- Assignments based learnings for Hands-on experience and Confidence build up.
- Assignments has nicely curated questions to cover all the important topics of Python, Machine Learning, SQL, Statistics and AWS SageMaker Deployments.
- Soft Deadlines and Reminders to encourage you to complete Assignments Consistently.
- 10+ Data Science projects using Kaggle Datasets that you can put in your Resume.
- Mentor Assigned to each learner to resolve all Assignment related queries.
- Content(YouTube videos and blogs) collection in structured way to learn all the concepts.
- Lifetime access to Assignments and Content collections.
- Regular Quizzes to test your Concepts.
- Course Completion Certificate.
- Get Interview Questions.
How CloudyML is different
from anyother online learning platform ?
- Here you will get step by step guidance from our mentors.
- You will get constant feedback on your assignments which will help you to improve.
- In CloudyML, you will get gentle reminders about your next assignments. We will keep you motivated and Josh “High” !!!
For Whom this course is
- College Students planning to learn Data Science from scratch.
- IT freshers planning to get into Data Science projects
- IT experienced person planning to switch to Data Science profile
No prior coding experience required.
A self-paced AI course with lifetime access.
Total Course Fee
Complete Course Detail
|1||Python Fundamentals||Variables in python, Numeric operators in python, Logical Operators, Loops (If, while and for) , Functions in python, lntoduction to string operations, Introduction to list, list comprehension and reference videos on every topic covered.|
|2||Data Structures||Lists, List comprehensions, Sets, Tuples, Dictionaries, and YouTube reference videos on every topic covered.|
|3||Numpy||Introduction to Numpy and its operations like creating numpy array, array indexing, array slicing, Numpy copy vs view, array shape, reshape, iterating, hstack and vstack. Youtube reference link on each topic.|
|4||Pandas||Introduction to pandas, series, dataframes, Indexes, loc and iloc, reading csv, merging, groupby, apply function and reference videos for all the topics.|
|5||Data cleaning||Treating Missing values, by dropping nan, imputing nan using ffill, bfill, mean, constant, interpolate, knn. Dropping irrelevant columns, dataframe shallow and deep copy, iterrows and itertuples, renaming, treating duplicate values, Treating constant column values. Implimenting Regex. Youtube reference link on each topic.|
|6||Exploratory Data Analysis||Data cleaning, basic statistical information of data, Detecting outliers, outlier removal techniques ( IQR, and Z-score ). Univariate plots (box plot, Histogram and density plots, Bar plots, Count Plots). Bivariate plots (Scatter plots, Line plots, box plots w.r.t third variable, joint distribution plots). Multivariate plots (Pairplot, multivariate scatter plot, parallel coordinates, Heatmaps). Youtube reference link on each topic..|
|7||Feature selection||Feature selection using Intrinsic (Tree based feature selection), wrapper method (RFE, SelectKbest) and filter methods (feature importance, pearson correlation) univariate ROC_AUC test and removal of multicollinearity. Youtube reference link on each topic.|
|8||Feature Engineering||Imputation, Handling outliers, Binning, One-hot encoding, Scaling, Date and Time engineering, Feature creation (sum, subtraction, mean etc),Variable Tranformation (Log, recirocal, expomential etc). Youtube reference videos links.|
|9||Simple Linear Regression||Introduction to linear regression, some fun with math behind LR, LR case study, Train and test split, Residual square error, R square. and Youtube reference link on each topic.|
|10||Multiple Linear Regression||Intoduction to Multiple Linear regression, Implementing Multiple LR and model building, model evaluation,visualizing error term and a case study. Youtube reference link on each topic.|
|11||Advanced regression||Regularization, Ridge algorithm, Lasso algorithm, ElasticNet algo, GridsearchCV, Bias Variance Tradeoff, R2, adjusted R square, mae, rmse, Model selection and a Case study. Youtube reference link on each topic.|
|12||Logistic Regression||Implementing Logistic Regression through case study. Sigmoid Function, Log odds, Model Performance, Model selection. Youtube reference links on each topic.|
