What you'll learn
This course offers a comprehensive learning experience, guiding beginners through the entire spectrum from foundational basics to advanced techniques in Data Science.
Programming Foundations
Develop strong programming skills using Python, essential for implementing algorithms and conducting data analysis. Gain proficiency in Python by incorporating crucial concepts of object-oriented programming (OOP) to enhance code structure and modularity.
Data handling, manipulation and visualization
Learn to efficiently handle and manipulate data using libraries like NumPy and Pandas, ensuring clean and structured datasets for analysis. Additionally, learn data visualization, using tools like Matplotlib and Seaborn to transform complex datasets into compelling visual insights.
Probability and Statistics
Understand statistical inference methods for drawing meaningful conclusions from data, supporting informed decision-making.
Exploratory Data Analysis (EDA)
Learn EDA techniques to uncover patterns, trends, and outliers in datasets, providing a solid foundation for decision-making.
Linear Algebra
Learn and master essential linear algebra concepts, a cornerstone for understanding and implementing various algorithms in machine learning.
ML Supervised Learning
Understand and implement regression and classification algorithms and explore all sophisticated ML models in detail. You'll also learn optimization (along with basic differential Calculus), model evaluation, hyperparameter tuning, feature selection, dimensionality reduction and application to real-world scenarios, shaping you into a proficient problem solver in predictive analytics.
ML Un-supervised Learning & Recommender Systems
Explore various clustering algorithms and anomaly detection. Also learn different techniques for creating Recommender Systems.
Natural Language Processing (NLP)
You'll learn from basics of text processing to diving into advanced techniques like sentiment analysis, text classification and Vector word embeddings.
Applied Deep Learning
Learn from fundamentals of deep learning architectures to navigating through convolutional neural networks (CNNs) for computer vision and recurrent neural networks (RNNs) for sequential data. You'll also solve real-world problems using libraries like TensorFlow and Keras.
Course Curriculum
To make sure this course aligns well with your learning plan, you can start learning for free right now by checking out the lessons that are marked free for preview.
Note: Please change the resolution to 1080p by clicking on the gear icon for better video quality.
Python Programming
Available in
days
days
after you enroll
- Resources
- Why Python for DS?
- Python Installation and Setup - Pre-requisites
- Miniconda Installation
- Module, Script, Package and Library
- Virtual Environments
- Python, IPython Shell
- Introduction to Jupyter
- Keywords and Identifiers
- Variables and Data Types
- Python Standard IO
- Executing Python Script using Terminal
- Operators
- Strings
- Strings - Indexing, Formatting
- Control Flow - If else
- Control Flow - While loop, Break and Continue
- Control Flow - for loop
- Lists
- Lists - Indexing and Slicing
- Tuples
- Sets
- Dictionary
- Dictionary and List Comprehensions
- Introduction to Functions
- Positional and Keyword arguments
- *args, **kwargs
- Lambda Functions
- Higher Order Functions
- Namespaces, Scope
- Enclosing Scope and LEGB Rule
- Decorators
- Intro. to Object Oriented Programming
- Classes and Objects
- Methods
- Inheritance
- Exception Handling
- Reading and Writing .txt files
- Reading and Writing .csv files
SQL
Available in
days
days
after you enroll
- Resources
- Introduction to Databases and SQL
- Installing MySQL
- DDL - CREATE, ALTER, DROP
- SQL DataTypes
- Setting up the data
- Data Retrieval - SELECT, WHERE, COUNT, DISTINCT, LIKE
- Data Retrieval - ORDER BY, LIMIT, OFFSET
- Get Summary Info. using GROUP BY
- GROUP BY - HAVING Clause
- Creating additional Columns
- Intro. to Joins
- JOINS - INNER, LEFT, RIGHT
- JOINS - OUTER
- Sub Queries, Correlated Sub Queries
- Window Functions - RANK, ROW_NUM, NTILE, LEAD, LAG
- DML - INSERT, DELETE, UPDATE
- DCL - GRANT, REVOKE
- Python - Reading the data from MySQL Table
- Python - Writing data to MySQL Table
- Python - Inserting Multiple records at once to MySQL Table
Python for Data Science : NumPy
Available in
days
days
after you enroll
Python for Data Science : Pandas
Available in
days
days
