End to End Data Science [ML+DL+SQL] Training [Only Recorded Classes]

• Instructor: administrator
• Lectures: 47
• Duration: 24 weeks

Duration: – 4 Month

Classes: – 46 Classes

Candidate Benefits

1.Lifetime Interaction with candidate during the course and after the course . One Tutor will be assigned with Candidate to solve candidate Problem. Support will be 24*7.

2.Candidate will get Full AI Course at low price .

3.During this Course we will share updated code and content which candidate can use during their job , so candidate can fast adapt the industry work and will work confidently.

4.Certification after complete course successfully and will prepare student for interview which will help student to get job fast.

5.Study Material which cover from  basic to expert level concepts .

6.After Mid-completion of course, we will start to share Projects which will from basic to expert level and we will pay for few projects.

7.End to End Approach – Our programs have no pre-requisite, they start from basics and prepare you for industry by building your portfolio of industry relevant projects.

Prerequisites

This program requires no past knowledge about Data Science, Machine Learning, Artificial Intelligence, or any tool.

Comparison : –  

We are Claiming our course feature is better than Online and classroom training .

Key Takeaways from End to End AI Training _ Industry Version P1:

Data Science and Machine Learning

• Applied Machine Learning – Beginner to Professional: This course starts from basics of Python, Statistics and provides you all the tools and techniques you need to apply Data Science & Machine Learning to solve business problems. We will cover the basics of machine learning and how to build, improve and deploy your machine learning models.
• Projects using Machine Learning – This course provides an end to end case study to build prediction. Starting from the business problem, converting it into a data problem and applying machine learning to solve the problem – you see an end to end case study and project in Machine Learning.
• End to End Deep Learning– Starting from the basics of Neural Networks and Deep Learning – this course provides you with all the basics and advance level of Deep Learning, its various architectures and its applications to build Intelligence on images and text.
• AI & ML for Business Leaders –If you are a Business Leader and want to understand how Machine Learning and Artificial Intelligence is applicable to various business functions – this is just the right course for you.

Machine Learning and Deep Learning Specializations

• Natural Language Processing (NLP) using Python:Want to become an NLP Expert? This course is the perfect starting point which covers the core components you need to start your NLP journey
• Natural Language Processing (NLP) using Pytorch  –This course teaches you the latest tools and techniques using the cutting-edge library Pytorch
• Different IMAGE Models.

Preparing for your next Data Science Interview

• Ace Data Science Interviews: Data science interviews can be daunting if you don’t know what to expect. You might feel you have all the knowledge and yet you keep getting rejected. This course will guide you on how to navigate data science interviews, lay down a comprehensive 7-step process, and help you land your dream data science role!
• Structured Thinking and Communication for Data Science Professionals: Whether you are creating dashboards for your business customers or solving cutting-edge machine learning problems, structured thinking and communications is a must have skill for every data professional
• Up-Level your Data Science Resume:Crafting the perfect data science resume is critical to landing your first data science role. Learn the various aspects of designing a resume that will give you the best chance of landing that interview you’ve been looking for

Why you should enroll in this course?

• Upskill yourself for the AI Revolution: Artificial Intelligence has already started making a huge impact in various industries, roles, and functions. The time to upskill yourself and become familiar with artificial intelligence and machine learning is NOW. This comprehensive program will enable you to do just that.
• Easy to understand content: Understanding data science concepts can be difficult. That’s why all the courses in this program have been curated and designed for people from all walks of life. We don’t assume anything – this is AI from scratch.
• Experienced Instructors: All the material in this program was created by instructors who bring immense industry experience. Combined among us, we have multiple decades of teaching experience.
• Industry Relevant: All the courses in this program have been vetted by industry experts. This ensures relevance in the industry and enables you with the content which matters most.
• Real life problems: All projects in the program are modeled on real-world scenarios. We mean it when we say “industry relevant”!
• 9 Projects: We will cover 9 Real industry Projects with updated code.

Frequently Asked Questions:    Common questions about Parentics Courses and Program

• How are these courses and Program delivered?

All the course and programs are self-paced in nature and can be consumed on basis of  your own convivence.

• How soon I can access the course ?

As you enrolled the course from same day you can start the course.

• I need help to choose the right course, what should I do ?

Feel free to reach out to us directly on parentics.solution@gmail.com or +91-8700139274

• What information need from candidate side to enroll the course.

We just need Email id and mobile number to enroll the course . Forward this detail at on parentics.solution@gmail.com or +91-8700139274

• Where we must transfer course fees ?

