of instructor-driven live sessions
  • Industry Vetted Curriculum
  • 8+ Real-World Projects on Industry data sets
  • Hands-on learning with HackConf
  • 100% Placement Assistance
  • Certified by: KPMG in India & Analytics Vidhya

In the coming sections, let’s explore more about these features.

 

Who is Ascend Pro for?

With passing time and unlimited access to information, anyone can become anything they want. With that thought, this program in collaboration with KPMG is created for anyone interested in data science. The following encapsulates the people who can pursue this program-

  • Anyone who wants to be a Master Data Scientist
  • Working Professionals who want to transition to a data science career
  • Students aspiring to be industry ready for data science and machine learning jobs

 

What will you learn?

Ascend Pro was made and launched keeping in mind the current market requirement and dynamics. The following roadmap captures an overview of what will be covered to make you a full-stack, industry-ready data scientist,

Ascend Pro Roadmap

And your learning experience does not stop there. Not only is the above-mentioned topic plainly taught by industry experts, the program includes-

  • 4 HackConfs – This is where the industry experts come to address the batch on the current developments how data is being leveraged in the market
  • 4 Hackathons – Competitions where you solve the given problem using the dataset provided to you
  • 8+ projects – We will be covering 8+ projects including Food Forecasting analysis, Customer Churn Prediction, etc
  • 14+ tools – the tools covered in this course include python, pandas, NumPy, MongoDB, AWS, etc.

You can download the detailed curriculum here.

 

12 Full-day of HackConfs

Many of you would be wondering by now, what exactly is this HackConf. Well, it is a 3-days live interactive session by experts to build an industry perspective.

Throughout the program, we will be organizing 4 such HackConfs. Some of the things we do in HackConf includes-

  • Hands-On Workshops –  4- Intensive workshops to build your real-world hands-on skills.

  • Full-Day Hackathons – Work on real-world datasets with exciting hackathon problems

  • Assessments – Evaluate your week-long learnings through assessments.

     

100% Placement Assistance

As mentioned above, the Ascend pro will enrich you with knowledge and industry experience thus making you the perfect fit for any role in Data Science. Bridging the gap between education and what employers want – the ultimate jackpot Analytics Vidhya and KPMG is providing!

The placement assistance program comprises of-

  • Sessions for Resume Building & Interview Preparation
  • Personalized Mock Interviews
  • Career Support with Job Portal Primary Access
  • Get exciting hiring opportunities with Top Companies including KPMG and KPMG Global services*

To excite you more, here is a list of companies that participates regularly in our hiring drives-

Ascend pro hiring

 

So what are you waiting for? Go on, register, and achieve your dream data science career.

Abhiraj Suresh
Abhiraj Suresh

My name is Abhiraj. I am currently a manager for the Instruction Design team at Analytics Vidhya. My interests include badminton, voracious reading, and meeting new people. On a daily basis I love learning new things and spreading my knowledge.

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