after you enroll
- Resources
- Introduction to Pandas
- DataFrames
- Series
- DataFrames - Indexing
- DataFrame - loc and iloc Operators
- DataFrame - Comparsion and Filtering
- DataFrame - Insert, Concatenate and Delete
- DataFrame - Merging
- DataFrame - apply, applymap and map
- Groupby
- Sorting and Ranking
- Reading and Writing Data Files
Data Visualization : Matplotlib and Seaborn
Available in
days
days
after you enroll
- Resources
- Introduction to Data Visualization
- Understanding Matplotlib Object Hierarchy
- Adding Color, Line Style, Markers
- Labels, Ticks and Legends to Plots
- Line Plots
- Histogram
- Bar Plots
- Stacked Bar Chart & Grouped Bar Chart
- Scatter Plots
- Box and Violin Plots
- Seaborn - Distribution plot, Kernel Density Estimate (KDE)
- Seaborn - Relational Plot, Joint Plot
Probability and Statistics
Available in
days
days
after you enroll
- Resources
- Intro. to Statistics
- Types of Data, Sample and Population
- Estimates of Location
- Estimates of Location - Coding
- Estimates of Variability
- Coefficient of Variation
- Descriptive Statistics - Coding
- Intro. to Probability, Random Experiment & Random Variable
- Calculating Probability
- Conditional Probability
- Bayes Theorem
- Bayes Theorem Problem
- Discrete RV, Probability Mass Function (PMF)
- Bernoulli Distribution
- Bernoulli Distribution and PMF using Python
- Binomial Distribution
- Geometric, Hyper Geometric Distribution
- Continuous RV, Probability Density Function (PDF)
- Cumulative Distribution Function (CDF)
- Gaussian Distribution
- Standard Normal Distribution, Z Score
- Normal Distribution - Coding
- Normal approximation to Binomial
- Log Normal Distribution
- Law of Large Numbers
- Central Limit Theorem (CLT)
- Verifying CLT
- Intro. to Confidence Intervals
- Confidence Intervals : Margin of Error
- Confidence Intervals : t-Distribution
- Hypothesis Testing
- Z Test
- One sample and Two sample t-test
- t-test - Implementation
- Paired t-test
- Chi Square Test
- Chi Square Test - Implementation
- Covariance
- Pearson Correlation
- Spearman Rank Correlation
- Kendall's Tau Correlation
Exploratory Data Analysis
Available in
days
days
after you enroll
- Resources
- Machine Learning Life Cycle
- Predictive Modeling Steps
- EDA Steps
- Variable Types
- Variable Identification
- Categorical Encoding - Label, Ordinal Encoding
- Categorical Encoding - One Hot Encoding
- Categorical Encoding - Frequency Encoding
- Missing Value Identification
- Univariate Analysis - Descriptive Statistics
- Univariate Analysis - Data Profiling
- What are Outliers?
- Impact of Outliers - Why are they bad?
- Identifying Outliers - Box Plot approach
- Identifying Outliers - Z Score method
- Identifying Outliers - Modified Z Score method
- Outlier Treatment - Ways to handle Outliers
- Multivariate Outlier Identification
- Multivariate Outlier Identification - Implementation
- Need for Scaling
- Standardization and Normalization of data
- Intro. to Bivariate Analysis
- Continuous-Continuous
- Categorical-Categorical : Hypothesis Testing
- Categorical-Categorical : Visualizations
- Categorical-Continuous
- Quantile-Quantile Plot (QQ Plot)
- Kolmogorov Smirnov Test (KS Test)
Linear Algebra
Available in
days
days
after you enroll
- Resources
- Introduction to Linear Algebra
- Vector Operations
- Vector Dot Product
- Projection of a Vector
- Basis, Span and Linear Dependence
- System of Linear equations
- Solving System of Linear equations
- Types of Matrices
- Linear Transformations
- Eigen Vectors, Eigen Values
- Eigen Decomposition
- Deriving Eigen Vectors and Values using Python
K Nearest Neighbors
Available in
days
days
after you enroll
- Resources
- KNN - Intuition
- Failure cases of KNN
- Distance Measures - Euclidean, Manhattan, Minkowski
- Distance Measures - Hamming distance
- Cosine Similarity and Cosine Distance
- KNN Implementation
- KNN - Breaking a tie
- Decision Regions
- Overfitting versus Underfitting
- KNN - Choosing an Optimal value for K
- Need for Cross Validation
- Holdout Validation, Stratified Holdout Validation
- Stratified Holdout Validation - Data Partitioning
- K-Fold Cross Validation, LOOCV
- K-Fold Cross Validation Implementation
- KNN for Regression Problems
- Weighted KNN
- Curse of Dimensionality
- Bias Variance Tradeoff
Performance Measurement of Classification Models
Available in
days
days
after you enroll
Linear Regression
Available in
days
days
after you enroll
- Resources
- Intro. to Linear Regression