You can pay us by transfer at A/c :- 106901506200 , IFSC Code:- ICIC0001069

Course Content Summary: –

• Machine Learning [14 Models]
• Clustering [6 Clustering Technique]
• Full Statistic Concept[All Statistical Test]
• Deep Learning[MP, SN,PM,FFN,CNN,RNN,LSTM,RCNN,YOLO,GRU] ,
• Python Language
• All important python Library like Pandas, Numpy, Scipy,Gensim etc.
• NLP,
• Time series [ARIMA, SARIMAX, VAR, etc.]
• Pytorch, Tensor Flow, Keras
• Object detection[LeNet,VGGNET,ALEXNET, INCEPTION, RESNET,ZFNET]
• Parallel Processing,
• SQL
• 9 Projects on industrial data.

CLASS-WISE CONTENT

Class wise Topic mentioned below: –

1 Python: Python Basics, Basic data types, Containers, Lists, Dictionaries, Sets, Tuples, Functions, Classes, If-else, While, do-while, Lambda Function, Map, Reduce, Filter Function, List Comprehension, string methods etc.

2 Pandas: – Basic Functionality, Dataframe, Reindexing, Iteration, Sorting, Indexing and selecting data, Group by, Filter, crosstab, melting, Date Functionality, Categorical Data,

3.Numpy, SciPy, Matplotlib Seaborn: – NumPy, Arrays, Array indexing, Datatypes, Array math, Broadcasting, SciPy, Image operations, MATLAB files, Distance between points, Matplotlib, Plotting, Subplots, Images, Visualization [Seaborn and matplotlib]

4 Stats Introduction:-Descriptive Statistic, Sample vs Population , Mean, Mode, Median , Variance, Standard deviation, Random Variable , Probability Distribution Function , Cumulative Distribution Function , Probability Mass Function , Binomial Distribution , Normal Distribution, Z-score, Central Limit Theorem, P-value, Confident value, Hypothesis testing, Null Hypothesis, Type1 and Type2 error.

5 Stat Test- Parametric and Non-Parametric, T-Test, Z-Test, Chi-square test, Annova, Mann-Whitney, Kruskal-Wallis, Friedmann test.

6 Basics of ML Correlation Categorical and Numerical [Pearson, Spearman], Introduction of Regression and Classification, Supervised-Unsupervised and Reinforcement Learning, Cost Function, Loss Function, Gradient Descent, Over fitting, Underfitting, Lift Function, Learning Curve, Train-Test-Val Split, Outlier detection, Null Value treatment.

7 Evaluation Matrix: – R-Square, Adjusted-Rsquare, RMSE, MAPE, MSE, Confusion Matrix, Accuracy, Precision, Recall, F-Score, Roc/AUC Curve, FPR, TPR.

8 Clustering- Shilloutte, Elbow Method, K mean, K++ Mean, K-Median, DB SCAN, Fussy Clustering, Hierarchical clustering, DI Index.

9 Dimension Reduction-PCA, Factor Analysis, Forward Elimination, Backward Elimination.

10 Linear Regression using OLS and scikit Learn, Introduction to scikit Learn, Assumptions, Polynomial Regression, Lasso, Ridge, Elastic Net, OLS, EDA, Feature Importance

11 Practice Class

12 Logistic Regression:-Bias-Variance trade-off, Assumptions, MLE, Feature Importance, EDA

13 Imbalance dataset:- SMOTE, UP-sampling, Down sampling, Near-miss.

14 Decision Tree Chaid, Entropy, Gini-Index, Tree Visualization.

15 Ensemble Technique, Bagging, Boosting, Random Forest, Stacking.

16 Boosting Model:-Ada-Boost, GBM,  Variable Importance

17 Important Models :-Light-GBM, XGBoost ,Cat-Boosts.

18 Other ML Model:-KNN, SVM, Naive-Bayes.

19. TS – ARIMA , SARIMA, VAR, ACF,PACF.

20NLP: Basic operation using NLTK, NLP Clustering, Topic Modeling, Word Cloud, Coherence Score.

21 Practice Class

22 Vector-Matrix Basic, Linear Algebra, 6-chair expert system, MP Neuron.

23.Perceptron Model, Gradient Descent

24 Sigmoid Model, Probability, Random Function

25 Feedforward Neural Networks, Back Propagation, Vectorized Feed Forward Networks.

26 Optimisation Techniques:-Activation Functions and Initialization Methods, Overfitting and Regularization.

27 Pytorch Introduction and Functionality, Feed Forward Networks using Pytorch.

28 CNN, Image Analytics, CNN using Pytorch, CNN architectures, Stride, Padding, Pooling, Fully connected layer, Lenet and  Deep CNN ,Vggnet, Alex-Net, Inception Model, R-net, Visualizing CNNs, Batch Normalization, Drop-out.

29 Sequence Learning Problems, Recurrent Neural Networks, Vanishing and exploding gradients, LSTMs and GRUs, Sequence Models in Pytorch, Vanishing and Exploding gradients and LSTMs, Sentiment Analysis Model , Part of speech tagging Model.

30 Encoder Decoder Models, Attention Mechanism, Machine Translation Model.

31 Object Detection, R-CNN, YOLO.

32-40 Real Industry 10 Project

41-45  SQL

46 Interview Questions