Build AI & Data Science Skills.
A Practical and Comprehensive Course to Transition into a Data Science Career
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Explore free vidoes before you take the next step
‘Simplified AI Labs’ Data Science course :
- Watch the free videos that are open for preview to get an understanding on how the complete course has been taught. You can also use the YouTube link provided below.
- 100% refund if you don’t align with the course after purchase. No Questions asked.
- 100+ students enrolled.
Everything you need to prepare for the field of AI/ML
From basic Python Programming to Deep Learning and Generative AI, we have got you covered.
Comprehensive Course Curriculum
- Learn from basics to advanced topics in a most intuitive and practical way.
- This course will help you navigate through the most difficult topics easily as it is built on two proper foundations : ‘Applied Math’ and ‘Programming’.
Personalized Learning Path for you
- The roadmap is curated by industry experts to align with your learning objectives.
- Regular mentorship sessions to understand your challenges and help solve them through out your learning journey.
1:1 Doubt Clarification and Job assistance
- You can Schedule 1:1 doubt-clarification sessions with us anytime.
- Regular mock interviews are conducted to assess your learning.
- Evaluate your current skillset and help you build a job-ready portfolio and assist you through-out your interview process.
Our students have gone on to work at some of the leading tech companies .
Curriculum that makes you job ready
At ‘Simplified AI Labs’, we have designed our data science course curriculum and learning pedagogy based on what top tech companies expect when you join them. The curriculum focuses on learning by doing including solving real world problems and working on real industry level projects.
# Python programing
- Getting Started with Python
- Virtual Environments
- Data Types in Python
- Indexing & Slicing
- In-built Functions and Methods
- Lists, Tuples, Dictionaries, Sets
- List and Dictionary Comprehensions
- Conditional Statements
- Control Flow : Loops & Iterations
- Conditional & Infinite Looping
- Advanced Looping Concepts
- Functional Programming in Python
- Custom functions in Python
- Higher order functions
- Lexical Scoping in Python
- Lambda & Map Functions
- Decorators
- Errors and Exception Handling
- OOPs in Python
- Inheritance,Encapsulation
- File I/O Operations
- Coding Best Practices
- Competitive Coding
# SQL
- Introduction to Databases and SQL.
- Installing MySQL.
- DDL – Create, Alter and Drop.
- SQL Data Types
- 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, ROWNUM, NTILE, LEAD, LAG.
- DML – Insert, Delete, Update.
- DCL – Grant, Revoke.
- Reading the data from MySQL table using Python.
- Writing data to MySQL table using Python.
- Inserting multiple records at once to the table using Python
# Python for data science
- Numerical Python (NumPy).
- Data wrangling using Pandas.
- Data Visualization using Matplotlib and Seaborn.
# Probability and statistics
- Types of data, sample and population.
- Estimates of location.
- Estimates of location – coding.
- Estimates of variability.
- Coefficient of variation.
- Descriptive statistics.
- Intro. to Probability, Random Experiment and Random variable.
- Calculating Probability.
- Conditional probability.
- Bayes Theorem and problems.
- Discrete Random variable.
- Probability Mass Function (PMF).
- Bernoulli Distribution.
- Bernoulli Distribution and PMF using Python.
- Binomial distribution.
- Geometric, Hyper
- Geometric distribution.
- Continuous random variable, 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.
- Confidence intervals.
- Margin of error, t-distribution.
- Hypothesis Testing.
- Z Test.
- One sample and two sample t-test.
- t-test implementation.
- Paired t-test.
- ANOVA test.
- Chi Square test.
- Covariance.
- Correlation – Pearson,
- Spearman and Kendall’s Tau
# Exploratary data analysis (EDA)
- 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)
# Foundational algebra : linear algebra
- 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
# Machine learning I : supervised learning and NLP
- Getting Started With ML.
- Implementing simple Supervised Algorithm.
- K Nearest Neighbors (K-NN).
Performance measures of Classification model. - Linear Regression.
- Performance measures of Regression model.
- Solving optimization problems.
- Constrained optimization – Ridge, Lasso and Elastic Net.
- Logistic regression.
- Support Vector Machines (SVM).
- Principal Component Analysis (PCA).
- Decision trees.
- Ensemble models.
- Random forest.
- Xgboost.
- Gradient bossting.
- Ada boost.
- Model Stacking and Blending.
- Case Study : Microsoft Malware Detection.
- Case Study : Vesta Fraud Detection.
- Foundations of Natural Language Processing (NLP).
- Case Study : Classification of Amazon reviews.
# Machine learning II : un-supervised learning and recommender systems
- Implementing simple Un-Supervised Algorithm.
- Clustering – KMeans, KMeans++, K-Medoids.
- Clustering – Hierarchical, DBSCAN.
- Recommender Systems and types.
- Matrix Factorization – SVD.
# Applied deep learning
- Fundamentals of Deep Learning.
- Intro. to Google Colab. Why use Colab?
- Perceptron, MLP.
- Visualizing Neural Network.
- Training a single neuron model.
- Training MLP.
- Back propagation.
- Activation Functions : Sigmoid, Tanh, ReLu, Leaky ReLu, Softmax
- Vanishing Gradient Descent.
- Performance of deep learning models.
- Weight Initialization.
- Batch Normalization.
- Model generalization – Dropouts.
- Variants of Gradient Descent.
- Optimizers : AdaGrad, Adam, RMSProp.
- Case Study : Image classification.
- Convolution Neural Nets.
- LSTM
- GRU
- RNN, Deep RNN
# Deployment and ML ops
- Ways of deploying ML or DL model.
- ML Ops overview.
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FAQ's
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.