- Baseline model and SSE
- Least Squares Method
- Evaluation Metrics for Regression model
- Linear Regression Output Interpretation
- Multiple Linear Regression and Problems
- Assumptions of Linear Regression
- Linear Regression Implementation - Problem and Data Pre-processing
- Linear Regression Implementation - Linearity, Multi-Collinearity
- Linear Regression Implementation - Data Transformations
- Linear Regression Implementation - Creating model
- Linear Regression Implementation - Model Evaluation
Solving Optimization Problems
Available in
days
days
after you enroll
Constrained Optimization, Ridge and Lasso Regression
Available in
days
days
after you enroll
Logistic Regression
Available in
days
days
after you enroll
- Resources
- Line, Plane and Hyper Plane
- Distance of a point from Plane
- Logistic Regression - Geometric Intuition
- Sigmoid Function
- Mathematical Formulation of Objective Function
- Objective Function using MLE
- Regularization - Logistic Regression
- Parameters and Hyper-Parameters
- Grid Search CV and Random Search CV
- Why and when Feature Scaling is required
- Implementation - Data Pre-processing
- Implementation - Missing value Imputation
- Implementation - Categorical Feature Encoding
- Implementation - Model Building
Support Vector Machines (SVM)
Available in
days
days
after you enroll
- Resources
- SVM - Geometric Intuition
- Mathematical Formulation of Objective Function
- Hard Margin SVM
- SVM Soft Margin
- Hinge Loss
- SVM Dual Formulation
- Kernel Trick
- RBF Kernel
- SVM Kernels, Decision Boundaries
- SVM Decision Boundaries with change in hyper parameters
- SVM Implementation - Grid Search CV
- SVM Implementation - Random Search CV
Principal Component Analysis (PCA)
Available in
days
days
after you enroll
Decision Trees
Available in
days
days
after you enroll
Ensemble Models
Available in
days
days
after you enroll
- Resources
- Intro. to Ensemble Learning
- Basic Ensemble Implementation for Classification problem
- Basic Ensemble Implementation for Regression problem
- Why Ensemble models work well?
- Bias Variance Trade-off (Revisited)
- Generalization Error, Bias Variance Decomposition
- Bootstrap Aggregation (Bagging) Intuition
- Intro. to Random Forest
- Random Forest model - Hyper Parameter Tuning
- Random Forest Implementation
- Extremely Randomized Trees
- Boosting Intuition
- Pseudo Residual Loss
- Gradient Boosting
- Gradient Boosting - Regularization by Shrinkage
- GBM Implementation
- Intro. to XGBoost
- XGBoost Implementation
- XGBoost CV Implementation
- AdaBoost Math
- AdaBoost In-depth
- AdaBoost Implementation
- Model Stacking
- Model Stacking Implementation
Project : Microsoft Malware Detection
Available in
days
days
after you enroll
- Resources
- Problem Description
- Dataset Overview
- EDA - Data Pre-processing Part I
- EDA - Data Pre-processing Part II
- EDA - Data Profiling
- EDA - Hypothesis Testing
- Feature Engineering
- Feature Encoding Approaches
- Applying Feature Encoding
- Data Pre-processing for ML Model building
- Creating a Decision Tree Model
- Decision Tree - Model Evaluation
- Creating a Random Forest Model
- Creating XGBoost Model
- Model Performance Assessment - Cumulative Gains Chart
Project : Vesta Fraud Detection
Available in
days
days
after you enroll
- Resources
- Problem Description
- Data Overview
- EDA - Data Pre-processing
- EDA - Data Profiling
- EDA - Hypothesis Testing
- Feature Engineering
- Feature Encoding Approaches
- Categorical Feature Encoding
- Feature Selection Methods, Data Partitioning
- Creating Random Forest Model
- Cost Sensitive Learning using Class Weights
- Feature Selection - Recursive Feature Elimination
- Random Forest - Model Evaluation, AUC ROC Curve, Precision Recall Curve
- Creating XGBoost Model
- Finding Optimal Threshold, Model Evaluation
- Cumulative Gain Curve
- Creating Light GBM Model
Fundamentals of Natural Language Processing (NLP)
Available in
days
days
after you enroll
- Resources
- Intro. to NLP
- Project : Customer Review Classification, Data Overview
- Data Cleaning
- Text to Vector conversion
- Bag of Words (BoW)
- Terminologies
- Text Pre-processing : Stop Word Removal, Stemming, Lemmatization
- Text Pre-processing : Code Example
- Text Pre-processing : applying to review data
- Creating Bag of Words
- uni-gram, bi-gram and tri-gram
- Creating uni-gram, bi-gram and tri-gram's
- Term Frequency (TF) and Inverse Document Frequency (IDF)
- Why log in IDF?