|13||Dimension Reduction using PCA||PCA introduction, Math behind PCA , Standardization, covariance matrix computation, computing eigen vectors and eigen values, Feature vectors , Principal component based data creation. PCA Case studies. Youtube reference videos.|
|14||k-nearest neighbor algorithm||Introduction to KNN, Effectiveness of KNN, Distance Metrics, finding K, KNN on regression, KNN on classification. Case study to implement KNN. Youtube reference link on each topic.|
|15||Decision Tree||Introduction to decision Tree, Homogenity, Entropy, Gini Index, Information gain, Preventing overfitting Issues in DT, Decision Tree case study. Youtube reference videos.|
|16||Naive Bayes||Introduction to Naive Bayes, Bayes Theorem, Practical example from NB with one column, Practical example from NB with Multiple column, Naive Bayes on text data, Laplace Smoothing, Bernoulli Naive Bayes, Case study. Youtube reference link on each topic.|
|17||Ensemble ML Algorithm-Bagging||Weak learners, Bias variance tradeoff, Bagging meta-estimator, Random Forest, Bootstrap method, bootstrap aggregation, Estimated Performance, Variable Importance. Youtube reference links.|
|18||Ensemble ML Algorithm-Boosting||Introduction to boosting algorithm, Types of boosting algorithms ( AdaBoost (Adaptive Boosting), Gradient Tree Boosting, XGBoost), parameter n_estimators, learning_rate, max_depth. Youtube reference video links.|
|19||Clustering||Introduction to clustering, Segmentation, Kmeans, Maths Behind Kmeans, Kmeans plus, Value of K, Hopkins test, Hierarchial Clustering, Case study. Youtube reference videos.|
|20||Support Vector Machine||Introduction to SVM, Hyperplane, Maths Behind SVM, Support Vectors, Parameters in SVM (C, gamma, kernel- linear, rbf, polynomial), Slack Variable, Kernel, Case study. Youtube reference videos.|
|21||Basic Statistics||Inferential statistics, Probability Theory, Probability Distribution, Binomial Distribution, Commulative Distribution, Normal Distribution, z score, Sampling, Sampling Distribution, Central Limit Theorem, Confidence Interval. YouTube reference videos links.|
|22||Cloud||Introduction to AWS Sagemaker. Model Deployments and Exposing it as an REST API using Lambda functions and API Gateway.|
|23||SQL||SQL Introduction, Syntax, Select, Select Distinct, Where, And or Not, Order by, Insert Into, Null values, Update, Delete, Select Top, Min, Max, Count, Avg, Sum, Like, Wildcards, In between, SQL Aliases, Joins, Inner Join, left Join, Right Join, Full join, Self join, Union, Group By, Having, Exists, Any All, Select Into, Insert into select, Case, Null functions, Stored procedures, Comments. Operators. Youtube reference link on each topic.|
|24||Hypothesis Testing||Introduction to Hypothesis Testing, NULL And Alternate Hypothesis, One/Two Tailed Tests, Critical Value Method, z Table, Implementing practical hypothesis example, P value, Types of Error, t-distribution. Youtube reference videos for all topics covered|
|25||Gradient Descent||Introduction to Gradient Descent, Defining Cost Functions, Implimenting gradient Descent, Optimization, Closed Form Vs Gradient Descent, Case Study on Gradient Descent. Youtube reference link on each topic.|
|26||Final Assignment – Kaggle||Final Assignment that uses all the concepts that we have learnt till now.|
Is there any pre-reuisites required ?
There are no pre-requisites as such. You can join even if you don’t have any prior coding experience.
What People are saying about us on social media
CloudyML Course Completion Certificate
Hello, I'm Akash.
I'm a Data Scientist.
I have transitioned my career from Manual Tester to Data Scientist by upskilling myself on my own from various online resources and doing lots of Hands-on practice. For internal switch I sent around 150 mails to different project managers, interviewed in 20 and got selected in 10 projects.
When it came to changing company I put papers with NO offers in hand. And in the notice period I struggled to get a job. First 2 months were very difficult but in the last month things started changing miraculously.
I attended 40+ interviews in span of 3 months with the help of Naukri and LinkedIn profile Optimizations and got offer by 8 companies.
Now I want to use my experience to help people upskill themselves and land their dream Data Science jobs!