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Machine Learning, AI, Data Science Foundation Course

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Overview

If you want to become a Data Scientist, this is the place to begin! Introduction to Machine Learning, AI, Data Science (Python version) covers every stage of the Data Science Lifecycle, from working with raw datasets to building, evaluating and deploying Machine Learning (ML) and Artificial Intelligence (AI) models that create efficiencies for the organization and lead to previously undiscovered insights from your data.

It begins by teaching you how to use Python libraries, such as Pandas, Numpy and Seaborn. You’ll learn how to manage, transform and visualize data in every conceivable way, in order to unearth the real value in your current and historic data. You’ll then use Python libraries such as Scikit- Learn to understand how to build, evaluate and deploy many Machine Learning (ML) and Artificial Intelligence (AI) models that not only predict into the future but constantly learn from data as new events unfold.

By the end, you will be able to confidently apply many ML & AI techniques to both enhance your organization’s efficiencies and through predictive modelling, be prepared for future possibilities.

Who this course is for :

  • Beginners to AI, Machine Learning and Data Science
  • Students or Professionals
  • Testers, Database Developers
  • Managers
  • Java Developers
  • Full Stack Developers etc.

Why should you take this training?

  • Data Science is one of the most attractive jobs in the present data driven world. Glassdoor ranks data scientist among the top most jobs of the year 2020.
  • Data Science is the building block for artificial intelligence. As per IDC 80% of all applications will have an AI component by 2021. 
  • The global artificial intelligence market size was valued at USD 39 .9 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027. 
  • In the next four years it is expected that the AI industry’s good growth will start to explode and its impact on business and society will begin to emerge. 
  • There is a saying, “A jack of all trades and a master of none.”When it comes to being a data scientist you need to be a bit like this, but perhaps a better saying would be, “A jack of all trades and a master of some.

Machine Learning, AI, Data Science Foundation Course

Welcome To The  Course

  • Introduction To Machine Learning, AI,
  • Data Science
  • Real Time UseCases
  • Github Tutorial
  • Skillsets needed
  • 6 Steps to take in 3 Months for a complete transformation to DataScience from any other domain
  • Machine Learning-Giving Computers

the ability to learn from data

  • Supervised vs Unsupervised
  • DeepLearning vs Machine Learning
  • Link to get Free Data to Practice?
  • Some Great self Learning Resources

(Books,Tutorials,Videos,Papers)

 

Python Fundamentals

 

  • Software Installation
  • Introduction To Python
  • “Hello Python Program” in IDLE
  • Jupyter Notebook Tutorial
  • Spyder Tutorial
  • Introduction to Python
  • Variable,Operators,DataTypes
  • If Else,For and While Loops
  • Functions
  • Lambda Expression
  • Filter, Map,Reduce
  • Taking input from keyboard
  • HANDS ON-

INTERVIEW QUESTION DISCUSSION

NumPy

  • Create Arrays
  • Array Item Selection and Indexing
  • Array Mathematics
  • Array Operation
  • HANDS ON

 

Pandas

  • Introduction to Pandas
  • Series
  • Series indexing and Selection
  • Series Operation
  • Introduction to Pandas
  • Data Frames
  • Data Collection from csv,json,html,excel
  • Data Merging,Concatenation,join
  • Group By and Aggregate Function
  • Order By
  • Missing Value Treatment
  • Outlier Detection and Removal
  • Pandas builtin Data Visualisation
  • HANDS ON

INTERVIEW QUESTION DISCUSSION

Visualisation-matplotlib,seaborn

 

  • Line Plots
  • Scatter Plots
  • Pair Plots
  • Histograms
  • Heat Maps
  • Bar Plots
  • Count Plots

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  • Factor Plots
  • Box Plots
  • Violin Plots
  • Swarm Plots
  • Strip Plots
  • Pandas Builtin Visualisation Library
  • HANDS ON

INTERVIEW QUESTION DISCUSSION

  • Descriptive vs Inferential Statistics
  • Mean,Median,Mode,Variance,Std. dev
  • Central Limit Theorm
  • Co-Variance
  • Pearson’s Product Moment Correlation
  • R – Square
  • Adjusted R-Square
  • Spearman’s. Rank order Coefficient
  • Sample vs Population
  • Standardizing Data(Z-score)
  • Hypothesis Testing
  • Normal Distribution
  • Bias Variance Tradeoff
  • Skewness
  • P Value
  • Z-test vs T-test
  • The F distribution
  • The chi-Square test of Independence
  • Type-1 and Type-2 errors
  • Annova
  • HANDS ON
  • INTERVIEW QUESTION DISCUSSION
  • Introduction to Machine Leaning
  • Machine Learning Usecases
  • Supervised vs Unsupervised vs Semi-

Supervised

  • Machine Learning process Workflow
  • Training a model
  • Validating results
  • Overfitting vs Underfitting
  • Ordinal vs Nominal data
  • Structured vs unstructured vs semi-

structured data

  • Intro to scikitLearn
  • HANDS ON

Regression

 

  • Regression Vs Classification
  • Linear regression
  • Multivariate regression
  • Polynomial regression
  • Multi-Colinearity,
  • Auto correlation
  • Heteroscedascity
  • Hands On

 

Classification

  • KNN
  • Svm
  • Decision Tree
  • Random Forest
  • Performance tuning of Random Forest
  • Naive Bayse
  • Overfitting Vs Underfitting
  • Hands On

 

Model Validation

  • Classification Report
  • Confusion Report
  • ROC
  • RMSE
  • MSE
  • Cross validation
  • Hands On

Clustering and PCA

  • Kmeans
  • How to choose number of K in KMeans
  • Hands on
  • PCA
  • Hands on

Ensemble Methods

  • What is Ensembling
  • Types of Ensembling
  • Bagging
  • Boosting
  • Stacking
  • Random Forest
  • Important Feature Extraction
  • XGBoost
  • HANDS ON
  • Tokenizer
  • Stop Word Removal
  • Tf-idf
  • Document similarity
  • Word2vec Model
  • t-SNE visualisation
  • Sentiment Analysis
  • HANDS ON
  • Basic of Neural Network
  • Type of NN
  • Cost Function
  • Tensorflow Basics
  • Hands on Simple NN with Tensorflow
  • Image classification using CNN
  • HANDS ON

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