- Creating TF-IDF matrix
- Model Building : Classification of Reviews
- Word Embeddings : Word2Vec
- Average Word2Vec, TFIDF Weighted Word2Vec
- Creating Word2Vec
Unsupervised Learning
Available in
days
days
after you enroll
K-Means Clustering
Available in
days
days
after you enroll
Hierarchical Clustering
Available in
days
days
after you enroll
DBSCAN (Density based clustering)
Available in
days
days
after you enroll
Recommender Systems and Matrix Factorization
Available in
days
days
after you enroll
- Resources
- Recommender Systems
- Types of Recommender Systems
- User based Collaborative Filtering
- Challenges in User based Collaborative Filtering
- Item based Collaborative Filtering
- Matrix Factorization : SVD
- SVD, Eigen Decomposition Relevance
- Matrix Factorization for Collaborative Filtering
- Creating Recommender Systems : Non Personalized
- Creating Recommender Systems : User based Collaborative Filtering
- Creating Recommender Systems : Item based Collaborative Filtering
- Creating Recommender Systems : Matrix Factorization
Deep Learning : Fundamentals
Available in
days
days
after you enroll
- Resources
- Artificial Intelligence vs Machine Learning vs Deep Learning
- What is Deep Learning?
- Factors behind Deep Learning popularity
- Intro. to Google Colab
- Perceptron
- Multi Layer Perceptron
- Visualizing Neural Network
- Neural Network : Notation
- Training Single Neuron Model
- Training MLP
- Backward Propagation
- Epoch versus Iteration
- Activation Functions : Linear activation
- Activations Functions : Sigmoid, Tanh
- Activation Functions : Softmax
- Vanishing Gradient Problem
- Activation Functions : ReLU, Leaky ReLU
Deep Learning : High Performing Neural Nets
Available in
days
days
after you enroll
Project : Image Classification using Tensorflow and Keras
Available in
days
days
after you enroll
Deep Learning : Convolutional Neural Nets
Available in
days
days
after you enroll
- Resources
- Why CNN?
- Convolution : Filters
- Convolution : Edge Detection using Sobel Kernel
- Convolution over RGB Images
- Local Connectivity and Parameter Sharing
- CNN Architecture
- Pooling
- CNN Forward Propagation
- CNN Back Propagation
- Creating CNN model
- Hyper parameter tuning
- Image augmentation
- Image augmentation implementation
- CNN Image augmentation using Keras
Deep Learning : LSTMs, RNNs
Available in
days
days
after you enroll
To clarify your doubts
Answers to some of your questions
Are there any prerequisites for this course?
All you need for this course is a computer (Windows/Mac/Linux), a grasp of basic school-level mathematics, and that's it! No prior coding experience required, and all the tools and software used in the course are completely free.
Who is this course for?
- If you are aiming to become a Data Scientist, ML/DL Practitioner, this course is for you.
- Any data professional who is looking to master data science.
Is it necessary to learn math and statistics?
It's crucial to grasp how mathematics underpins data algorithms. Without comprehending the inner workings, particularly the math-based aspects, progressing beyond a certain level can be challenging.
I hate math. Is there a workaround?
I've put in maximum effort to ensure the lectures are as intuitive as can be. Beyond teaching the concepts, I guide you through real-world Python examples to illustrate their practical applications.
Do you provide a certificate after completion?
Yes, we definitely do.
What is the Job Assistance program?
From the moment you enroll to securing a job, we guide you through the entire process and seamlessly transition you into the placement process upon successful completion of the course, including all assignments and projects.
Do I need to take notes?
Though we provide all relevant material and resources, we encourage you to take notes especially for all mathematical derivations that we do as part of course.
Why are you using Python and not R?
I haven't come across a more versatile and user-friendly language that effectively accomplishes the task at hand.
How long will I have access to the course?
You have access to the course for at least 1 year. You'll need to renew it after 1 year if you'll still need it.
Check your inbox to confirm your subscription